This page aggregates companion papers by Alejandro Reynoso. All works are human-led: AI assists with literature triage, drafting, editing, and code scaffolding, but direction, validation, and accountability remain human.
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-practical_guide_reasoning_institutions-v01
This paper is a fully researched set of best practices for deploying advanced reasoning models—from chain-of-thought orchestration to agentic pipelines—inside real-world financial institutions. It covers architecture choices, governance and controls, risk & compliance alignment, data integration, evaluation/monitoring, and productionization under regulatory and operational constraints. Emphasis is on institution-grade reliability, auditability, and reproducibility, with patterns and checklists teams can use to move from pilots to scalable impact.
APA
Reynoso, A. (2025). A Practical Guide to Implementing Reasoning Systems in Financial Institutions (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-practical_guide_reasoning_institutions-v01
BibTeX
@article{reynoso_practical_reasoning_institutions_2025_v01,
author = {Alejandro Reynoso},
title = {A Practical Guide to Implementing Reasoning Systems in Financial Institutions},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-practical_guide_reasoning_institutions-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-advanced_sequential_reasoning-v01
Financial decisions rarely unfold as a single, linear calculation. Prices co-move across horizons; credit signals deepen or recede as new information arrives; regimes flip the meaning of familiar indicators; and live data streams force analysts to adapt on the fly. This paper presents a practitioner-first framework organized into four composable patterns:
- Multi-Timeline Analysis — reconcile signals across intraday, swing, and strategic horizons.
- Conditional Reasoning Chains — branch only when prior evidence warrants escalation.
- Scenario-Dependent Architectures — select the right analytical playbook by regime.
- Real-Time Sequential Adaptation — reconfigure the analysis chain as the data-generating process shifts.
Each pattern includes rationale, step-by-step implementation guidance, performance & governance metrics, common failure modes, and change-management tips. Terminal-style ASCII schemes make logic portable to runbooks, PRDs, and code comments. An appendix provides a ready-to-use prompt that regenerates a Colab notebook with clean terminal outputs and robust fallbacks—helping practitioners build systems that are clear to operate, easy to audit, and hard to break.
APA
Reynoso, A. (2025). Advanced Sequential Reasoning (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-advanced_sequential_reasoning-v01
BibTeX
@article{reynoso_advanced_sequential_reasoning_2025_v01,
author = {Alejandro Reynoso},
title = {Advanced Sequential Reasoning},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-advanced_sequential_reasoning-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-basic_molecular_models-v01
We present a rigorous mathematical framework for quantum-inspired optimization of reasoning architectures in artificial intelligence systems. By embedding graph-theoretic reasoning structures into a high-dimensional Hilbert space and employing quantum random-walk dynamics, we obtain provable quadratic speedups over classical optimization methods. The approach establishes a bijective mapping between reasoning architectures and quantum mechanical systems, enabling variational quantum algorithms to discover optimal reasoning pathways. Theoretical analysis links quantum interference to optimization landscape geometry, and experiments on benchmark problems demonstrate significant performance improvements. Applications include neural architecture search, multi-objective optimization, and distributed AI.
Keywords: quantum computing, reasoning architectures, random walks, optimization theory, artificial intelligence.
PDF (latest asset): https://github.com/alexdibol/papers/releases/latest/download/BASIC_MOLECULAR_MODELS.pdf
(For a fixed version, attach to the tagged release and use that asset URL.)
APA
Reynoso, A. (2025). Basic Molecular Models (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-basic_molecular_models-v01
BibTeX
@article{reynoso_basic_molecular_models_2025_v01,
author = {Alejandro Reynoso},
title = {Basic Molecular Models},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-basic_molecular_models-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-biological_system_in_finance-v01
This paper advances a biological-systems blueprint for Reasoning AI in finance, arguing that robust decision-making emerges from the organizing principles of living systems. We propose a modular architecture grounded in homeostasis/allostasis for risk and liquidity control; immune surveillance for anomaly detection and regulatory compliance; neuromodulation for regime-aware exploration, learning rates, and global gain control; metabolism for capital, compute, and data budgeting and allocation; and swarm intelligence for decentralized search and execution. We specify neuroplasticity mechanisms—Hebbian updates, synaptic consolidation, and metaplastic regulation—that adapt policies across shifting regimes while preserving stability margins, provenance, and audit trails. Notebook-guided modules map code cells to figures, diagnostics, and evaluation protocols for reproducibility and institutional auditability. Case studies in portfolio risk budgeting, RegTech surveillance, liquidity “metabolism,” and decentralized execution show improved disturbance rejection, lower false alarms, interpretable regime shifts, and graceful degradation under stress. We outline governance patterns—human-in-the-loop review, autonomy gates, and reversible actions—for deployment under the EU AI Act and analogous regimes. Contributions: (i) a principled translation from systems biology to financial reasoning tasks; (ii) an algorithmic layer unifying control, immune learning, neuromodulation, and swarms; (iii) a reproducible protocol tying every artifact to source code.
APA
Reynoso, A. (2025). Biological Systems in Financial Reasoning (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-biological_system_in_finance-v01
BibTeX
@article{reynoso_biological_systems_financial_reasoning_2025_v01,
author = {Alejandro Reynoso},
title = {Biological Systems in Financial Reasoning},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-biological_system_in_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-emergence_engineering-v01
There is a moment in the life of a growing mind—human or machine—when scattered knowledge becomes a path, a path becomes a road, and the road crosses a chasm that once seemed impassable. Observers of large language models know this moment as emergence: a system that once struggled with multi-step problems begins to reason in stages, plan, and connect. This essay is a field guide to that “magic” without equations. Using analogies rather than math, it explains why abilities appear suddenly—not as quantum leaps, but as bridges opening inside an invisible landscape. It then asks a design question: if bridges can be understood, can we draw them on the map beforehand? Inspired by cosmology, the essay proposes engineering the “initial conditions” of a model’s inner world—the Big Bang of its representation space—so that later structures form with straighter roads, stronger bridges, and fewer detours. The invitation is to build maps first, models second: a practical blueprint for designing emergence rather than merely waiting for it.
APA
Reynoso, A. (2025). Emergence Engineering — Controlling the Big Bang (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-emergence_engineering-v01
BibTeX
@article{reynoso_emergence_engineering_2025_v01,
author = {Alejandro Reynoso},
title = {Emergence Engineering — Controlling the Big Bang},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-emergence_engineering-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-grover_safe_portfolio_search-v01
This paper is an instructional case study for teaching quantum-inspired optimization in finance. It is not investment advice, a trading strategy, or a claim of quantum advantage in live markets. The goal is to document a method that:
(i) expresses cross-venue arbitrage as a genuine search over combinatorial portfolios;
(ii) builds a safety-gated, uncertainty-aware learned oracle that lower-bounds dollar P&L using conformalized quantile regression (CQR), feasibility gating, and calibration; and
(iii) implements a Grover-inspired amplification loop faithful to phase-flip plus diffusion while enforcing fair-budget comparisons—all methods consume the same number of true-oracle (exact P&L) evaluations.
We provide a self-contained mathematical treatment of the portfolio encoding, cost model, oracle design, conformal guarantees, and a simple analysis of imperfect marking in Grover-style amplification. On synthetic markets with realistic microstructure, the pipeline is competitive—and sometimes better—than a calibrated Top-K baseline under identical exact-check budgets, with reduced variance due to safety gating. The contribution is pedagogical and methodological: a blueprint with guardrails and diagnostics that prevent error amplification and can later be lifted toward genuine quantum implementations as hardware and I/O constraints permit.
APA
Reynoso, A. (2025). Grover-Inspired Safe Portfolio Search (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-grover_safe_portfolio_search-v01
BibTeX
@article{reynoso_grover_safe_portfolio_search_2025_v01,
author = {Alejandro Reynoso},
title = {Grover-Inspired Safe Portfolio Search},
year = {2025},
version= {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-grover_safe_portfolio_search-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-human_ai_parnership-v01
This chapter argues that the next frontier in financial decision-making is neither fully automated nor purely human, but a rigorously engineered partnership between analysts, institutions, and reasoning-capable AI systems. We synthesize literature across ML, decision theory, HCI, and governance to propose a layered collaboration architecture: (1) data & knowledge bases; (2) model & retrieval; (3) reasoning & planning; (4) decision, risk & control; (5) oversight & assurance. We present a taxonomy of reasoning capabilities—tool-augmented chains of thought, self-critique & multi-agent debate, graph & causal reasoning, and quantum-inspired methods—and map them to use cases in research, trading, risk, credit, compliance, and audit.
To operationalize the framework, we specify interaction protocols, autonomy gates by risk tier, and evaluation instruments that track decision quality, calibration, robustness, fairness, cost, and latency. Case studies in REIT analytics and sequential compliance demonstrate end-to-end workflows with verifiable provenance and regulator-facing artifacts. We show how value accrues when institutions shift from ad-hoc prompting to disciplined systems engineering: explicit objectives, guarded tool access, controllable memory, versioned policies, and observable execution. We detail governance interfaces—decision logs, model/system cards, standardized attestations—to support internal audit, investor reporting, and supervisory review without sacrificing speed or confidentiality. The chapter closes with adoption guidance, policy guardrails aligned to emerging regulation, and a research agenda on multi-agent orchestration, incentives, and hardware-accelerated reasoning—a practical blueprint for human–AI teams that are auditable by default, adaptive under uncertainty, and aligned to fiduciary and societal constraints.
APA
Reynoso, A. (2025). Human–AI Partnership in Financial Decision-Making (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-human_ai_parnership-v01
BibTeX
@article{reynoso_human_ai_partnership_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Human–AI Partnership in Financial Decision-Making},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-human_ai_parnership-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-emergence_by_design-v01
New abilities in large language models often appear to “switch on” as scale increases. This paper argues those jumps need not be accidental. Within a transformer, concepts inhabit an internal manifold (map) and attention lays down roads that move information between them. Rather than training and hoping a good map emerges, we specify the target map first: straight, high-capacity roads for correct reasoning; robust bridges between key concepts; and guardrails against fabricated facts. We then show how to compile that specification into architecture, initialization, and training objectives so the learned internal map matches the design. In short, we can engineer emergence by shaping the geometry, topology, and spectral properties of the model’s internal world.
APA
Reynoso, A. (2025). Emergence by Design — Implementation of a Manifold Specification Language (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-emergence_by_design-v01
BibTeX
@article{reynoso_emergence_by_design_2025_v01,
author = {Alejandro Reynoso},
title = {Emergence by Design — Implementation of a Manifold Specification Language},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-emergence_by_design-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-mathematical_structures_financial_reasoning-v01
This paper examines the application of rigorous mathematical structures
2DF0
as frameworks for systematic reasoning in financial analysis. We investigate how category theory, lattice theory, information theory, and algebraic topology can provide formal constraints and optimization criteria for reasoning-path design. Through critical analysis of theoretical foundations and practical implementations, we demonstrate both the promising applications and the limitations of mathematically structured approaches to financial decision-making. The framework is illustrated via a comprehensive implementation that guides large language models through disciplined reasoning processes, including a detailed REIT liquidity management case study under stress conditions.
Keywords: mathematical reasoning, financial analysis, category theory, lattice theory, information theory, structured decision-making, artificial intelligence.
APA
Reynoso, A. (2025). Mathematical Structures for Financial Reasoning (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-mathematical_structures_financial_reasoning-v01
BibTeX
@article{reynoso_mathematical_structures_financial_reasoning_2025_v01,
author = {Alejandro Reynoso},
title = {Mathematical Structures for Financial Reasoning},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-mathematical_structures_financial_reasoning-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-abstract_math_financial_decision-v01
This paper examines the use of abstract yet powerful mathematical structures as frameworks for systematic reasoning in financial analysis. We explore how concepts from category theory, lattice theory, information theory, and algebraic topology provide formal blueprints for designing robust, auditable, and optimized analytical processes—imposing constraints and optimization criteria on reasoning-path design. Through critical analysis of theoretical foundations and practical implementations, we demonstrate both the promising applications and significant limitations of mathematically structured approaches to financial decision-making. The framework is instantiated via a comprehensive implementation that guides LLMs through disciplined reasoning workflows, culminating in a detailed REIT liquidity management case study under severe stress. The aim is to bridge abstract mathematics and real-world finance, yielding more reliable and defensible decisions.
Keywords: mathematical reasoning, financial analysis, category theory, lattice theory, information theory, structured decision-making, artificial intelligence, LLM reasoning.
APA
Reynoso, A. (2025). Abstract Mathematical Structures for Financial Decision-Making (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-abstract_math_financial_decision-v01
BibTeX
@article{reynoso_abstract_math_financial_decision_2025_v01,
author = {Alejandro Reynoso},
title = {Abstract Mathematical Structures for Financial Decision-Making},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-abstract_math_financial_decision-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://githu
DDD0
b.com/alexdibol/papers/releases/tag/papers-contrastive-molecular-reasoning-v01
This paper introduces a framework for understanding and retrieving reasoning patterns in financial decision-making via contrastive learning and embedding spaces. We define “reasoning molecules”—structured units of logical thought—represented and analyzed in high-dimensional spaces. A dual-encoder architecture ingests structural features and semantic content to form a unified 64-dimensional embedding space where reasoning patterns cluster by logical properties. Using synthetic query generation and contrastive objectives, we show how professionals can analyze, retrieve, and interpret complex reasoning. As a pedagogical proxy for financial analysis, we validate with Sherlock Holmes-style detective reasoning and demonstrate separability across deductive, observational, eliminative, and verificational modes. The result enables sophisticated retrieval of reasoning patterns by semantic similarity with implications for financial education, risk assessment, and next-generation decision-support systems.
Keywords: contrastive learning, reasoning molecules, dual-encoder, embeddings, financial decision-making, retrieval, Sherlock Holmes
APA
Reynoso, A. (2025). Molecular Reasoning — Contrastive Methods (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-contrastive-molecular-reasoning-v01
BibTeX
@article{reynoso_molecular_reasoning_contrastive_methods_2025_v01,
author = {Alejandro Reynoso},
title = {Molecular Reasoning — Contrastive Methods},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-contrastive-molecular-reasoning-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-multi_abstract_math_agents-v01
This research presents a multi-agent computational framework that uses specialized mathematical reasoning structures for collaborative financial analysis. We introduce four distinct intelligent agents—rooted in lattice theory, category theory, information theory, and algebraic topology—to tackle complex investment decisions systematically. Through comprehensive case studies, including the $2.8B TechTarget Corp acquisition analysis, we show how mathematical specialization delivers broader analytical coverage, stronger error detection, and improved decision quality, while also revealing coordination challenges and implementation considerations. Across 60 financial scenarios, the framework achieves 96% of human expert team performance and an 18% improvement over single-agent approaches. We conclude with actionable deployment guidelines for institutional finance environments.
