# Reasoning Systems as Structured Processes: Foundations, Failures, and Formal Criteria

arXiv: https://arxiv.org/abs/2508.01763 · HTML: https://mlsanity.com/paper/2508.01763/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 34 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 24 of 24)

- [Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought](https://mlsanity.com/paper/2210.01240/) (2022)
- [Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations](https://mlsanity.com/paper/2205.11822/) (2022)
- [Multi-Step Reasoning with Large Language Models, a Survey](https://mlsanity.com/paper/2407.11511/) (2024)
- [Faithful Reasoning Using Large Language Models](https://mlsanity.com/paper/2208.14271/) (2022)
- [Deductive Verification of Chain-of-Thought Reasoning](https://mlsanity.com/paper/2306.03872/) (2023)
- [Self-Discover: Large Language Models Self-Compose Reasoning Structures](https://mlsanity.com/paper/2402.03620/) (2024)
- [Towards System 2 Reasoning in LLMs: Learning How to Think With Meta Chain-of-Thought](https://mlsanity.com/paper/2501.04682/) (2025)
- [Question Decomposition Improves the Faithfulness of Model-Generated Reasoning](https://mlsanity.com/paper/2307.11768/) (2023)
- [Why think step by step? Reasoning emerges from the locality of experience](https://mlsanity.com/paper/2304.03843/) (2023)
- [Phenomenal Yet Puzzling: Testing Inductive Reasoning Capabilities of Language Models with Hypothesis Refinement](https://mlsanity.com/paper/2310.08559/) (2023)
- [Grokked Transformers are Implicit Reasoners: A Mechanistic Journey to the Edge of Generalization](https://mlsanity.com/paper/2405.15071/) (2024)
- [ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://mlsanity.com/paper/2304.10703/) (2023)
- [Boosting Language Models Reasoning with Chain-of-Knowledge Prompting](https://mlsanity.com/paper/2306.06427/) (2023)
- [Meta-Reasoner: Dynamic Guidance for Optimized Inference-time Reasoning in Large Language Models](https://mlsanity.com/paper/2502.19918/) (2025)
- [Pangu-Agent: A Fine-Tunable Generalist Agent with Structured Reasoning](https://mlsanity.com/paper/2312.14878/) (2023)
- [SCREWS: A Modular Framework for Reasoning with Revisions](https://mlsanity.com/paper/2309.13075/) (2023)
- [On the Diagram of Thought](https://mlsanity.com/paper/2409.10038/) (2024)
- [Instantiation-based Formalization of Logical Reasoning Tasks using Language Models and Logical Solvers](https://mlsanity.com/paper/2501.16961/) (2025)
- [A General Framework for Modelling Conditional Reasoning -- Preliminary Report](https://mlsanity.com/paper/2202.07596/) (2022)
- [On Interactive Explanations as Non-Monotonic Reasoning](https://mlsanity.com/paper/2208.00316/) (2022)
- [Disentangling Reasoning Capabilities from Language Models with Compositional Reasoning Transformers](https://mlsanity.com/paper/2210.11265/) (2022)
- [Dialogue-based Explanations for Logical Reasoning using Structured Argumentation](https://mlsanity.com/paper/2502.11291/) (2025)
- [Dynamic MOdularized Reasoning for Compositional Structured Explanation Generation](https://mlsanity.com/paper/2309.07624/) (2023)
- [Learning Representations for Reasoning: Generalizing Across Diverse Structures](https://mlsanity.com/paper/2410.13018/) (2024)

## Built on by (most cited first, 1 of 1)

- [Belief Graphs with Reasoning Zones: Structure, Dynamics, and Epistemic Activation](https://mlsanity.com/paper/2510.10042/) (2025)

## Abstract (arXiv)

This paper outlines a general formal framework for reasoning systems, intended to support future analysis of inference architectures across domains. We model reasoning systems as structured tuples comprising phenomena, explanation space, inference and generation maps, and a principle base. The formulation accommodates logical, algorithmic, and learning-based reasoning processes within a unified structural schema, while remaining agnostic to any specific reasoning algorithm or logic system. We survey basic internal criteria--including coherence, soundness, and completeness-and catalog typical failure modes such as contradiction, incompleteness, and non-convergence. The framework also admits dynamic behaviors like iterative refinement and principle evolution. The goal of this work is to establish a foundational structure for representing and comparing reasoning systems, particularly in contexts where internal failure, adaptation, or fragmentation may arise. No specific solution architecture is proposed; instead, we aim to support future theoretical and practical investigations into reasoning under structural constraint.