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 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://github.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, latency-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}
}
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