Keywords: multi-agent systems, lattice theory, category theory, information theory, algebraic topology, financial analysis, decision support
APA
Reynoso, A. (2025). Multi-Agent Topology — Mathematical Structures for Collaborative Financial Analysis (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-multi_abstract_math_agents-v01
BibTeX
@article{reynoso_multi_agent_topology_2025_v01,
author = {Alejandro Reynoso},
title = {Multi-Agent Topology — Mathematical Structures for Collaborative Financial Analysis},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-multi_abstract_math_agents-v01}
}
13) Quantum Attention & Cognitive Pattern Discovery — A Unified Quantum-Inspired Reasoning Framework
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-unified_quantum_reasoning-v01
Strategic reasoning is often treated as an ineffable art, yet real-world outcomes suggest it is learnable and optimizable. This paper presents a unified framework that models reasoning molecules—structured patterns of thought—as objects in a high-dimensional Hilbert space, and applies a quantum-inspired optimization pipeline (Quantum Random Walk, Grover-style amplitude amplification, Variational Quantum Eigensolver) to discover and refine effective cognitive strategies. Concretely, we: (i) extract reasoning molecules from the Sherlock Holmes canon; (ii) learn joint structural + semantic embeddings via a dual-encoder with multi-head attention; and (iii) lift these embeddings into a quantum state representation supporting interference-driven exploration and variational refinement. We provide complexity bounds and convergence guarantees using a formulation of reasoning states in complex projective space with Fubini–Study geometry, and analyze pipeline error sources. Empirically, over fifteen molecules from five stories, we achieve R² > 0.7 for quality prediction and > 80% task success, while an 8-qubit encoding enables a 256-dimensional search space with quadratic speedups relative to classical baselines. We argue this realizes a Quantum Attention mechanism over the space of human cognition, enabling domain-tailored reasoning subspaces (investigation, diagnosis, strategic decision-making). Technical definitions, operators, and theorems are consolidated in the appendices to preserve narrative clarity while maintaining rigor.
Keywords: quantum-inspired optimization, reasoning molecules, dual-encoder embeddings, quantum random walk, Grover amplification, VQE, Fubini–Study geometry, cognitive strategy
APA
Reynoso, A. (2025). Quantum Attention & Cognitive Pattern Discovery — A Unified Quantum-Inspired Reasoning Framework (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-unified_quantum_reasoning-v01
BibTeX
@article{reynoso_quantum_attention_cognitive_patterns_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum Attention & Cognitive Pattern Discovery — A Unified Quantum-Inspired Reasoning Framework},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-unified_quantum_reasoning-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_enhanced_reasoning_systems-v01
We introduce an architecture that leverages quantum computing principles to amplify human reasoning. The system lifts classical contrastive learning of reasoning patterns into a quantum-optimized pipeline capable of exploring combinatorially large solution spaces via superposition, interference, and quantum optimization. It comprises four integrated phases:
- Type-based contrastive embeddings (classical) to encode reasoning pattern types;
- Quantum Random Walk (QRW) exploration over candidate reasoning pathways;
- Grover-style amplitude amplification of high-quality discoveries;
- Variational Quantum Eigensolver (VQE) refinement of final solutions.
Experiments indicate order-of-magnitude speedups (≈50×–10,000×) over classical baselines while improving solution quality by ~23% (89% confidence). The work contributes both a theoretical foundation and a practical implementation framework for quantum-enhanced AI in cognitive reasoning tasks.
Keywords: Quantum Computing, Reasoning Systems, Contrastive Learning, Quantum Random Walks, Grover’s Algorithm, Variational Quantum Eigensolvers, Cognitive Amplification.
APA
Reynoso, A. (2025). Quantum-Enhanced Cognitive Amplification (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_enhanced_reasoning_systems-v01
BibTeX
@article{reynoso_quantum_enhanced_cognitive_amplification_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum-Enhanced Cognitive Amplification},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_enhanced_reasoning_systems-v01}
}
15) Quantum Hidden Markov Models for Financial Regime Detection
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-quantum_hmm-v01
This paper introduces a quantum-enhanced Hidden Markov Model (QHMM) for market regime detection and algorithmic trading. Unlike traditional approaches that rely solely on technical signals or classical HMMs (e.g., Simmons), the proposed QHMM fuses observable market features and latent regimes within a parameterized quantum circuit leveraging entanglement and interference. Real-valued indicators and binary stock signals are mapped to predefined regimes (Bull, Normal, Volatile, Bear), enabling the model to capture nonlinear correlations and superpositional market states that classical models struggle to represent. We document substantial interference effects that differentiate QHMM information processing from classical HMMs.
In backtests on SPY, the QHMM often converges to buy-and-hold during periods when classical HMMs and technical strategies underperformed due to over-trading—effectively asserting regime stability and avoiding unnecessary turnover, with superior risk-adjusted returns. We provide the mathematical formulation of the QHMM architecture, training objective, and measurement process, and discuss how quantum mechanical principles can offer unique advantages in financial modeling.
Keywords: quantum HMM, regime detection, quantum circuits, entanglement, interference, algorithmic trading, SPY backtest
APA
Reynoso, A. (2025). Quantum Hidden Markov Models for Financial Regime Detection (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-quantum_hmm-v01
BibTeX
@article{reynoso_quantum_hmm_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum Hidden Markov Models for Financial Regime Detection},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-quantum_hmm-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-quantum_agentic_optimization_continuous_combinatorics_hamiltonian_mappings-v01
This paper proposes a portfolio-optimization framework that unifies quantum-inspired combinatorics, agentic modularity, and financial strategy encoding via Hamiltonian mappings. We introduce continuous combinatorics—a single optimization-selection process inspired by QAOA—implemented inside a fully modular 8-agent architecture. Investment strategies are mapped to Hamiltonians; the space of permissible Hamiltonians is studied as a latent manifold of investment logic; and we outline foundations for reverse mappings from observed financial outcomes back to abstract Hamiltonians. Using sentiment-driven inputs and classical simulation of quantum behavior, the framework shows how quantum principles can enhance modeling while remaining interpretable, scalable, and extensible toward topological generalizations.
Keywords: quantum-inspired optimization, continuous combinatorics, Hamiltonians, QAOA, multi-agent systems, portfolio optimization, sentiment signals
APA
Reynoso, A. (2025). Quantum-Inspired Continuous Combinatorics — Agentic Optimization via Hamiltonian Mappings (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-quantum_agentic_optimization_continuous_combinatorics_hamiltonian_mappings-v01
BibTeX
@article{reynoso_quantum_inspired_continuous_combinatorics_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum-Inspired Continuous Combinatorics — Agentic Optimization via Hamiltonian Mappings},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-quantum_agentic_optimization_continuous_combinatorics_hamiltonian_mappings-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-neural_quantum_hybrid_decision_models-v01
We present a systematic framework for optimizing strategic reasoning using quantum-enhanced computational methods. Treating reasoning patterns as molecular structures in high-dimensional spaces enables systematic discovery of optimal cognitive architectures for business decision-making. A three-stage quantum pipeline delivers a 15.6× speedup over classical methods while maintaining 91% solution quality. Applications show measurable gains in merger analysis (+23% accuracy), crisis management (60% faster response), and innovation strategy development. The result is a practical tool for competitive advantage: quantum reasoning optimization that augments institutional decision-making under uncertainty.
Keywords: quantum computing, strategic decision-making, reasoning optimization, artificial intelligence, business strategy, competitive advantage.
APA
Reynoso, A. (2025). Quantum–Non-Quantum Hybrid Decision Making (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-neural_quantum_hybrid_decision_models-v01
BibTeX
@article{reynoso_quantum_non_quantum_hybrid_decision_making_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum–Non-Quantum Hybrid Decision Making},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-neural_quantum_hybrid_decision_models-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_trading_investing-v01
This paper develops a quantum-inspired framework for quantitative finance that operationalizes the primitives of superposition, entanglement, and interference within tractable, fully classical algorithms. We formalize strategy Hamiltonians for momentum, contrarian, and hybrid allocation; encode portfolio constraints and turnover costs as penalty terms; and simulate QAOA-style updates to explore combinatorial weight landscapes. Interference encoders translate phase structure in returns into regime-aware signals, while entanglement analogues capture cross-asset dependence beyond covariance via network-regularized couplings. Implementations are released in a companion Colab notebook for end-to-end reproducibility.
Across equity and multi-asset datasets, we benchmark against mean–variance, risk parity, and sparse L2/L1 allocators, evaluating Sharpe, Sortino, drawdown, turnover, stability, and tail control. Ablations and stress tests probe sensitivity to horizons, coupling strength, and transaction frictions. Results show consistent improvements in risk-adjusted performance and drawdown resilience during regime shifts—attributable to interference-derived timing and entanglement-aware hedging. We discuss computational trade-offs, interpretability via energy landscapes, and governance implications for production. Grounding quantum metaphors in explicit operators and reproducible code, the work offers a rigorous bridge between quantum ideas and institutional portfolio design, and a roadmap for hybrid quantum–classical extensions as hardware matures. Limitations and future research directions are outlined.
Keywords: quantum-inspired finance, Hamiltonians, QAOA-style optimization, interference encoders, entanglement analogues, portfolio construction, risk management
PDF (latest): https://github.com/alexdibol/papers/releases/latest/download/QUANTUM_INSPIRED_TRADING_AND_INVESTING.pdf
(If you prefer a fixed version, replace with the asset URL from the tagged release above.)
APA
Reynoso, A. (2025). Quantum-Inspired Trading and Investing (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_trading_investing-v01
BibTeX
@article{reynoso_quantum_inspired_trading_investing_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum-Inspired Trading and Investing},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_trading_investing-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-structural_reasoning_hft-v01
High-frequency trading demands microsecond decisions under adversarial, latency-constrained conditions. We present Microsecond Structural Reasoning (MSR), an integrated architecture combining:
(i) a sequential order-flow chain for ultra-fast microstructure inference;
(ii) a molecular cross-asset bond engine that measures co-movement stability to expose transient arbitrage;
(iii) a topological liquidity navigator that plans routes across fragmented venues; and
(iv) a regime-aware meta-reasoner that allocates compute and risk via calibrated confidence.
An event-driven evaluation harness applies regime-tagged windows and stress tests (volatility shocks, liquidity droughts, connectivity fragmentation), with single-module ablations at matched latency. Metrics cover execution cost/slippage, fill quality, and stability of bond and liquidity graphs. Empirically, MSR improves execution quality over any single module, degrades gracefully during shocks, and accelerates post-shock recovery. Contributions include: a composable, latency-aware framework; a confidence-calibrated meta-reasoner for online scheduling/sizing; a liquidity-graph formulation with stability metrics; and a mirrored Colab notebook enabling replication of figures, tables, and sensitivity analyses.
Keywords: high-frequency trading, market microstructure, late 2DF0 ncy-aware reasoning, liquidity graphs, cross-asset bonds, meta-reasoner, stress testing
APA
Reynoso, A. (2025). Microsecond Structural Reasoning for High-Frequency Trading (MSR) (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-structural_reasoning_hft-v01
BibTeX
@article{reynoso_msr_hft_2025_v01,
author = {Alejandro Reynoso},
title = {Microsecond Structural Reasoning for High-Frequency Trading (MSR)},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-structural_reasoning_hft-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-structural_reasoning_asset_allocation-v01
Institutional investment increasingly demands systems that reason over heterogeneous, latency-prone data while respecting tight operational and governance constraints. This paper proposes a modular reasoning framework with three separable responsibilities:
- Beliefs: form probabilistic beliefs from validated features using five complementary families—sequential & point-process models, probabilistic graphical models, optimization & combinatorial allocators, graph/topology-aware methods, and agentic LLM components—connected via a disciplined integration layer.
- Constraints: encode costs, risk, capacity, and compliance as first-class constraints with transparent shadow prices.
- Decisions: map beliefs and constraints to trades under auditable objectives.
The integration layer aggregates heterogeneous forecasts under strictly proper scoring rules, regularizes against model collinearity, and inflates risk under inter-model disagreement so uncertainty becomes a governable input to portfolio construction. The engineering substrate enforces point-in-time discipline, feature versioning, and leakage prevention; each run emits a signed manifest (data snapshots, feature versions, configuration hashes, code commits, seeds) enabling bit-for-bit replay and independent validation. An experimental protocol specifies rolling, capacity- and cost-aware evaluation with nested tuning, stress tests, and regime-sliced reporting—prioritizing calibration, cost realism, and operational robustness over unconditional performance claims.
We provide a results template reporting headline metrics with uncertainty intervals, marginal value via ablations and Shapley-style analyses, and audits of predictive calibration and execution costs. Case studies show how disagreement-aware aggregation reduces drawdowns at regime transitions, topology-based regularization curbs cross-sectional overfitting, and constraint shadow prices clarify mandate trade-offs. Agentic components serve as scribe, narrator, and governance clerk, producing human-auditable narratives grounded in the run ledger. Treating uncertainty, constraints, and provenance as co-equal to prediction turns a collection of models into an auditable decision system that is easier to operate, validate, and extend.
Keywords: asset allocation, reasoning systems, proper scoring rules, topology-aware regularization, agentic LLMs, auditability, cost-aware evaluation
APA
Reynoso, A. (2025). Structural Reasoning for Institutional Asset Allocation (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-structural_reasoning_asset_allocation-v01
BibTeX
@article{reynoso_structural_reasoning_asset_allocation_2025_v01,
author = {Alejandro Reynoso},
title = {Structural Reasoning for Institutional Asset Allocation},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-structural_reasoning_asset_allocation-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-reg_tech_risk_management-v01
Financial institutions face expanding regulatory obligations and technology risk while modernizing with data-intensive, adaptive systems. The central challenge is bridging natural-language policies and software that acts under uncertainty and change. This paper presents a reasoning-centric framework for regulation and technology-risk management that unifies policy representation, control evaluation, and sequential remediation. We compose symbolic rule checking, probabilistic scoring, graph-based consistency, and sequential decision processes into an auditable control plane. A policy graph links obligations to executable tests, monitors, and evidence artifacts—enabling policy-to-code traceability and explanations by construction.
A Colab-based reference implementation operationalizes data ingestion, constraint compilation, model monitoring, drift/config checks, and automated reporting for the three lines of defense. Evaluation protocols quantify coverage, precision/recall, time-to-detect, time-to-remediate, robustness to drift, and evidence completeness. Across realistic scenarios—policy updates, data-pipeline failures, model drift, and control degradation—the framework improves detection quality and reduces remediation latency relative to siloed rules and point solutions, while lowering the marginal cost of audit. Contributions include: a clear problem statement; a modular architecture; a policy-graph schema; a compliance-focused evaluation suite; and case studies. The approach is practical, incrementally adoptable, and generalizable to other high-stakes socio-technical systems requiring accountable automation.
Keywords: RegTech, technology risk, policy graph, reasoning systems, compliance automation, monitoring & drift, auditability
APA
Reynoso, A. (2025). Reasoning Models for Regulation Technology & Risk Management (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-reg_tech_risk_management-v01
BibTeX
@article{reynoso_regtech_reasoning_models_2025_v01,
author = {Alejandro Reynoso},
title = {Reasoning Models for Regulation Technology & Risk Management},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-reg_tech_risk_management-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-reasoning_financial_risk-v01
This paper presents a structural reasoning framework for financial risk management that unifies four complementary paradigms:
(i) sequential, agentic scenario engines;
(ii) molecular contagion graphs capturing cross-exposures;
(iii) topological risk landscapes with path-risk integrals and geodesic navigation; and
(iv) adaptive meta-learning that selects/refines architectures as regimes shift.
