AI Reasoning
AI Reasoning is a governance-first laboratory for designing and reviewing reasoning systems as controlled inference mechanisms—rather than persuasive chat. In professional finance and institutional settings, “reasoning” can be the most dangerous feature: coherent explanations can conceal unsupported leaps, silent assumptions can creep into decisions, and clean narratives can bury uncertainty behind fluent structure.
This repository rejects the “trust the model” posture. It treats reasoning as an engineering discipline: explicit state, explicit routing, explicit constraints, explicit termination, explicit artifacts. The aim is not to create impressive prose. The aim is to create reviewable decision machinery, so an independent reviewer can reconstruct what happened, why it happened, what was known, what was assumed, what remains unverified, and what required escalation.
Each notebook implements governed reasoning patterns—chains, trees, loops, committees— and forces them to behave like contracts: bounded behavior, stage gates, and mandatory audit bundles. Outputs are deliberately marked Not verified unless and until qualified humans validate them. That is not a disclaimer taped on at the end; it is the system’s operating principle.
If a system “works” only when you ignore gates, constraints, termination logic, or evidence rules, then it does not work.
Notebook path convention: /notebooks/CHAPTER%20N.ipynb (GitHub URL-encoded spacing).
The Book
The book is the governing specification. It defines what “good” means in this repository: not eloquence, not confidence, not “helpfulness,” but controlled inference under professional accountability. The notebooks are the operational proofs: executable laboratories that implement the mechanisms and enforce the acceptance gates described in the book.
AI Reasoning Book (PDF)
Definitions, reasoning shapes, failure modes, governance controls, and acceptance gates. The emphasis is traceability: how to prevent silent assumption creep, scope drift, and persuasive overreach; how to force explicit termination; and how to produce artifacts that can be reviewed independent of the model’s narrative.
Operating posture: outputs are mechanisms to be inspected—not truths to be trusted.
Read Book (PDF)Governed Notebooks (Reasoning Patterns as Controllers)
The notebooks are not “examples.” They are controlled reasoning systems that must pass evidence discipline and stage gates. Each chapter notebook operationalizes a reasoning pattern and produces artifacts that make the run reconstructable: what the system believed, what it assumed, what remained open, and why the process stopped.
Chapter 1 — Why Reasoning Needs Governance
Establishes the central hazard: reasoning can look like verification. The notebook forces an explicit separation of facts_provided, assumptions, and open_items, and adopts a conservative refusal posture when evidence is missing. The goal is not “more output” but safer termination and clean escalation.
Open NotebookChapter 2 — Chains: Stepwise Reasoning as a Contract
Implements chain reasoning with explicit intermediate state and dependency tracking. Each step must bind to declared inputs, and unsupported leaps are treated as control failures. “Stop-if” rules and structured outputs prevent narrative smoothing from hiding uncertainty.
Open NotebookChapter 3 — Trees: Branching Scenarios and Decision Topologies
Treats branching as a governed operation: when to branch, how many branches, how to prune, and how to keep assumptions isolated across scenarios. The deliverable is a decision topology a reviewer can inspect: explored paths, pruned paths, and the criteria used.
Open NotebookChapter 4 — Loops: Iterative Refinement With Convergence Discipline
Makes iteration safe: bounded retries, explicit improvement targets, and delta-logging between iterations. If open items cannot be closed, the system escalates rather than polishing prose. Termination reasons become a first-class artifact—not an afterthought.
Open NotebookChapter 5 — Committees: Multi-Perspective Deliberation With Dissent Preservation
Implements role-based deliberation with mandates and dissent preservation. The goal is not consensus-by-averaging, but governed synthesis: what each role accepted, rejected, or escalated, and why. The final output is board-facing while remaining artifact-traceable and explicit about what is unverified.
Open NotebookWhat Every Run Produces (Minimum Artifact Standard)
Every notebook is designed to be reviewable by artifact inspection. At minimum, each run produces a reproducibility and governance bundle that enables independent reconstruction.
Run Manifest
run_manifest.json captures seed/config, versions, and an environment fingerprint
so results can be reproduced and compared without relying on memory or narrative.
The manifest is the “receipt.” If you cannot reproduce the run, you cannot trust the conclusion.
Final State + Decision
final_state.json records what the system believed at termination.
decision.json records advance / revise / reject with explicit reasons.
Decisions must be explainable by artifacts—not by post-hoc rationalization.
Risk Log + Deliverables
risk_log.json records triggered risks and applied controls.
artifacts/ contains any generated reports, tables, or exhibits.
When evidence is missing, the correct deliverable is a verification task list, not persuasive prose.
Shared Governance Spine
Generation ≠ Verification
Outputs are marked Not verified unless a qualified human validates them. The system is designed to preserve uncertainty instead of hiding it.
See Governance SpecFacts vs Assumptions vs Open Items
Every governed output separates supported inputs from declared assumptions and open questions. Silent assumption creep is treated as a failure mode.
See Chapter 1 LabStop is a Feature
Termination is explicit. Refusal and escalation are correct outcomes when evidence is insufficient, scope is violated, or gates fail.
See Loop ControlsLicensing, Governance & AI Use Disclosure
Educational / Non-Reliance: All materials are provided for educational and research purposes only. Nothing in this repository constitutes investment, trading, legal, tax, accounting, audit, or compliance advice.
Not verified: Unless explicitly stated otherwise in a specific artifact, treat all outputs, claims, calculations, citations, and conclusions as Not verified.
Confidentiality and data hygiene: Do not paste confidential, proprietary, regulated, or personally identifying information into external systems. Use anonymization/redaction and minimum-necessary inputs by default.
No fabricated sources or claims: Zero tolerance for invented citations, performance claims, fees, terms, or consequences. When evidence is missing, the correct output is a verification task list, not persuasive narrative.
License: This project is released under the MIT License. Preserve copyright and license notices.
Copyright (c) 2026 Alejandro Reynoso