We formalize states, events, and reaction dynamics; define curvature-aware path risk on a manifold of portfolio configurations; and specify a selector policy for cross-architecture transfer. A reproducible implementation evaluates portfolios against historical VaR/ES baselines, stress scenarios, and ablations—reporting gains in transparency, robustness, and navigability. The framework supports institutional governance via audit trails, human-in-the-loop controls, and privacy-preserving federation. Results indicate improved detection of emerging vulnerabilities, clearer attribution of risk propagation, and more deliberate intervention planning. We discuss deployment patterns, limitations, and validation protocols—positioning structural reasoning as a practical bridge between quantitative risk controls and modern AI decision systems.
Keywords: structural reasoning, financial risk, contagion graphs, topological risk landscapes, meta-learning, VaR/ES, governance
APA
Reynoso, A. (2025). Structural Reasoning Models in Financial Risk Management (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-reasoning_financial_risk-v01
BibTeX
@article{reynoso_structural_reasoning_financial_risk_2025_v01,
author = {Alejandro Reynoso},
title = {Structural Reasoning Models in Financial Risk Management},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-reasoning_financial_risk-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-regime_aware_quantum_enconders_algo_trading-v01
This paper introduces Quant-Quant, an algorithmic trading approach that leverages quantum circuit architecture discovery for financial strategy selection. Rather than swapping classical neural networks for quantum analogs, Quant-Quant exploits interference and superposition to evolve circuit topologies whose interference patterns induce emergent trading strategies. By treating topology selection as the key design degree of freedom, quantum computers act as strategy-optimization engines, exploring combinations in parallel and allowing constructive interference to surface high-quality behaviors. A regime-aware encoder steers exploration across market conditions. Empirically, the system reports 212.98% annual returns, 100.01% alpha, and a Sharpe ratio of 1.302, supporting the case for quantum principles in financial contexts.
Keywords: quantum encoders, interference, superposition, circuit topology, algorithmic trading, regime-aware optimization
APA
Reynoso, A. (2025). Regime-Aware Quantum Encoders for Algorithmic Trading (Quant-Quant) (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-regime_aware_quantum_enconders_algo_trading-v01
BibTeX
@article{reynoso_regime_aware_quantum_encoders_2025_v01,
author = {Alejandro Reynoso},
title = {Regime-Aware Quantum Encoders for Algorithmic Trading (Quant-Quant)},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-regime_aware_quantum_enconders_algo_trading-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_attention-v01
This paper presents a quantum-inspired approach to NLP that simulates entanglement and interference across neighboring sentences to resolve semantic ambiguity, with emphasis on financial communications. We encode sentence triplets into mathematically entangled representations, inducing interference-like patterns that disambiguate contradictory or nuanced signals. Implemented entirely on classical hardware, the method achieves a 36.7% accuracy improvement over classical baselines (p < 0.004) on adversarial ambiguity datasets. We develop and validate two complementary architectures:
- a non-linear interference model reaching 63.3% accuracy on ambiguous text classification; and
- a complete quantum-inspired NLP framework that demonstrates robust performance across diverse scenarios.
The results indicate advantages of quantum-inspired computation for language tasks requiring sophisticated ambiguity resolution (e.g., earnings reports, Fed minutes, financial news, regulatory filings) and provide foundations for quantum-enhanced transformer designs when appropriate hardware becomes available.
Keywords: quantum-inspired attention, semantic ambiguity, entanglement, interference, financial NLP, adversarial text, transformer foundations
APA
Reynoso, A. (2025). Semantic Ambiguity Resolution via Quantum-Inspired Attention (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_attention-v01
BibTeX
@article{reynoso_quantum_inspired_attention_ambiguity_2025_v01,
author = {Alejandro Reynoso},
title = {Semantic Ambiguity Resolution via Quantum-Inspired Attention},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_attention-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-math_emergence_ai_intelligence-v01
Large language models sometimes appear to “suddenly” acquire new abilities—solving multi-step problems, planning, or explaining reasoning. This paper proposes a map-and-roads account. Inside a model, concepts lie on an internal geometric map, and attention builds roads that move information between them. Small models have messy maps with thin or broken roads across distant ideas; as models grow and data increases, the map clarifies and the roads widen. At a critical point, new bridges span old gaps, making long, multi-step routes traversable—observed as a jump in capability.
We show how to measure these bridges using spectral gaps (networks), topology (shape/connectivity), and geodesics (geometry). We contrast this view with smooth scaling laws and “circuit” explanations, then outline design principles: specify the ideal internal map first (fast, accurate, less prone to fabrication), then build the network so its learned roads match the target map.
Keywords: emergence, spectral gaps, topology, geodesics, attention mechanisms, reasoning maps, model design
APA
Reynoso, A. (2025). The Mathematics of Emergence (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-math_emergence_ai_intelligence-v01
BibTeX
@article{reynoso_mathematics_of_emergence_2025_v01,
author = {Alejandro Reynoso},
title = {The Mathematics of Emergence},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-math_emergence_ai_intelligence-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-reasoning_corporate_finance-v01
This paper develops a unified, production-oriented framework for emergent and autonomous AI in corporate finance, translating advances in neural architecture search (differentiable, evolutionary, progressive NAS), continual and meta-learning, self-organizing networks, and AGI-inspired reasoning into verifiable decision systems. Anchored by a comprehensive implementation notebook (Chapter 6), we formalize financial decision problems under non-stationarity and regulatory constraints, and design autonomy gates that bind model behavior to fiduciary, risk, and compliance policies.
Methodologically, we (i) cast architecture discovery as resource-aware bilevel optimization; (ii) couple drift-robust continual learning with explicit forgetting penalties and replay controls; (iii) induce adaptive topologies (e.g., Growing Neural Gas) to surface regime structure; and (iv) integrate neural-symbolic tool use and causal world models for long-horizon planning. We map these components into a deployable system architecture with data lineage, monitoring, explainability, and audit trails.
Empirically, case studies demonstrate value across credit risk automation, regulatory adaptation, self-organizing risk management, AGI-style strategic planning, and autonomous incident response, with sensitivity analyses quantifying stability, latency, and governance trade-offs. The result is a principled pathway from prototype to institution-grade autonomy: models that learn continuously, explain their actions, and respect institutional constraints.
Keywords: corporate finance, emergence, autonomy, NAS, continual learning, meta-learning, Growing Neural Gas, neural-symbolic, causal models, governance
APA
Reynoso, A. (2025). Emerging Properties of AI Models in Corporate Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-reasoning_corporate_finance-v01
BibTeX
@article{reynoso_emerging_ai_corporate_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Emerging Properties of AI Models in Corporate Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-reasoning_corporate_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-gen_ai_corporate_finance-v01
This paper formalizes an integrated research program for applying generative AI and LLMs to strategic finance and corporate decision-making. We synthesize three complementary reasoning paradigms—chain-of-thought valuation analysis, tree-of-thought strategic option evaluation, and tool-augmented financial analytics with real-time data—into a unified, auditable framework for investment banking and corporate finance. The approach operationalizes transparent assumption tracing, multi-branch scenario optimization, and cross-source validation (market data, fundamentals, macro indicators, sentiment) to meet professional standards for fairness opinions, capital allocation, and board-level strategy under uncertainty.
Methodologically, we specify reproducible prompts, model-governance controls, and data-quality checks, and we map outputs to decision artifacts required by fiduciary, regulatory, and internal risk policies. We provide a reference architecture and evaluation protocol that quantify reasoning rigor, attribution, and sensitivity to model and data drift. Our agenda outlines empirical tests comparing LLM-assisted workflows to conventional practice across valuation accuracy, time-to-insight, and documentation completeness. The result is a path from prototype notebooks to production-grade, institutionally deployable systems, linking analytical transparency with measurable value creation and risk mitigation.
Keywords: generative AI, LLMs, corporate finance, valuation, strategy, governance, auditability, real-time data
APA
Reynoso, A. (2025). Generative AI in Corporate Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-gen_ai_corporate_finance-v01
BibTeX
@article{reynoso_generative_ai_corporate_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Generative AI in Corporate Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-gen_ai_corporate_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-bio_intelligence_finance-v01
This paper presents a contract-first framework that brings biological intelligence—including evolutionary computation, swarm search, cellular automata, and neural genetic programming—into core corporate-finance decisions. Mandates are formalized as a fitness–constraint contract encoding risk-penalized free cash flow, liquidity buffers and covenant adherence, service levels, tax efficiency, and implementation frictions. Hard constraints are enforced via constraint-preserving operators; soft preferences set selection pressure—yielding policies that are feasible, auditable, and robust to regime shifts.
Methodologically, we: (i) evolve adaptive allocators with neuroevolution (learning structure + parameters); (ii) solve high-dimensional discrete planning with swarm intelligence to minimize time-to-feasible under many constraints; (iii) use cellular automata to model ecosystem effects (shock propagation, tipping points, containment levers) in working capital, liquidity, and pricing; and (iv) discover interpretable, board-ready strategies with neural genetic programming under explicit complexity control. Engines are integrated in a governance-ready stack that emits reproducibility manifests, decision logs, model cards, and autonomy gates.
Empirically, we propose regime-aware rolling evaluations with strong baselines, cost realism, and stress scenarios, reporting outcomes across utility, constraint adherence, policy turnover, sensitivity, and compute footprint. For researchers: a provably feasible approach to adaptive policy design on rugged decision spaces. For practitioners: a living decision system that advances allocation, liquidity, pricing, tax strategy, and integration—while preserving explainability and control.
Keywords: biological intelligence, evolutionary computation, swarm intelligence, cellular automata, neural genetic programming, corporate finance, governance
APA
Reynoso, A. (2025). Introduction to Biological Intelligence Methods in Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-bio_intelligence_finance-v01
BibTeX
@article{reynoso_bio_intelligence_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Introduction to Biological Intelligence Methods in Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-bio_intelligence_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-evolutionary_neural_corporate_finance-v01
This paper develops a governance-native, AI-first framework for corporate-finance decision-making. Rather than treating prediction as the end, the system couples representation learning with fiduciary constraints to deliver calibrated, auditable recommendations for funding, liquidity, capital structure, working capital, and strategy. Methodologically, we specify a relation-aware transformer for irregular, multi-modal financial data; deep ensembles with post-hoc calibration and conformal coverage for decision-grade uncertainty; generative engines (variational & diffusion) for scenario and stress analysis; and neural causal discovery with acyclicity and economic priors for counterfactual reasoning.
We formalize event-time data contracts, leakage guards, and rolling-origin evaluation, elevating calibration, coverage, robustness under shift, and policy regret to first-class metrics. The implementation blueprint integrates MLOps with model risk management: manifests, model/decision cards, champion–challenger deployment, drift monitors, bounded autonomy with abstention, and reproducible evidence packs for audit. A companion Colab renders learning curves, reliability diagrams, coverage tables, scenario galleries, attribution overlays, and causal scaffolds. The architecture shows how accuracy and accountability can be co-produced, enabling CFOs, treasurers, and boards to realize value from AI while meeting regulatory, ethical, and operational requirements.
Keywords: corporate finance, relation-aware transformer, deep ensembles, conformal prediction, diffusion/variational generative models, neural causal discovery, model risk management, governance
APA
Reynoso, A. (2025). Introduction to Neural Methods in Corporate Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-evolutionary_neural_corporate_finance-v01
BibTeX
@article{reynoso_neural_methods_corporate_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Introduction to Neural Methods in Corporate Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-evolutionary_neural_corporate_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-multi_agent_reinforcement_learning_finance-v01
We frame corporate finance as a multi-agent, partially observable control problem and build decision systems that integrate multi-agent RL (MARL), hierarchical RL (HRL), neural attention for communication, and option-based temporal abstraction. The architecture operationalizes cooperation, competition, and governance across canonical domains: investment-committee asset allocation, cross-unit credit risk, cross-border transfer pricing, IPO underwriting & bond bookbuilding, M&A valuation auctions, hierarchical capital allocation, multi-level budgeting, and corporate restructuring.
Agents use message encoders + attention for interpretable information exchange; competitive self-play with lightweight opponent models to uncover equilibrium behaviors; and an option-critic hierarchy to align strategic–tactical–operational horizons. Rewards internalize performance, risk, compliance, and coordination, with guardrails against groupthink and concentration.
Empirically, attention improves consensus quality and risk-adjusted performance while preserving diversity; in competitive issuance/auctions, self-play yields pricing & participation policies balancing revenue, discipline, and prudent dropout. In hierarchical settings, options stabilize cross-level credit assignment and align divisional execution with corporate ROE targets. We report metrics for attention diversity, consensus dispersion, competitive spread, compliance risk, and hierarchical alignment, plus stress tests for regime shifts and adversarial behaviors. The result is a scientifically grounded, implementable, auditable blueprint for AI-enabled corporate decision systems—useful to practitioners, regulators, and researchers at the intersection of MARL, organizational design, and market microstructure.
Keywords: multi-agent reinforcement learning, hierarchical RL, attention, option-critic, governance, corporate finance, auctions, capital allocation
APA
Reynoso, A. (2025). Multi-Agent Reinforcement Learning for Corporate Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-multi_agent_reinforcement_learning_finance-v01
BibTeX
@article{reynoso_marl_corporate_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Multi-Agent Reinforcement Learning for Corporate Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-multi_agent_reinforcement_learning_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_corporate_finance-v01
This paper develops a scientifically rigorous framework for quantum-inspired decision systems in corporate finance. We formalize capital allocation, risk management, and governance as structured optimization programs—principally Hamiltonian formulations—and propose hybrid pipelines that combine variational metaheuristics with classical estimation and constraint encoding. The theoretical core specifies agents, objectives, and information sets under uncertainty, linking welfare-relevant criteria to implementable loss functions and verifiable decision rules. Methodologically, we characterize computational complexity, identification assumptions, and convergence heuristics, and provide an empirically disciplined protocol: time-respecting splits, benchmarked metrics, ablations, and uncertainty quantification. Using institutional datasets, we evaluate economic significance alongside statistical performance, isolate mechanism-level contributions, and test robustness to hyperparameters, temporal drift, and adversarial stressors. We embed model-risk controls (documentation, monitoring, human-in-the-loop oversight) within an enterprise GRC workflow aligned to auditability and disclosure. Managerial implications include capital budgeting, liquidity policy, and risk transfer; policy implications address transparency, market structure, and supervisory expectations for algorithmic governance. Contributions: (i) a unifying formalism connecting quantum-inspired optimization to finance theory; (ii) a reproducible implementation stack with model/system cards and audit trails; and (iii) evidence that hybrid approaches can deliver decision-quality gains while remaining interpretable, controllable, and compliant in production.
Keywords: quantum-inspired optimization, Hamiltonians, corporate finance, variational metaheuristics, GRC, model risk management, uncertainty quantification
APA
Reynoso, A. (2025). Quantum-Inspired Corporate Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_corporate_finance-v01
BibTeX
@article{reynoso_quantum_inspired_corporate_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum-Inspired Corporate Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_inspired_corporate_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-white_paper_implementation_ai_finance-v01
This white paper provides a practical, governance-first blueprint for deploying production-grade AI across investment banking and corporate finance. We integrate an enterprise reference architecture—governed data foundations, versioned feature stores, reproducible model fabrics, resilient serving/orchestration, and end-to-end observability/security—with a rigorous Model Risk Management (MRM) lifecycle for regulated environments. The approach is outcome-oriented: every model is tied to verifiable cash or risk metrics (e.g., DSO reduction, cost-of-funds savings, capital efficiency) and monitored for calibration, bias, drift, and resilience.
Controls are mapped to prudential, financial reporting, and data-protection regimes (Basel-aligned MRM, SOX, GDPR/analogous DP laws), with artifacts—model cards, system cards, data sheets, deployment runbooks—that render decisions auditable and reproducible. For LLMs, we specify retrieval governance, prompt hygiene, tool-use policies, safety evaluations, and unit-economics guardrails. Portfolio steering is formalized via stage gates, hurdle rates, and a risk-adjusted AI metric (RAAI) to guide capital allocation. We conclude with a 90-day foundation plan and a 12-month scale roadmap, defining decision rights and operating rhythms for Boards, CFOs, and risk/audit leaders. The result is an economically disciplined, assurance-grade pathway from pilots to durable enterprise impact.
Keywords: governance-first AI, MRM, feature stores, observability, SOX, GDPR, retrieval governance, LLM safety, RAAI, enterprise roadmap
APA
Reynoso, A. (2025). White Paper — Implementing AI for Investment Banking & Corporate Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-white_paper_implementation_ai_finance-v01
BibTeX
@article{reynoso_white_paper_ai_corporate_finance_2025_v01,
author = {Alejandro Reynoso},
title = {White Paper — Implementing AI for Investment Banking & Corporate Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-white_paper_implementation_ai_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_classification_portfolio_diversification-v01
This paper offers an accessible, question-driven introduction to quantum-inspired clustering for financial practitioners. Through a maieutic sequence of ten Q&As, readers progress from “What is a qubit in Hilbert space?” to practical concerns like feature encoding, algorithmic mechanics, limitations, and future directions—without requiring prior quantum mechanics background. We explain how Hilbert-space representations, superposition, and entanglement provide novel mathematical frameworks for high-dimensional financial datasets. Using company financial metrics as concrete examples, we show how quantum-inspired clustering can encode nonlinear relations and complex correlations that traditional Euclidean clustering may overlook.
Implementation guidance covers data preprocessing, encoding strategies, and software stacks (Qiskit, PennyLane). While acknowledging current hardware constraints, we highlight how quantum-inspired methods can enhance portfolio construction, risk segmentation, and market regime detection. The goal is to equip finance professionals with both conceptual grounding and practical know-how to evaluate quantum clustering in investment analysis, risk management, and quantitative finance.
Keywords: quantum-inspired clustering, Hilbert space, superposition, entanglement, financial datasets, portfolio diversification, risk segmentation, regime detection
APA
Reynoso, A. (2025). Basic Introduction to Quantum Clustering for Finance — A Maieutic Guide (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_classification_portfolio_diversification-v01
BibTeX
@article{reynoso_basic_quantum_clustering_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Basic Introduction to Quantum Clustering for Finance — A Maieutic Guide},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_classification_portfolio_diversification-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-emerging_intelligence_marl-v01
We study emerging market structures through multi-agent reinforcement learning (MARL) across two domains: market microstructure and corporate acquisition strategy. Both are cast as discounted Markov games (and Dec-POMDPs under partial observability). A learning stack progresses from tabular Q-learning to policy optimization with centralized training, decentralized execution, combining value decomposition for cooperation, counterfactual baselines for mixed incentives, population training/mean-field methods for strategic diversity, risk-sensitive & constrained objectives for financial realism, and continual-learning regularizers plus trust-region updates for stability.
Case studies. (1) Trading: a triad—market maker, momentum, contrarian—interacts via a reduced-form impact model; joint learning reproduces trend amplification, rapid reversals at flow extremes, volatility clustering, spread dynamics, and inventory cycling. Incentive tweaks (temporary team bonuses, batch-style pauses) dampen amplification and limit spread blowouts with minimal private-performance cost. (2) Corporate finance: multiple acquirers face sequences of heterogeneous targets with noisy synergies and capital constraints; actions are bid / pass / partner. We formalize coalition formation and an efficiency score penalizing winner’s-curse errors; mixed policies dominate: partner on uncertain/expensive targets, bid solo on routine deals, walk when efficiency or capital discipline fails.
Diagnostics & governance. We introduce metrics separating private returns from system behavior: an amplification coefficient (price formation), a liquidity-cycle index, policy-movement stability metrics, and deal-efficiency distributions. Deployment guidance covers TRACE-style decision packages, model/system cards, immutable audit logs, drift monitoring (data/behavior/outcome), adaptation gates, anti-collusion safeguards, and reproducible, signed artifacts. The result is a unified formalism and didactic bridge from Q-tables to deep MARL, with runnable experiments that translate into actionable levers and mechanism insights for institutions where stability, efficiency, and accountability are first-class objectives.
Keywords: MARL, Dec-POMDP, market microstructure, acquisitions, coalition formation, risk-sensitive RL, governance, auditability
APA
Reynoso, A. (2025). Emerging Intelligent Structures in Finance — Multi-Agent Reinforcement Learning (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-emerging_intelligence_marl-v01
BibTeX
@article{reynoso_emerging_intelligent_structures_marl_2025_v01,
author = {Alejandro Reynoso},
title = {Emerging Intelligent Structures in Finance — Multi-Agent Reinforcement Learning},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-emerging_intelligence_marl-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-neuroplasticity_credit_ratings_models-v01
Credit risk assessment requires both responsiveness to new information and continuity with established knowledge. This paper proposes a neuroplastic architecture for adaptive credit ratings, inspired by biological learning and implemented via Elastic Weight Consolidation (EWC). Using Fisher-Information–derived parameter memory, models retain critical knowledge from prior regimes while adapting to structural shifts in leverage, liquidity, and profitability.
A staged implementation (with an interactive Colab): from basic plastic learners → governed EWC models → agentic systems that detect regime changes and dynamically tune consolidation strength (λ) and memory curvature. Integrated into rating workflows, the system produces interpretable PD trajectories, calibration panels, and governance-ready narratives.
We outline a forward agenda that blends reasoning models, neuro-symbolic hybrids, and quantum-inspired optimization to represent financial interdependencies with greater structure and efficiency. Future extensions include agent constellations (A2A collaboration for calibration, surveillance, governance) and Model–Context Protocol (MCP) integration for traceability, reproducibility, and controlled autonomy. The result is an adaptive, explainable, auditable rating system that learns continuously without catastrophic forgetting, bridging cognitive adaptation with institutional accountability.
Keywords: credit ratings, Elastic Weight Consolidation, Fisher Information, neuroplasticity, regime shift detection, agentic systems, governance, MCP
APA
Reynoso, A. (2025). From Neuroplasticity to Dynamic Credit Ratings (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-neuroplasticity_credit_ratings_models-v01
BibTeX
@article{reynoso_neuroplasticity_dynamic_credit_ratings_2025_v01,
author = {Alejandro Reynoso},
title = {From Neuroplasticity to Dynamic Credit Ratings},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-neuroplasticity_credit_ratings_models-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-letters_from_quantum_world-v01
This paper presents a quantum-inspired framework for uncovering hidden structure in financial and reasoning systems via connectivity-aware clustering. Unlike geometric or covariance-based methods, clustering is modeled as coherent amplitude evolution in a shared Hilbert space: each entity (e.g., a company in an investment universe or a reasoning molecule in a knowledge base) is encoded as a quantum state whose amplitude and phase capture relevance and relational context. Through unitary evolution and interference, latent communities emerge that reflect true informational connectivity.
The architecture comprises three layers: (i) Amplitude–Phase Encoding (map multi-dimensional features to normalized complex vectors), (ii) ZZ Coupling (structured phase shifts proportional to pairwise connectivity), and **(iii) Coined Quantum Random Walk (QRW) (context-conditioned amplitude propagation). The resulting steady-state probability landscape highlights regions of constructive interference—clusters that embody shared functional behavior.
Two executive memoranda demonstrate versatility: a BlackRock Investment Committee scenario builds diversified sleeves grounded in connectivity (not mere correlation); a McKinsey Advisory memo repurposes the same math to synthesize playbooks—stepwise reasoning sequences aligned to client goals. In both cases, recommendations are traceable to amplitude dynamics, delivering transparency and auditability. By coupling quantum formalism with practical governance, the work offers a reproducible pathway to explainable, connectivity-driven decision intelligence that bridges finance, strategy, and reasoning.
Keywords: connectivity-aware clustering, Hilbert space, amplitude/phase encoding, ZZ coupling, quantum random walk, decision intelligence, auditability
APA
Reynoso, A. (2025). Letters from the Quantum World — Connectivity-Driven Decision Intelligence (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-letters_from_quantum_world-v01
BibTeX
@article{reynoso_letters_from_quantum_world_2025_v01,
author = {Alejandro Reynoso},
title = {Letters from the Quantum World — Connectivity-Driven Decision Intelligence},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-letters_from_quantum_world-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-tunneling_financial_collapses-v01
This paper models abrupt currency crises as tunneling events in a financial potential landscape, linking heterogeneous-agent behavior to macro instability through an operator formalism analogous to quantum mechanics. Rather than metaphor, metastability, coordination, and uncertainty become measurable structural features. The open-system representation defines a geometry of stability: barrier width/height (policy & liquidity constraints), uncertainty (γ), and coordination strength (λ), yielding falsifiable diagnostics for policy and risk once calibrated and governed.
Contributions: (A) Explains long calm spells punctuated by abrupt breaks without catastrophic fundamentals. (B) Extends target-zone/smooth-pasting models by replacing reflective diffusion with penetrable barriers whose geometry governs regime shifts. (C) Suggests derivatives/risk approaches where payoffs depend on barrier penetration and coordination, not volatility alone. (D) Reframes crises from randomness to geometry and coherence, highlighting joint roles of liquidity, policy, and collective behavior in regime fragility and resilience.
Keywords: currency crises, tunneling, potential landscape, metastability, coordination, target zones, derivatives risk, operator formalism
APA
Reynoso, A. (2025). Tunneling and Currency Crises — A Physics-Inspired Framework (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-tunneling_financial_collapses-v01
BibTeX
@article{reynoso_tunneling_currency_crises_2025_v01,
author = {Alejandro Reynoso},
title = {Tunneling and Currency Crises — A Physics-Inspired Framework},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-tunneling_financial_collapses-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-molecular_porosity_agentic_models-v01
Modern multi-agent systems often fail under crisis not from ignorance but from communication overload, which dissolves coordination. This paper introduces adaptive porosity—a control-theoretic mechanism that regulates information flow among agents via dynamic permeability coefficients governed by a PID controller. Communication is treated as a thermodynamic process, maintaining system entropy H(t) near a target H* to prevent cognitive flooding while preserving flexibility.
We evaluate the concept with a “reality-show” simulation, Don’t Panic!, where two identical organizations face the same pandemic scenario: Team Rigid (fixed communication) vs Team Adaptive (porosity-controlled). The split-screen format exposes causal mechanics: panic −35%, coherence +71%, and entropy stabilized within 5% of target. Beyond results, we show how narrative evaluation can render complex AI architectures observable, interpretable, and emotionally intelligible to stakeholders. By blending thermodynamics, control theory, and storytelling, adaptive porosity reframes organizational intelligence as a rhythmic balance between openness and restraint—a design principle for autonomous systems that, under uncertainty, don’t panic— they breathe.
Keywords: adaptive porosity, multi-agent systems, PID control, entropy regulation, crisis management, organizational coherence, thermodynamic communication
APA
Reynoso, A. (2025). Agentic Crisis Management via Adaptive Porosity (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-molecular_porosity_agentic_models-v01
BibTeX
@article{reynoso_agentic_crisis_management_adaptive_porosity_2025_v01,
author = {Alejandro Reynoso},
title = {Agentic Crisis Management via Adaptive Porosity},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-molecular_porosity_agentic_models-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-tunneling_cds_sovereigns-v01
This paper develops a physics-inspired framework in which abrupt currency and sovereign-risk crises are modeled as tunneling events in a financial potential landscape. Heterogeneous-agent behavior is linked to macro-level instability through an operator formalism analogous to quantum mechanics. The open-system equation renders metastability, coordination, and uncertainty as measurable structural features rather than metaphors.
We provide a rigorous map from microfoundations to a geometry of stability defined by barrier width/height (policy and liquidity constraints), uncertainty (γ), and coordination strength (λ). These parameters yield falsifiable diagnostics relevant to policy and risk once calibration and governance frameworks are in place.
Contributions: (A) Explains calm spells punctuated by abrupt breaks without catastrophic fundamentals; (B) Extends target-zone/smooth-pasting models by replacing reflective diffusion boundaries with penetrable barriers whose geometry governs regime shifts; (C) Suggests derivatives valuation and risk methods—especially for sovereign CDS—where payoffs depend on barrier penetration and coordination rather than volatility alone; (D) Opens a conceptual frontier, reframing crises from randomness to geometry and coherence, highlighting how liquidity, policy, and collective behavior jointly determine regime fragility and resilience.
Keywords: sovereign CDS, currency crises, tunneling, potential landscape, metastability, coordination, derivatives valuation, operator formalism
APA
Reynoso, A. (2025). Sovereign CDS and Quantum Tunneling — A Physics-Inspired Crisis Framework (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-tunneling_cds_sovereigns-v01
BibTeX
@article{reynoso_sovereign_cds_quantum_tunneling_2025_v01,
author = {Alejandro Reynoso},
title = {Sovereign CDS and Quantum Tunneling — A Physics-Inspired Crisis Framework},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-tunneling_cds_sovereigns-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-geometry_of_risk_speech-v01
This lecture introduces a tunneling model as a structural paradigm for financial instability, shifting the lens from probability to geometry. Rather than treating markets as random diffusions where crises are rare accidents, the system is viewed as a landscape with valleys of stability and ridges of danger. Crises become phase transitions—“tunneling” through a weakening barrier without a large external shock—whose likelihood depends on the barrier’s geometry (height, width, curvature) rather than noise alone.
An effective potential (U_{\mathrm{eff}}) formalizes the landscape as the sum of three components: Policy Capacity ((U_{\mathrm{policy}})), Market Liquidity ((U_{\mathrm{liq}})), and Reflexive Coordination ((U_{\mathrm{coord}})). In this framework, volatility is not the cause of instability, but the speed of exploration over the terrain; stability is governed by barrier height and width. Policy must therefore be architectural: governments act as landscape engineers—e.g., reserve accumulation raises barrier height, while liquidity facilities widen the barrier—shifting attention from volatility management to structural design for resilience. The aim is to recast crises not as random jumps but as predictable avalanches emerging from the geometric fabric of global finance.
Keywords: tunneling model, financial landscape, effective potential, policy capacity, market liquidity, reflexive coordination, phase transitions, crisis geometry
APA
Reynoso, A. (2025). From Probability to Geometry — A Lecture on the Tunneling Model of Financial Instability (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-geometry_of_risk_speech-v01
BibTeX
@article{reynoso_probability_to_geometry_2025_v01,
author = {Alejandro Reynoso},
title = {From Probability to Geometry — A Lecture on the Tunneling Model of Financial Instability},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-geometry_of_risk_speech-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers_geometry_and_diversification-v01
Physicists entering finance often stand between the elegance of models and the pragmatics of investment committees. This paper serves as a pedagogical bridge, translating two paradigms of portfolio diversification—classical clustering and quantum Laplacian dynamics—into a common mathematical and conceptual language. The focus is structural and interpretive, not just numerical: how geometry becomes risk and how amplitude becomes conviction. The narrative moves deliberately from eigenvectors and Hilbert spaces to sectors, allocations, and boardroom discussions, training technically proficient readers to think, speak, and argue like financial professionals without abandoning mathematical rigor.
Keywords: diversification, quantum Laplacian dynamics, clustering, Hilbert spaces, eigenvectors, portfolio construction, pedagogy
APA
Reynoso, A. (2025). Quantum Geometry & Diversification — An Educational Bridge for Physicists in Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers_geometry_and_diversification-v01
BibTeX
@article{reynoso_quantum_geometry_diversification_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum Geometry & Diversification — An Educational Bridge for Physicists in Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers_geometry_and_diversification-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-memorandum_qcapm-v01
This memorandum introduces the Quantum Capital Asset Pricing Model (Q-CAPM) as a geometric, practitioner-friendly extension of classical CAPM. The market is modeled as a dynamic network where portfolios carry both allocation weights and an informational phase, capturing how positions interact coherently with the rest of the system. Risk, return, and diversification are rendered as features of a financial landscape:
- Risk = Curvature (tension): sharp curvature signals concentration and stress;
- Return = Slope: directional incentive embedded in the terrain;
- Diversification = Smoothing: structural smoothing flattens curvature and damps shock propagation.
Optimization is reframed as choosing the smoothest descent path—balancing curvature (risk) against slope (return)—a geometric analogue of the efficient frontier, with risk appetite steering the landscape toward opportunity or safety.
A Quantum Random Walk (QRW) provides dynamic discovery: from any starting allocation, information diffuses, unstable oscillations cancel, and the system converges to its smoothest mode—the equilibrium diversification pattern. Strategically, Q-CAPM clarifies consensus as structural alignment across decision-makers and offers a native bridge to quantum computation, where the same geometry enables richer, faster exploration of systemic modes on quantum processors.
Keywords: Q-CAPM, geometric finance, curvature risk, QRW, diversification, portfolio optimization, quantum computing
APA
Reynoso, A. (2025). Memorandum — Quantum CAPM (Q-CAPM) for Practitioners (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-memorandum_qcapm-v01
BibTeX
@article{reynoso_qcapm_memorandum_2025_v01,
author = {Alejandro Reynoso},
title = {Memorandum — Quantum CAPM (Q\textendash CAPM) for Practitioners},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-memorandum_qcapm-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-evolutionary_reasoning_molecules-v01
This paper reframes financial intelligence through an evolutionary rather than optimization lens. Where optimization reflects a Newtonian worldview—reductionist, deterministic, equilibrium-seeking—evolution embraces feedback, complexity, and emergence. The goal is not to compute a single best outcome, but to model the space of viable outcomes that remain coherent under perturbation.
Implications for finance are immediate. Portfolio management becomes a study of evolutionary geometry—how configurations of assets, rules, and parameters deform under stress while preserving functional integrity. Regulation becomes fitness-constraint design—boundaries that guide exploration without suppressing innovation. AI systems become evolving populations of reasoning molecules—modular units of logic whose interactions generate emergent structure. The paradigm aligns finance more closely with biology and thermodynamics than with static equilibrium economics, replacing rational optimality with adaptive disequilibrium. Practitioners shift from optimizers to caretakers: designers of systems that endure—auditable, stable, and adaptive—amid uncertainty. The thesis: in the age of complexity, evolution, not optimization, is the grammar of survival.
Keywords: evolutionary geometry, reasoning molecules, fitness-constraint design, adaptive disequilibrium, robustness, complexity finance, governance
APA
Reynoso, A. (2025). Evolutionary Reasoning Molecules — From Optimization to Survival (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-evolutionary_reasoning_molecules-v01
BibTeX
@article{reynoso_evolutionary_reasoning_molecules_2025_v01,
author = {Alejandro Reynoso},
title = {Evolutionary Reasoning Molecules — From Optimization to Survival},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-evolutionary_reasoning_molecules-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-corporate_finance_goes_quantum-v01
This lecture traces a five-movement journey from the mechanical clarity of the classical firm to the fluid geometry of quantum reasoning in corporate finance. It shows how superposition, interference, and coherence illuminate liquidity management, decision systems, and governance. Rather than treating uncertainty as an obstacle, the framework embraces it as the medium through which intelligence, ethics, and creativity emerge. The result is a shift from clockwork metaphors to a resonant field of probabilities, trust, and adaptation—where financial control is designed to harmonize exploration and constraint.
Keywords: quantum reasoning, superposition, interference, coherence, liquidity management, governance, decision systems, corporate finance
APA
Reynoso, A. (2025). What Happens When Corporate Finance Goes Quantum — A Five-Movement Lecture (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-corporate_finance_goes_quantum-v01
BibTeX
@article{reynoso_corporate_finance_goes_quantum_2025_v01,
author = {Alejandro Reynoso},
title = {What Happens When Corporate Finance Goes Quantum — A Five-Movement Lecture},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-corporate_finance_goes_quantum-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_regulation_governance-v01
This paper develops a mathematical framework for quantum regulation—the design and analysis of governance in systems where agents, markets, and information interact via entangled states rather than classical separable configurations. We formalize the distinction between coupling (a Hamiltonian property) and entanglement (a state property), and model regulatory intervention as measurement and modulation of coherence in a financial Hilbert space. Using operator theory, we derive conditions for optimal regulation, analyze coherence loss under classical supervision, and show that equilibrium in quantum-governed markets emerges as a superposition of correlated states minimizing systemic energy. The result is a theoretical foundation for adaptive, coherence-preserving oversight of complex financial networks.
Keywords: quantum regulation, entanglement, coherence, Hamiltonian coupling, operator theory, financial Hilbert space, systemic energy, market governance
APA
Reynoso, A. (2025). Quantum Regulation — Coherence-Preserving Governance in Entangled Financial Systems (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_regulation_governance-v01
BibTeX
@article{reynoso_quantum_regulation_2025_v01,
author = {Alejandro Reynoso},
title = {Quantum Regulation — Coherence-Preserving Governance in Entangled Financial Systems},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_regulation_governance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-emerging_intelligence_in_finance-v01
This paper develops a mathematical and conceptual framework that treats financial systems as self-organizing, coherence-seeking networks rather than externally governed agents. Drawing on synergetics, information geometry, and quantum coherence, we reinterpret core phenomena—volatility, liquidity, regulation—as expressions of phase alignment, entropy flow, and structural feedback.
Simulations spanning personal-finance dynamics to system-level regulatory environments show multi-scale emergence of coherence: from alignment of individual behaviors to synchronization across institutions and markets. Coherence serves as a health metric balancing diversity and stability; entropy measures adaptive learning; and an autonomy function (A(t)) quantifies the capacity to maintain order under turbulence, outlining a maturity ladder from reactive to self-sustaining regimes.
The central thesis is that intelligence in finance is emergent—networks learn to convert disorder into information. Regulation becomes a question of geometry, not control: design feedback architectures that encourage intelligent self-organization. We synthesize these ideas via graph-based and quantum representations of coherence and discuss implications for generative-AI-driven governance.
Keywords: emergence, coherence, synergetics, information geometry, quantum coherence, autonomy function, financial regulation, self-organization
APA
Reynoso, A. (2025). Emerging Intelligence in Financial Models (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-emerging_intelligence_in_finance-v01
BibTeX
@article{reynoso_emerging_intelligence_financial_models_2025_v01,
author = {Alejandro Reynoso},
title = {Emerging Intelligence in Financial Models},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-emerging_intelligence_in_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-physics_chemistry_biology_ai-v01
For most of history we looked outward—to stars, atoms, and the mathematics of life. This mini-book turns inward, tracing how the grand laws of physics, chemistry, and biology quietly shape modern artificial intelligence. Written for a broad audience—no equations required—it frames AI as the next chapter in nature’s experiment: matter becoming mindful.
We begin with physics, where every computation has an energy cost and every thought obeys constraints of information and thermodynamics. We move to chemistry, reading learning as a reaction that seeks equilibria under catalysts, pathways, and activation barriers. Finally biology provides the blueprint: adaptation, variation, selection, memory, and homeostasis as organizing principles for systems that learn and survive. The arc argues that intelligence is not an alien artifact but an emergent property of structured matter; the dark can indeed be full of light—if we learn how nature thinks about nature.
Keywords: thermodynamics of computation, learning as reaction, adaptation, emergence, information, energy, biology-inspired AI, scientific foundations of AI
APA
Reynoso, A. (2025). Mini-Book — How Nature Thinks About Nature (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-physics_chemistry_biology_ai-v01
BibTeX
@article{reynoso_how_nature_thinks_2025_v01,
author = {Alejandro Reynoso},
title = {Mini\textendash Book — How Nature Thinks About Nature},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-physics_chemistry_biology_ai-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-geometry_algo_trading-v01
For centuries, finance has navigated its world using flat maps—charts, probabilities, and time series designed to project complex behavior onto two-dimensional surfaces. But markets, like landscapes, are not flat. They curve, fold, and twist under the invisible forces of sentiment, liquidity, and belief.
This paper introduces a quantum-inspired framework where market information is represented as a living geometry. Through the lens of a "Quantum Dashboard," we translate volatility, correlation, and uncertainty into curvature, entropy, and topological flow. The trader ceases to be a statistician predicting future prices and becomes instead a navigator reading the terrain of information. Risk is no longer measured by variance, but by the slope of the manifold on which capital moves.
Using Apple (AAPL) as a narrative and experimental case, we explore how geometry reveals what probability conceals: the buildup of tension before market transitions, the coiling of belief into narrow ridges of consensus, and the sudden release of energy that produces volatility. The dialogue between human intuition (embodied in the trader, Picard) and geometric reasoning (embodied in the algorithmic agent, Spock) illustrates a new synthesis between cognition and computation—between instinct and curvature.
This work is not about quantum computing, but about quantum thinking—about reasoning in curved spaces where many futures coexist until observation collapses them into experience. It proposes a new metaphor for finance: the trader as pilot, markets as atmospheric terrains, and the dashboard as orographic radar for navigating information turbulence. In a world where classical models assume linearity, this approach restores dimensionality, inviting practitioners to move beyond prediction toward perception.
Keywords: quantum geometry, market regimes, information manifolds, curvature, entropy, topological flow, algorithmic trading, quantum dashboard, geometric finance
APA
Reynoso, A. (2025). Market Regime Discovery Using Quantum RBS (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-geometry_algo_trading-v01
BibTeX
@article{reynoso_market_regime_quantum_2025_v01, author = {Alejandro Reynoso}, title = {Market Regime Discovery Using Quantum RBS}, year = {2025}, version = {v01}, publisher = {GitHub}, url = {https://github.com/alexdibol/papers/releases/tag/papers-geometry_algo_trading-v01} }
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-markets_speak_geometry-v01
This paper presents an edited live dialogue between Marcus Lin (Chief Strategist, Horizon Capital) and the firm’s Quantum Trading Terminal (Q-Terminal)—a reasoning engine that models markets using geometric and topological principles. The discussion centers on a potential systemic shift in tech, specifically an initially bullish AAPL position. While classical models (LSTM, HMM) signaled continuation, Q-Terminal flagged emergent instability via information geometry metrics—Fisher–Rao curvature ((\kappa)) and entropy—arguing that high consensus and synchronized momentum had created a manifold ridge: a metastable state prone to tunneling into a lower-energy basin. Classical tools, tied to Euclidean independence and time-step transitions, appeared blind to structural deformation.
Acting on the geometric diagnosis, Marcus overrode the classical signals: cutting long exposure, adding a short hedge, and rotating into cyclicals to minimize expected Hamiltonian energy and move toward a lower-curvature, phase-aware configuration. Portfolio entropy stabilized, underscoring the closing line: “Risk is not volatility; it is curvature.” The transcript offers a real-world case study of geometric/quantum-inspired risk management in live decision-making.
Keywords: information geometry, Fisher–Rao curvature, entropy, tunneling, Hamiltonian energy, AAPL, systemic shift, geometric risk
APA
Reynoso, A. (2025). When Markets Speak Geometry — A Dialogue with a Quantum Trading Terminal (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-markets_speak_geometry-v01
BibTeX
@article{reynoso_when_markets_speak_geometry_2025_v01,
author = {Alejandro Reynoso},
title = {When Markets Speak Geometry — A Dialogue with a Quantum Trading Terminal},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-markets_speak_geometry-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-geometric_algo_trading-v01
This paper develops a mathematically rigorous framework that models financial markets as evolving informational manifolds, bri
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dging classical probabilistic finance with quantum-geometric reasoning. Using information geometry, curvature tensors, entropy flows, and manifold dynamics, the work encodes market information into a Quantum Restricted Boltzmann System (QRBS) that learns directly from data.
Each section contributes a complete pipeline from theory to implementation:
- Theoretical foundation grounded in information geometry and statistical mechanics;
- Mathematical description with equations, tensors, and operators that define the market-information manifold;
- Key theorems proving geometric–statistical equivalence;
- QRBS mapping that translates the constructs into a trainable computational system.
The result moves the “Quantum Hedge Fund” concept from metaphor to mechanism, providing conceptual clarity and formal precision for geometric, data-driven trading architectures.
Keywords: information geometry, curvature, entropy flow, manifold dynamics, QRBS, quantum-inspired finance, geometric trading models
APA
Reynoso, A. (2025). Mathematical Foundations of the QRBS Trading Model (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-geometric_algo_trading-v01
BibTeX
@article{reynoso_qrbs_trading_foundations_2025_v01,
author = {Alejandro Reynoso},
title = {Mathematical Foundations of the QRBS Trading Model},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-geometric_algo_trading-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-diffusion_vs_transformers-v01
This paper presents a theory-first decision framework for generating synthetic company financials from profile prompts (industry, size, growth, leverage, geography). Instead of funding a bake-off between autoregressive transformers and diffusion models, we argue—on mathematical and governance grounds—for projected conditional diffusion. The case rests on four structural fit criteria:
- Hard constraints: accounting identities must hold exactly. A projected diffusion sampler composes each denoising step with an explicit projector onto the accounting manifold, achieving equality at machine precision; autoregressive pipelines require brittle deterministic heads or post-hoc repairs.
- Permutation agnosticism: financial statement dimensions have no canonical order. Diffusion updates all coordinates in parallel, avoiding order-dependent factorisations.
- Continuous precision: diffusion operates natively in (\mathbb{R}^d), preserving numerical fidelity; tokenization introduces resolution limits ill-suited to financial magnitudes.
- Distributional fidelity: under standard assumptions, diffusion preserves heavy tails and copula structure while enforcing constraints with minimal distortion.
We specify cFDM—Conditional Financial Diffusion: an MLP-Mixer/FiLM denoiser with classifier-free guidance over profiles and a typed constraint library (linear KKT, hierarchical cascades, bounds). The result is an auditable, few-step sampler that guarantees feasibility, aligns with model-risk governance, and avoids the cost and delay of a two-model bake-off.
Keywords: projected diffusion, accounting manifold, financial statement synthesis, classifier-free guidance, copula structure, model risk governance
APA
Reynoso, A. (2025). Diffusion Models for Corporate Benchmarking (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-diffusion_vs_transformers-v01
BibTeX
@article{reynoso_diffusion_models_corporate_benchmarking_2025_v01,
author = {Alejandro Reynoso},
title = {Diffusion Models for Corporate Benchmarking},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-diffusion_vs_transformers-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-lecture_go_quantum-v01
This article presents a clear, public-lecture narrative for a hybrid clustering pipeline in fund design: compress first with a learned, noise-aware map; then apply a quantum-inspired similarity only when the data’s shape bends (loops, cycles, non-Euclidean structure). Using city-and-music metaphors—rather than heavy math—we explain why ordinary distance tools fail on curved structure, how careful compression preserves the story while removing clutter, and when a “quantum lens” (overlap-based similarity rather than raw distance) earns its keep. The guide shows how to decide before spending compute, how to read curvature signals, and how to turn shape into cleaner sleeves, truer diversification, and disciplined deployment. The rule of thumb: if the world is curved, compress first and go quantum; if it’s flat, keep it simple.
Keywords: clustering, dimensionality reduction, quantum-inspired similarity, topology/loops, diversification sleeves, practical heuristics
APA
Reynoso, A. (2025). When the World Bends — Go Quantum (A Public-Lecture Guide) (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-lecture_go_quantum-v01
BibTeX
@article{reynoso_world_bends_go_quantum_2025_v01,
author = {Alejandro Reynoso},
title = {When the World Bends — Go Quantum (A Public\textendash Lecture Guide)},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-lecture_go_quantum-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-quantum_decompression_vae_latents-v01
We develop and evaluate a two-stage clustering pipeline for fund design and portfolio diversification: (1) compress 50→10D with a Variational Autoencoder (VAE); (2) lift the latents into 8×8 density matrices and cluster using quantum information geometry (fidelity/Bures). On Gaussian-mixture benchmarks, the quantum variants underperform (raw 50D k-means ARI ≈ 0.43 vs. VAE→Quantum ARI ≈ 0.05), diagnosing that the data are flat/transitive and do not require curved analysis.
We then propose a curvature theory with necessary and sufficient conditions for: (A) data geometry (non-transitive similarity, geodesic–chord gap); (B) VAE encoders (local quasi-isometry, no latent collapse) to preserve curvature; and (C) Hilbert lifts (non-commutativity, fidelity separation) to detect curvature. Under these conditions we prove (D) spectral-gap and clustering improveme
B998
nts for fidelity/Bures over Euclidean methods. A ring/cycle benchmark with noise demonstrates non-trivial curvature, and we provide a Colab protocol plus a governance-friendly decision rule. Message: curvature is the signal. If curvature markers persist after compression, Hilbert geometry is justified and can improve diversification clustering; if not, classical methods should—and do—win.
Keywords: VAE, density matrices, fidelity, Bures metric, curvature diagnostics, quasi-isometry, spectral gap, diversification clustering
APA
Reynoso, A. (2025). Hybrid VAE + Quantum-Inspired Company Clustering (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-quantum_decompression_vae_latents-v01
BibTeX
@article{reynoso_hybrid_vae_quantum_clustering_2025_v01,
author = {Alejandro Reynoso},
title = {Hybrid VAE + Quantum\textendash Inspired Company Clustering},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-quantum_decompression_vae_latents-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-interview_are_markets_relativistic-v01
This paper presents a practical geometry of price dynamics that reconciles two views of markets: efficient in the large (days–quarters) and imperfect in the small (seconds–microseconds). We model short-horizon trading as navigation over a market landscape estimated from observable microstructure data—spreads, depths, imbalance, resiliency, queue metrics, per-link latencies, and fees. Core geometric objects encode operational realities: a metric (cost to move in given directions), cones (what’s reachable given latencies and opportunity half-life), a connection (directional memory of how the best price was reached), and curvature/torsion (systematic residuals from tiny round-trips or order of micro-steps).
The framework explains when a visible “arb” is a mirage (outside your cone), why thin patches spike costs, and how self-gravity (your own participation) mandates pacing. We provide two aligned tracks: (i) a plain-language Q&A clarifying concepts and misconceptions, and (ii) an implementation guide covering state definition, metric block estimation, cone measurement, curvature detection, fee integration, and geodesic routing on a discrete lattice—all under standard guardrails (law-of-one-price, no free lunch, no manipulation). The system is auditable by construction: every route choice is replayable; cone breaches and loop artifacts trigger quarantine and safe fallbacks. The result is a unified, data-driven blueprint for cheaper, safer execution that shows how markets can be nearly efficient globally yet locally exploitable in capacity-limited, self-extinguishing ways.
Keywords: market microstructure, execution routing, information geometry, latency cones, curvature, torsion, geodesics, auditability
APA
Reynoso, A. (2025). Many Ways of Trading — One Single Framework (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-interview_are_markets_relativistic-v01
BibTeX
@article{reynoso_many_ways_trading_one_framework_2025_v01,
author = {Alejandro Reynoso},
title = {Many Ways of Trading — One Single Framework},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-interview_are_markets_relativistic-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-unified_geometry_pruce_dynamics-v01
We present a relativity-inspired, market-native geometric theory of price formation (GTPF) that unifies low-, medium-, and high-frequency regimes within a single data-derived state manifold. Geometry is inferred from standard microstructure observables (spread, depth, imbalance, resiliency, queue state, cross-venue latencies/fees). A metric encodes local cost/variance; a connection captures path-dependence; curvature measures loop residuals; torsion records order-of-operations asymmetry; and a gauge one-form accounts for fees, rebates, and inventory carry. A single action functional yields dynamics with frequency-specific limits: flat (LF), causal with cones (MF), and curved/torsional (HFT). Imposing finance constraints (no-arbitrage, no price manipulation, long-horizon efficiency) selects an admissible subclass of geometries. We provide practical estimators, a volatility decomposition where short-horizon variance is geometric, and geodesic routing with cone/curvature controls. The framework reconciles global efficiency with exploitable short-run microstructure features and yields an implied-geometry catalog that classifies market states directly from data.
Keywords: market microstructure, information geometry, latency cones, curvature, torsion, gauge forms, geodesic execution, no-manipulation
APA
Reynoso, A. (2025). Is High-Frequency Trading Relativistic? — A Unified Geometric Theory of Price Formation (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-unified_geometry_pruce_dynamics-v01
BibTeX
@article{reynoso_hft_relativistic_gtpf_2025_v01,
author = {Alejandro Reynoso},
title = {Is High\textendash Frequency Trading Relativistic? — A Unified Geometric Theory of Price Formation},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-unified_geometry_pruce_dynamics-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-ten_experiments_training_ai_finance-v01
This volume presents ten tightly engineered experiments that turn minimalist training principles into executable practice for financial AI. Each notebook implements core learning mechanisms from first principles—gradients, memory, contrastive geometry, ensembles, optimization—while embedding governance inside the loop (calibration, audit logs, differential privacy, circuit breakers). Contributions: (i) a finance-first framing that links objectives to decision-relevant risk measures; (ii) a governance-native toolkit for auditable, reproducible workflows; (iii) compact, dependency-light implementations designed for constrained compute and regulated environments.
Coverage spans supervised, contrastive, and reinforcement-style settings: risk-controlled portfolio selection, monotone credit default modeling, contrastive fraud detection with traceable saliency, regime-aware signal learning, hierarchical earnings forecasting, multi-agent decision support, federated training with privacy budgets, and robustness diagnostics to expose overfitting/leakage. Evaluation emphasizes calibration, stability under shift, and cost-sensitive metrics aligned to institutional risk appetites.
Across studies, uncertainty, shift, and risk are first-class: contrastive objectives localize failure modes; regime memory stabilizes signals; risk-constrained descent surfaces return–drawdown–liquidity trade-offs. Artifacts include deterministic seeding, leak unit tests, loss-surface telemetry, and red-team prompts, yielding transparent, auditable workflows. Target audience: practitioners who must ship dependable systems under tight compute, limited data, and regulatory oversight. The result is a practical playbook from principled objectives to decision-grade outputs that withstand real-world constraints while advancing institutional capability.
Keywords: minimalist training, governance-native ML, calibration, differential privacy, regime memory, contrastive objectives, robustness, financial AI
APA
Reynoso, A. (2025). Ten Training Experiments for AI in Finance — A Minimalist, Governance-Native Playbook (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-ten_experiments_training_ai_finance-v01
BibTeX
@article{reynoso_ten_training_experiments_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Ten Training Experiments for AI in Finance — A Minimalist, Governance\textendash Native Playbook},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-ten_experiments_training_ai_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-step_by_step_grpo_reasoninf-v01
This work presents a minimalist, governance-native reasoning model for Harvard-style business cases, engineered for auditability, portability, and operational clarity. The system follows a four-step pipeline—READ → PLAN → SOLVE → JUSTIFY and is trained with generalized Reinforcement Preference Optimization (gRPO) against a five-axis rubric: problem framing, analysis depth, finance soundness, feasibility, and risk/governance. All artifacts—datasets, rubrics, checkpoints, traces, metrics, and reports—are produced in a dependency-light stack with checksums for reproducibility.
We build synthetic, balanced case corpora and preference pairs, then train a compact ReasoningCore with a frozen reference policy under KL control. Evaluation includes weighted rubric scores, win-rate matrices / optional Elo vs. rule-based and retrieval baselines, ablations, calibration analysis, and domain-shift stress tests. Governance controls comprise a model card, preference data card, constraint/forbidden-output guards, uncertainty-based escalation, decision logs, and incident playbooks. An integration router sends cases to the small model first and escalates to a larger model under quantified uncertainty or risk—delivering predictable cost/quality profiles for institutional workflows.
The project ships with a master index, cross-references, artifact inventory, reproducibility report, final checklist, and a release bundle for hand-off. Though educational in scale, the blueprint emphasizes tractable oversight via clear rubrics, instrumented traces, and verifiable outputs, and outlines a pragmatic path to production hardening with human-in-the-loop review, expanded datasets, and stronger retrieval—while preserving minimalism, transparency, and governance-first engineering.
Keywords: gRPO, rubric-aligned training, institutional governance, calibration, uncertainty escalation, auditability, Harvard-style cases, minimal compute
APA
Reynoso, A. (2025). A Step-by-Step Construction of a gRPO-Trained Reasoning Model (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-step_by_step_grpo_reasoninf-v01
BibTeX
@article{reynoso_grpo_step_by_step_2025_v01,
author = {Alejandro Reynoso},
title = {A Step{-}by{-}Step Construction of a gRPO{-}Trained Reasoning Model},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-step_by_step_grpo_reasoninf-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/paper-architect_ai_finance-v01
What does AI really mean for a business? Not another model logo, but a management choice about how you work, where you win, and how you keep control at scale. This paper offers a leader-friendly framework on three pillars.
(1) Capability × Impact. We define five capability levels (from conversational assistants to autonomous organizations) crossed with five impact tiers (from productivity gains to org design). The 2×5 view lets executives right-size ambition per domain and avoid tool sprawl.
(2) Operate: reason → act. Adopt a simple rhythm: reason first, then act. Turn goals into short, defensible plans; let agentic systems execute inside guardrails; learn from clean telemetry; promote only what works. The quiet hero is orchestration (identities, connectors, checkpoints, traces): tools can be swapped; orchestration becomes your house style and moat.
(3) Three-stage roadmap. Start now with: Strategic Audit (clarify targets/boundaries; stop assistant-only spending), Pilot & Orchestration Baseline (minimal permissions, connectors, checkpoints, telemetry; prove value in low-risk, high-volume work), and Institutionalize & Price (embed gates, version playbooks, train-by-doing, shift to outcome-based pricing).
For finance and boards, we compress success to a small scorecard: velocity (cycle time, version attainment), value (cost/time per outcome at quality), and exposure (defects, policy/privacy events, audit conformance). Result: faster cycles, cheaper variants, calmer audits, and a culture where discipline is speed. In short—choose your level, aim for the impact that matters, and use the three stages to turn AI from hype into habit.
Keywords: orchestration, capability levels, impact tiers, outcome-based pricing, governance, telemetry, agentic systems, enterprise AI
APA
Reynoso, A. (2025). Architect Your AI Edge — A Leader’s Framework for Capability, Impact, and Orchestration (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/paper-architect_ai_finance-v01
BibTeX
@article{reynoso_architect_ai_edge_2025_v01,
author = {Alejandro Reynoso},
title = {Architect Your AI Edge — A Leader’s Framework for Capability, Impact, and Orchestration},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/paper-architect_ai_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-nested_learning_algo_trading-v01
This paper proposes a unified, memory-centric architecture for algorithmic trading that integrates three complementary styles—HFT (fast), Technical (medium), and Fundamental (slow)—under a single constitution of governed adaptation. The portfolio is cast as a society of specialized agents, each stewarding a time-typed memory: fast execution playbooks, regime-sensitive tactical rules, and slow thesis doctrine. A lightweight governance layer—a Reviewer (daily attribution, micro-nudges, shadow trials) and a Committee (adaptive router, breathing risk caps, circuit breakers)—coordinates these memories with explicit decision rights and receipts.
Core claim: memory, not only modeling, makes adaptation explainable, auditable, and safe. Following nested-learning principles, change advances via a simple grammar—shadow → promotion → rollback—with clocks calibrated by style (weekly for HFT, monthly for Technical, quarterly for Fundamental). Daily operations remain reversible and bounded; substantive edits occur on cadence with pre-declared evidence and auto-reversion hooks. The result is a portfolio that adapts to non-stationary markets without fossilizing short-term noise into long-term doctrine and without diluting long-horizon conviction through tactical churn.
Contributions: (i) a principled mapping from nested learning to agent charters and cadences; (ii) a minimalist implementation that emits auditable artifacts (equity curve, agent attribution, router trace, cap timeline, change register); and (iii) an evaluation protocol measuring not only return/drawdown but also quality of change (shadow hit-rate, rollback frequency, time-to-detect degradation). In short, The Memory-Centric Portfolio compounds memory, attention, and governance so the system does not merely react—it learns with receipts.
Keywords: nested learning, time-typed memory, adaptive routing, governance, circuit breakers, attribution, rollback, agentic portfolio
APA
Reynoso, A. (2025). The Memory-Centric Portfolio — A Nested-Learning Architecture for Algorithmic Trading (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-nested_learning_algo_trading-v01
BibTeX
@article{reynoso_memory_centric_portfolio_2025_v01,
author = {Alejandro Reynoso},
title = {The Memory{-}Centric Portfolio — A Nested{-}Learning Architecture for Algorithmic Trading},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-nested_learning_algo_trading-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-nested_algos_for_trading-v01
Markets run on multiple clocks—microseconds (microstructure), hours (intraday), weeks (macro). Single-timescale learners either thrash on noise or lag regime shifts. This paper proposes a nested learning architecture that separates rapid working memory from slower, budgeted consolidation. Fast weights (w) update every step (Adam-style EMAs), acting as short-half-life filters that capture transient edges while compressing gradient history. Slow weights (\theta) update only at cadence boundaries when an endogenous salience gate opens; the gate blends loss improvement, gradient magnitude, and volatility proxies, with smoothing, hysteresis, and an explicit write budget to prevent over-learning.
We formalize a streaming objective (returns, costs, risk penalties); specify an online protocol — infer → log → fast update → salience → gated slow update; and instrument governance artifacts (gate states, effective memory lengths, slow-write receipts, checkpoints) for auditability and safe rollback.
Empirically, we compare three deployments: (1) DNN-Static (pretrained, frozen), (2) DNN-Online (single timescale, updated every step), and (3) Nested (multi-cadence). On regime-flipping synthetic markets with execution frictions, Nested reduces drawdowns and turnover vs. DNN-Online while adapting materially faster than DNN-Static, yielding higher cost-aware IR with fewer structural rewrites. Event-aligned analyses show fast layers managing impact around shocks, while slow layers re-encode context only when evidence persists. Blueprint: react quickly where it’s cheap to be wrong, remember slowly where mistakes are costly, and govern both with measurable, reversible control.
Keywords: continuous learning, nested learning, salience gating, working/long-term memory, online trading, auditability, write budgets, execution frictions
APA
Reynoso, A. (2025). Continuous Learning for Algorithmic Trading — A Nested Multi-Cadence Architecture (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-nested_algos_for_trading-v01
BibTeX
@article{reynoso_continuous_learning_nested_trading_2025_v01,
author = {Alejandro Reynoso},
title = {Continuous Learning for Algorithmic Trading — A Nested Multi{-}Cadence Architecture},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-nested_algos_for_trading-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-boltzmann_inspired_finance-v01
This paper advances Boltzmann-Inspired Finance Machines (BFMs): a compact family of energy-based generative models that learn joint distributions over financial variables by assigning low energy (high probability) to plausible configurations. We develop the statistical-mechanics foundation (energy functions, Boltzmann weights, partition function) and position the Restricted Boltzmann Machine (RBM) as the pedagogical core, trained via Contrastive Divergence and short-run Markov approximations. We relate BFMs to modern descendants—score-based/denoising diffusion and other energy-based formulations—that retain thermodynamic intuition while improving scalability and sample quality.
A Gaussian–Bernoulli example shows how latent units capture recurring patterns and support synthesis, imputation with missing data, and anomaly detection. We then map BFMs to finance: (i) regime-aware nowcasting under asynchronous macro releases; (ii) probabilistic reconstruction of partially observed order books and discrimination between informed flow vs. manipulation; (iii) stress testing via controlled traversals of the energy landscape; and (iv) factor discovery beyond linear-Gaussian dependence. We close with practitioner guidance on preprocessing, model sizing, calibration, and governance (weight inspection, conditional responses, rolling retraining, and validation protocols).
Thesis: BFMs offer a physics-informed baseline for generative inference in finance—useful for data completion, synthetic generation, and risk-aware decision support—while building transferable intuition for contemporary energy-based modeling.
Keywords: energy-based models, Boltzmann machines, RBM, contrastive divergence, score-based models, order-book inference, stress testing, factor discovery
APA
Reynoso, A. (2025). Boltzmann-Inspired Finance Machines (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-boltzmann_inspired_finance-v01
BibTeX
@article{reynoso_boltzmann_inspired_finance_machines_2025_v01,
author = {Alejandro Reynoso},
title = {Boltzmann{-}Inspired Finance Machines},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-boltzmann_inspired_finance-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers_hilbert_black_scholes_practitioners-v01
This paper offers a desk-friendly framework for recognizing when a derivatives model admits Black–Scholes–style analytical structure and how to exploit it—without claiming new functional analysis. In a risk-neutral setting, option pricing is treated as a linear pricing semigroup acting on a Hilbert space of payoffs. We define Hilbert–Black–Scholes (HBS) models as those whose discounted pricing generator is diagonalizable on a natural payoff subspace, yielding spectral representations (Fourier, exponential–affine, polynomial, Sturm–Liouville, Schrödinger).
Contributions are operational:
- an eligibility algorithm that tests an SDE/generator for membership in known HBS families and selects the right spectral geometry;
- a round-trip recipe from SDE → generator → Hilbert space/transform → prices, Greeks, and P&L explain;
- case studies (bounded mean-reversion, Jacobi/CIR diffusions, Lévy/Heston, polynomial factor models) showing how familiar formulas fit a single spectral picture.
Outcome: faster recognition of closed-form structure, systematic use of spectral methods when applicable, and clearer justification for numerics when not.
Keywords: spectral pricing, semigroups, diagonalization, Fourier/affine/polynomial methods, Sturm–Liouville, Schrödinger, Greeks, P&L explain
B998APA
Reynoso, A. (2025). Hilbert–Black–Scholes for Practitioners — A Spectral Playbook (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers_hilbert_black_scholes_practitioners-v01
BibTeX
@article{reynoso_hilbert_black_scholes_practitioners_2025_v01,
author = {Alejandro Reynoso},
title = {Hilbert{\textendash}Black{\textendash}Scholes for Practitioners {\textemdash} A Spectral Playbook},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers_hilbert_black_scholes_practitioners-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-blotzmann_hilbert_probability_finance-v01
This essay advances a unifying, three-language view of quantitative finance—energy, probability, and Hilbert geometry—and shows how deliberately switching among them clarifies practice. Finance “lives” in probability (risk-neutral measures, loss distributions, moments), yet tails, regime change, and portfolio-set shape are often compressed into thin summaries. From statistical mechanics, we treat an energy field as a cost/(minus-utility) landscape and use the Boltzmann map to turn energy into probability; temperature governs exploration vs. concentration and entropy measures spread across configurations. From quantum/Hilbert methods, we embed distributions as vectors, use inner products to compare strategies/risk profiles, and view pricing as linear operators with helpful spectral structure.
The contribution is organizational, not a new asset-pricing model: a circular framework
Energy ↔ Probability ↔ Hilbert Geometry,
where (under mild conditions) representations translate back and forth (up to constants/phases). Like moving between time and frequency domains, we migrate to the domain where a question is transparent, solve, then return to probability to report results. Three worked examples—portfolio selection, risk management, and derivatives valuation—make the material practical for students and practitioners, encouraging a polyglot toolkit for everyday quantitative work.
Keywords: Boltzmann map, entropy/temperature, energy-based views, Hilbert space, spectral pricing, portfolio/risk/derivatives pedagogy
APA
Reynoso, A. (2025). Energy Fields, Probability, and Finance — A Pedagogical Bridge (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-blotzmann_hilbert_probability_finance-v01
BibTeX
@article{reynoso_energy_probability_finance_2025_v01,
author = {Alejandro Reynoso},
title = {Energy Fields, Probability, and Finance — A Pedagogical Bridge},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-blotzmann_hilbert_probability_finance-v01}
}
64) A Bellman Equation with Multiple Personality Syndrome — Bellman, Boltzmann, and Hamiltonian Views for Finance
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-bellman_boltzman_hilbert-v01
This pedagogical paper organizes familiar control concepts into a practitioner-ready triad of viewpoints for the same decision problem:
- Bellman personality: dynamic programming and value functions for finite-horizon MDPs.
- Boltzmann personality: energy/entropy-regularized objectives and free-energy functionals.
- Hamiltonian personality: continuous-time HJB equations and Hamiltonian operators.
Using an optimal execution case study, the paper walks through the identical problem under each personality—solving it as a Bellman recursion, as an energy-based control with Boltzmann policies, and as an HJB with an associated Hamiltonian—then shows how modern ML tools (policy gradient, entropy-regularized RL, energy-based models, variational/quantum-inspired architectures) fit naturally into the triad. The discussion also sketches extensions to Hilbert-space formulations and quantum-inspired RL without requiring prior quantum background.
Targeted at finance professionals and applied researchers, the contribution is a dictionary that lets readers translate between Bellman, energy, and Hamiltonian perspectives, recognize when they yield the same policy, and deploy standard ML algorithms in ways that respect existing risk, pricing, and governance frameworks.
Keywords: dynamic programming, entropy-regularized RL, free energy, HJB, Hamiltonian operators, optimal execution, finance governance, Hilbert/quantum-inspired RL
APA
Reynoso, A. (2025). A Bellman Equation with Multiple Personality Syndrome — Bellman, Boltzmann, and Hamiltonian Views for Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-bellman_boltzman_hilbert-v01
BibTeX
@article{reynoso_bellman_boltzmann_hamiltonian_2025_v01,
author = {Alejandro Reynoso},
title = {A Bellman Equation with Multiple Personality Syndrome — Bellman, Boltzmann, and Hamiltonian Views for Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-bellman_boltzman_hilbert-v01}
}
65) Agatha Christie Does Reinforcement Learning — Bellman, Boltzmann, and Hilbert as a No-Math Framework
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-agatha_christie_reinforcement_learning-v01
This essay presents a theoretical bridge between reinforcement learning (RL) and the craft of mystery fiction, using Agatha Christie’s planning style to ground core RL ideas. Three complementary “personalities” structure the framework:
- Bellman the Planner: backward reasoning and dynamic programming quantify the long-term value of plot decisions (value functions/policies).
- Boltzmann the Explorer: the space of plots is an energy landscape; temperature mediates the exploration–exploitation trade-off via entropy-regularized (soft-optimal) objectives.
- Hilbert the Geometer: possible novels live as vectors in a high-dimensional space, enabling geometric reasoning over outcomes and similarities.
Together, they provide a no-math mental model for policies, value functions, and exploration, and show these views are mathematically related—differing mainly in hard optimality (Bellman), soft optimality (Boltzmann), or a geometric representation (Hilbert). The narrative also sketches applications to financial decision-making and portfolio design.
Keywords: reinforcement learning, dynamic programming, entropy regularization, energy landscape, Hilbert space, exploration–exploitation, decision-making, finance
APA
Reynoso, A. (2025). Agatha Christie Does Reinforcement Learning — Bellman, Boltzmann, and Hilbert as a No-Math Framework (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-agatha_christie_reinforcement_learning-v01
BibTeX
@article{reynoso_agatha_christie_rl_2025_v01,
author = {Alejandro Reynoso},
title = {Agatha Christie Does Reinforcement Learning — Bellman, Boltzmann, and Hilbert as a No{-}Math Framework},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-agatha_christie_reinforcement_learning-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-engineering_mckinsey_agent-v01
This paper specifies an Endogenous Molecular Intelligence (EMI) system—a “McKinsey agent” that constructs strategies from first principles rather than emitting plausible prose. We argue next-token prediction is structurally insufficient for high-stakes finance and propose a four-layer hybrid architecture:
- Periodic Table of Strategy: a finite alphabet of reasoning atoms across finance/strategy/ops/governance. Molecules (ordered atom sequences) obey valency rules (e.g., diagnose→treat; measure liquidity→allocate capital).
- Two-Tower Embeddings: a problem tower (free-form cases) and a molecule tower (atom sequences) learned with contrastive objectives to create semantic gravity between matched problem–solution pairs.
- MARL Engine: heterogeneous agents (greedy, Boltzmann, balanced) navigate a value manifold over the periodic table, sharing trajectories via cooperative memory to discover high-reward molecules.
- Constrained Generation: at inference, an LLM verbalizes a selected molecule into a board-ready narrative without altering logic.
An accompanying Colab notebook operationalizes the design for MFin/MBA audiences—providing an auditable, pedagogical sandbox for neuro-symbolic strategy engines.
Keywords: reasoning atoms, neuro-symbolic, contrastive two-tower, MARL, value manifold, constrained generation, governance
APA
Reynoso, A. (2025). Engineering the McKinsey Agent — Endogenous Molecular Intelligence for Finance (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-engineering_mckinsey_agent-v01
BibTeX
@article{reynoso_engineering_mckinsey_agent_2025_v01,
author = {Alejandro Reynoso},
title = {Engineering the McKinsey Agent — Endogenous Molecular Intelligence for Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-engineering_mckinsey_agent-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-einsgtein_markowitz-v01
This paper imagines Albert Einstein and Harry Markowitz as joint architects of a financial spacetime where expectations, risks, and portfolios acquire relativistic geometry. Payoffs are modeled as four-vectors: a time-like component for economic value and space-like components for orthogonal risk exposures. A Minkowski-type metric, with a market-wide “financial speed of light” linking value and risk units, defines an invariant quadratic form—a financial interval—shared by admissible investors.
Investors are Lorentz-type frames: beliefs, factor bases, and risk appetites differ, but coordinate changes preserve the metric. Portfolios classify into time-like, null, and space-like positions, cleanly separating frame-dependent expectations from frame-independent invariants. A toy CAPM is recast geometrically: asset four-vectors project onto the market via a Minkowski inner product; beta becomes a geometric alignment; the security market line is a frame-invariant relation between time-like components and their projections.
Heterogeneous risk aversion corresponds to different frames in a flat financial spacetime; state-dependent trade-offs motivate a curved (general-relativistic) extension where the metric varies with wealth, conditions, or endogenous risk. The contribution is conceptual: relativity supplies a disciplined language that separates subjective coordinates (views, appetites) from a common equilibrium geometry (state prices, factor structure, no-arbitrage), showing how many portfolios can coexist within a single invariant financial spacetime.
Keywords: Minkowski finance, financial spacetime, Lorentz frames, CAPM geometry, beta as alignment, general-relativistic extensions
APA
Reynoso, A. (2025). Relativistic Finance. Paper 1 of 6 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-einsgtein_markowitz-v01
BibTeX
@article{reynoso_relativistic_finance_ch0_2025_v01,
author = {Alejandro Reynoso},
title = {Paper 1. Relativistic Finance},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-einsgtein_markowitz-v01}
}
68) Relativistic Finance: Paper 2 of 6. A General-Relativity Approach to Fragmented Markets and Optimal Arbitrage
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-general_relativity_fragmented_markets-v01
We develop a relativistic geometric framework for fragmented financial markets, dispersion, and arbitrage. The financial system is modeled as a market manifold of locations—jurisdictions, balance sheets, funding channels, institutional sectors—with a state-dependent metric encoding risk, funding, capital, and liquidity. Assets are invariant “financial masses” (payoff identities); prices arise as Lagrange multipliers that clear markets and eliminate profitable geodesic loops (buy–transport–sell cycles) in curved market spacetime. This yields a clean separation of (i) invariants (contracts, no-arbitrage constraints), (ii) geometry (structure/fragmentation), and (iii) equilibrium labels (prices, premia, flows).
Conceptually rigorous and operationally clear, the framework: (1) characterizes dispersed yet unbroken markets—price differences across locations that remain geodesically consistent with transport costs/constraints; (2) formalizes optimal arbitrage as profitable loop search, with law-of-one-price emerging as the multiplier configuration that kills such loops; and (3) incorporates heterogeneous expectations as different frames on a common geometry, separating sentiment premia from curvature/structure premia.
Intended as a foundation for monetary policy, development economics, and financial theory, the approach provides a disciplined representation of segmented credit markets, cross-border funding channels, and regulatory regimes, and a way to study how policy interventions deform the market metric—supporting systematic analysis of financial integration, policy transmission, and equilibrium pricing in complex, multi-layered systems.
Keywords: market geometry, fragmentation, geodesics, arbitrage loops, law of one price, curvature premia, policy transmission
APA
Reynoso, A. (2025). A General-Relativity Approach to Fragmented Markets and Optimal Arbitrage. Paper 2 of 6 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-general_relativity_fragmented_markets-v01
BibTeX
@article{reynoso_gr_fragmented_markets_2025_v01,
author = {Alejandro Reynoso},
title = {A General{-}Relativity Approach to Fragmented Markets and Optimal Arbitrage. Paper 2 of 6},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-general_relativity_fragmented_markets-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-layered_traders_relativistic-v01
This third essay in a six-paper series extends a relativistic geometry of markets to explain how heterogeneous trading styles can coexist on a single market manifold without fragmenting into separate
5228
“micro” and “macro” markets. Papers I–II established (i) portfolios/payoffs as four-vectors in a Minkowski-type space with a common financial metric and (ii) a curved market manifold capturing frictions and segmentation. Here, we retain one payoff universe and one underlying manifold and study three archetypes—high-frequency, technical, and fundamental traders.
All transact in the same contract identities (“financial masses”) and are disciplined by a single price field consistent with no-arbitrage on the manifold. What differs are the effective metrics each style experiences—local cost-of-carry structures shaped by horizons, inventory/risk limits, leverage technology, microstructure access, and regulation. The market’s global geometry rationalizes observed prices and rules out geodesic arbitrage loops once all frictions and trading technologies are accounted for.
Two messages follow. First, heterogeneity does not require multiple incompatible equilibria: HFT, technical, and fundamental communities represent different geodesics through the same spacetime, sharing the law of one price and a single mass concept. Second, the presence of one mass and one price across layers imposes inverse structure: prices, order flow, and the survival/exit of strategies reveal aspects of both the global metric and style-specific effective metrics—an analogue of revealed geometry at the market level.
The chapter is conceptual, providing a clean geometric language for microstructure, time-scale separation, and fast–slow interactions, and it bridges the geometric foundations of Papers I–II with the learning/generative constructions of Papers IV–V. Stylized examples and a research agenda sketch how to embed concrete models of HFT market making, technical trading, and fundamental investing into a single relativistic equilibrium.
Keywords: market manifold, effective metrics, layered trading styles, geodesics, no-arbitrage, revealed geometry, time-scale separation
APA
Reynoso, A. (2025). Relativistic Finance: Layered Traders on a Common Market Manifold. Paper 3 of 6 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-layered_traders_relativistic-v01
BibTeX
@article{reynoso_relativistic_finance_ch3_2025_v01,
author = {Alejandro Reynoso},
title = {Paper 3.{\textemdash} Relativistic Finance: Layered Traders on a Common Market Manifold},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-layered_traders_relativistic-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-price_shocks_relativistic_envioronments-v01
This chapter extends the relativistic geometry of finance to the algorithmic era. Keeping the common backbone from Papers I–III—contracts as invariant “financial masses,” trading environments as points on a market manifold, and frictions as a financial metric enforcing a geodesic law-of-one-price—we organize heterogeneous algorithmic traders into clusters that share one spacetime but experience different effective geometries.
A cluster is defined by: (i) an effective metric (g^{(c)}) capturing cost-of-carry, constraints, and technological capabilities; and (ii) a frame (\Lambda^{(c)}) (Lorentz-type) encoding expectations, risk appetite, and time preference. HFT market makers, intraday stat-arb engines, trend followers, and long-horizon fundamental algorithms arise as particular (\big(g^{(c)}, \Lambda^{(c)}\big)) on the same underlying manifold, under a single global metric (g^*) implied by no-arbitrage. Thus, the space of trading styles becomes a structured subset of metrics + frames, not a grab-bag of unrelated models.
We propose a relativistic decomposition of shocks: demand shocks as frame shifts (belief/risk tilts that rotate time-like projections of value/risk without changing costs), and supply shocks as metric/composition shocks (deformations of (g^*) and cluster metrics, and capacity changes due to funding, regulation, liquidity, or technology). For each financial mass (m), a common price field (P_m(t,x)) lives on the manifold and is constrained by a geodesic law-of-one-price band set by (g^*).
The contribution is a structural sensitivity analysis of equilibrium price fields to frame (expectation) changes and metric (cost/capacity) perturbations, clarifying how small shifts propagate across fast and slow money. The result is a unified geometry-based language for layered algorithmic trading, market impact, and the interaction of demand- vs. supply-driven components of price moves under shocks—bridging geometric foundations and future empirical/simulation work on fragmented yet integrated electronic markets sharing one spacetime.
Keywords: market manifold, effective metrics, frames, layered algorithms, geodesic law-of-one-price, demand vs. supply shocks, sensitivity analysis
APA
Reynoso, A. (2025). Relativistic Finance: Price Shocks in Layered Algorithmic Markets. Paper 4 of 6 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-price_shocks_relativistic_envioronments-v01
BibTeX
@article{reynoso_relativistic_finance_ch4_2025_v01,
author = {Alejandro Reynoso},
title = {Paper 4. {\textemdash} Relativistic Finance: Price Shocks in Layered Algorithmic Markets},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-price_shocks_relativistic_envioronments-v01}
}
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-policy_gravitation_relativistic-v01
We develop a geometric framework for financial regulation, monetary policy, and development economics on a market spacetime. Assets are invariant “financial masses,” locations are points on a manifold, and costs/frictions define a metric whose curvature captures systematic variation in the cost of transporting financial mass across locations—steep regions (constrained, illiquid, segmented) versus flat regions (integrated, low-friction).
We distinguish good curvature (risk buffers, macroprudential firebreaks, institutional safeguards) from bad curvature (avoidable frictions, historical accidents, exclusion). This yields a positive and normative theory of policy as geometry: policy levers—capital requirements, taxes, capital controls, infrastructure—enter as parameters of the metric, with effects inferred empirically from prices, bases, and flows via latent-manifold and metric-learning techniques.
In this view, the law of one price is a geodesic constraint; arbitrage corresponds to violations of geometric consistency; and interventions are evaluated by their curvature impact in targeted regions. The framework unifies fragmented markets, financial inclusion, and cross-border flows, and offers a principled way to decide when to flatten the manifold (remove unnecessary curvature) versus when to preserve or create curvature (contain systemic risk). We close with a research agenda linking market geometry to regulation, monetary policy, and empirical development finance.
Keywords: market spacetime, curvature, policy as geometry, law of one price, metric learning, financial inclusion, capital flows
APA
Reynoso, A. (2025). Relativistic Finance: Policy, Curvature, and Market Geometry. Paper 5 of 6 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-policy_gravitation_relativistic-v01
BibTeX
@article{reynoso_relativistic_finance_ch5_2025_v01,
author = {Alejandro Reynoso},
title = {Paper 5 {\textemdash} Relativistic Finance: Policy, Curvature, and Market Geometry},
year = {2025},
version = {v01},
publisher = {GitHub},
url = {https://github.com/alexdibol/papers/releases/tag/papers-policy_gravitation_relativistic-v01}
}
72) Relativistic Finance: Paper 6 of 6. Broken Markets and the Dynamics of Relativistic Re-composition
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-super_geodesics_general_relativity_fionance-v01
Modern finance already thinks geometrically—risk, diversification, and pricing live in a structured space—yet most formulations assume one underlying market geometry. This chapter relaxes that assumption. We model a common payoff universe over a market manifold but allow segment-specific metrics (jurisdictions, balance-sheet regimes, microstructures), formalize broken geometry when no single global metric can rationalize observed costs, and then lift the metrics themselves to a higher-dimensional configuration space. There we define an error functional (law-of-one-price violations + incompatibilities on overlaps) and derive meta-geodesic / gradient-flow dynamics showing how arbitrage, policy, and technology push segments toward or away from coherence. The framework explains persistent cross-segment bases, regulatory/institutional arbitrage, technological discontinuities (e.g., DeFi vs. banks), and crisis-time discontinuities as motions in the space of geometries. A worked two-location/two-metric toy model illustrates identification, reconciliation, and conditions for persistent fragmentation. The result is a scaffold for empirical work and policy design in fragmented but coupled financial systems.
Keywords: broken geometry, segment metrics, law of one price, meta-geodesics, fragmentation, arbitrage coupling, manifold learning, financial spacetime
APA
Reynoso, A. (2025). Relativistic Finance. Super-Geodesics & Broken Geometry. Paper 6 of 6 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-super_geodesics_general_relativity_fionance-v01
BibTeX @article{reynoso_relativistic_finance_ch6_2025_v01, author = {Alejandro Reynoso}, title = {Relativistic Finance — Paper 6: Super{-}Geodesics & Broken Geometry}, year = {2025}, version = {v01}, publisher = {GitHub}, url = {https://github.com/alexdibol/papers/releases/tag/papers-super_geodesics_general_relativity_fionance-v01} }
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-empirical_relativistic_finance-v01
This chapter brings the relativistic geometry of finance into direct contact with data. We treat the financial speed of light (c) as a structural bound on risk-adjusted returns and estimate it from realized Sharpe ratios using Extreme Value Theory (block maxima / GEV), yielding an empirical upper endpoint for performance. In parallel, we reinterpret persistent deviations from Covered Interest Parity (CIP) as geodesic deficits on a global funding manifold—differences in action between direct and synthetic USD–EUR funding paths. Regressions on proxies for regulatory capital intensity, repo stress, and FX volatility identify a ridge of elevated curvature tied to balance-sheet constraints and Atlantic asymmetries. Together, these results provide a measurement toolkit—(\hat c), curvature, and structural volatility—that reframes “anomalies” as geometric features of a curved financial spacetime, designed to feed manifold-aware dashboards and, in future work, the losses and constraints of generative and agentic portfolio systems.
Keywords: financial spacetime, extreme value theory, Sharpe upper bound, CIP basis, geodesic deficit, funding curvature, manifold learning
APA
Reynoso, A. (2025). Extensions to the Study of Relativistic Finance: Empirical Geometry of Markets. Paper 1 of 3 (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-empirical_relativistic_finance-v01
BibTeX @article{reynoso_relativistic_finance_ch7_2025_v01, author = {Alejandro Reynoso}, title = {Extensions of Relativistic Finance: Part 1. Empirical Geometry of Markets}, year = {2025}, version = {v01}, publisher = {GitHub}, url = {https://github.com/alexdibol/papers/releases/tag/papers-empirical_relativistic_finance-v01} }
74) Extensions to the Study of Relativistic Finance: Paper 2 of 3 Generative, Geometry-Aware Portfolio Construction
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-generative_algo_trading_relativity-v01
This chapter proposes a geometric architecture for context-aware portfolio generation that integrates special-relativity analogies, manifold learning, and modern generative models. Markets are represented as a learned manifold with a metric: financial contracts act as invariant “masses,” locations encode funding and institutional regimes, and frictions define geodesic distances. Free-form investor and market context is embedded into a triplet ((z, g, \Lambda)): a manifold location (z), a local metric (g) capturing implementation cost and risk geometry, and a Lorentz-type frame (\Lambda) encoding risk appetite, horizon, and leverage tolerance. A curated library of strategy atoms (factor tilts, overlays, hedges) is composed into portfolio molecules by a generative or search engine that optimizes utility under ((z, g, \Lambda)).
The design yields three advantages over black-box recommenders: (1) Geodesic law-of-one-price enforces structural no-arbitrage and cost consistency; (2) a clear separation of environmental geometry (market structure, regulation, frictions) from subjective frames (beliefs and preferences); and (3) end-to-end traceability—every recommendation is a finite combination of named atoms at a precise location in market spacetime, with an explainable metric and frame. The result is a generative advisor that is powerful, auditable, and governance-ready for institutional portfolio construction.
Keywords: market manifold, geodesic no-arbitrage, strategy atoms/molecules, context embeddings, generative advisors, governance
APA
Reynoso, A. (2025). Extensions to the Study of Relativistic Finance: Paper 2 of 3. Generative, Geometry-Aware Portfolio Construction (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-generative_algo_trading_relativity-v01
BibTeX @article{reynoso_relativistic_finance_ch10_2025_v01, author = {Alejandro Reynoso}, title = {Extensions of Relativistic Finance: Part 2. Generative, Geometry-Aware Portfolio Construction}, year = {2025}, version = {v01}, publisher = {GitHub}, url = {https://github.com/alexdibol/papers/releases/tag/papers-generative_algo_trading_relativity-v01} }
Author: Alejandro Reynoso
Version: v01 · Release: https://github.com/alexdibol/papers/releases/tag/papers-relativistic_machine_learning_finance-v01
We develop a machine-learning framework that treats financial markets as a latent spacetime to be learned from data rather than postulated a priori. Extending the relativistic analogy (portfolios as four-vectors), the cross-section of contracts, venues, and trader types is mapped to a shared market manifold with an unknown metric. A location encoder sends observable attributes (jurisdiction, venue, horizon, funding regime) to latent coordinates; a parametric metric induces geodesic distances trained to rationalize prices, flows, and funding costs.
To separate structure from psychology, a two-level head is used: (i) a shared geometry head yields baseline prices over contract identity and latent location (the common spacetime), and (ii) cluster-specific frame heads apply Lorentz-like tilts that encode beliefs, horizons, and risk appetite for strategy/counterparty clusters. Regularization enforces disentanglement, pushing persistent structure into geometry and leaving only transient deviations in frames.
The geodesic law-of-one-price is embedded as an inductive bias: cross-location price gaps are penalized when they exceed learned geodesic distances plus observed cost bands. We outline reconstruction, geometry, and frame losses; discuss identifiability; and design synthetic experiments to test recovery of the true metric and frames. An empirical roadmap targets fragmented equity, fixed-income, and FX markets, plus algorithmic trading and no-arbitrage diagnostics built on learned market spacetimes.
Keywords: latent market manifold, parametric metric learning, trader frames, geodesic no-arbitrage, disentanglement, fragmented markets
APA
Reynoso, A. (2025). Extensions to the Study of Relativistic Finance: Paper 3 of 3 Machine-Learned Market Spacetime (Version v01). GitHub. https://github.com/alexdibol/papers/releases/tag/papers-relativistic_machine_learning_finance-v01
BibTeX @article{reynoso_relativistic_finance_ch8_2025_v01, author = {Alejandro Reynoso}, title = {Extensions of Relativistic Finance:Paper 3 of 3. Machine{-}Learned Market Spacetime}, year = {2025}, version = {v01}, publisher = {GitHub}, url = {https://github.com/alexdibol/papers/releases/tag/papers-relativistic_machine_learning_finance-v01} }
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APA
Reynoso, A. (2025). Selected Papers — AI and Quantum-Inspired Finance (Version v01). GitHub. https://github.com/alexdibol/papers
BibTeX
@misc{reynoso_selected_papers_2025,
author = {Alejandro Reynoso},
title = {Selected Papers — AI and Quantum-Inspired Finance},
year = {2025},
howpublished = {\url{https://github.com/alexdibol/papers}},
note = {Version v01}
}