Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping

• URL: http://arxiv.org/abs/2603.06923v1
• Date: Fri, 06 Mar 2026 22:51:39 GMT
• Title: Reforming the Mechanism: Editing Reasoning Patterns in LLMs with Circuit Reshaping
• Abstract summary: Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability.<n>Existing approaches to improving reasoning typically treat it as a general and monolithic skill, applying broad training which is inefficient and unable to target specific reasoning errors.<n>We introduce Reasoning Editing, a paradigm for selectively modifying specific reasoning patterns in LLMs while preserving other reasoning pathways.
• Score: 51.33479921152361
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large language models (LLMs) often exhibit flawed reasoning ability that undermines reliability. Existing approaches to improving reasoning typically treat it as a general and monolithic skill, applying broad training which is inefficient and unable to target specific reasoning errors. We introduce Reasoning Editing, a paradigm for selectively modifying specific reasoning patterns in LLMs while preserving other reasoning pathways. This task presents a fundamental trade-off between Generality, the ability of an edit to generalize across different tasks sharing the same reasoning pattern, and Locality, the ability to preserve other reasoning capabilities. Through systematic investigation, we uncover the Circuit-Interference Law: Edit interference between reasoning patterns is proportional to the overlap of their neural circuits. Guided by this principle, we propose REdit, the first framework to actively reshape neural circuits before editing, thereby modulating interference between reasoning patterns and mitigating the trade-off. REdit integrates three components: (i) Contrastive Circuit Reshaping, which directly addresses the generality-locality trade-off by disentangling overlapping circuits; (ii) Meta-Contrastive Learning, which extends transferability to novel reasoning patterns; and (iii) Dual-Level Protection, which preserves preexisting abilities by constraining reshaping update directions and regularizing task-level predictions. Extensive experiments with Qwen-2.5-3B on propositional logic reasoning tasks across three difficulty levels demonstrate that REdit consistently achieves superior generality and locality compared to baselines, with additional validation in mathematics showing broader potential. Our code is available at https://github.com/LzyFischer/REdit.

Related papers

• Learning to Refine Hidden States for Reliable LLM Reasoning [0.21485350418225244]
  Large language models show strong reasoning ability, but their internal reasoning process can remain unstable in complex multi-step settings.<n>We propose ReLAR, a reinforcement-guided latent refinement framework that iteratively updates hidden representations before decoding.
  arXiv  Detail & Related papers  (2026-06-16T05:03:27Z)
• R-APS: Compositional Reasoning and In-Context Meta-Learning for Constrained Design via Reflective Adversarial Pareto Search [18.8323743697237]
  Large language models (LLMs) are fluent on open-ended tasks, yet in agentic settings, fluency does not ensure reliable delivery.<n>We trace this gap to three coupled structural failures: errors propagate without localization, worst-case perturbations go unevaluated, and accumulated knowledge is never invalidated.<n>We argue these share a root cause: abductive, counterfactual, meta-inductive, corrective, and inductive reasoning pull a shared context in incompatible directions.
  arXiv  Detail & Related papers  (2026-06-03T12:45:39Z)
• MMEmb-R1: Reasoning-Enhanced Multimodal Embedding with Pair-Aware Selection and Adaptive Control [36.06412693943508]
  We propose MMEmb-R1, an adaptive reasoning-based multimodal embedding framework.<n>We formulate reasoning as a latent variable and introduce pair-aware reasoning selection to identify reasoning paths beneficial for query-target alignment.<n>Experiments on the MMEB-V2 benchmark demonstrate that our model achieves a score of 71.2 with only 4B parameters, establishing a new state-of-the-art while significantly reducing reasoning overhead and inference latency.
  arXiv  Detail & Related papers  (2026-04-07T17:55:17Z)
• Structured Reasoning for Large Language Models [59.215789462977206]
  We propose Structured Reasoning (SCR), a framework that decouples reasoning trajectories into explicit, evaluable, and trainable components.<n>SCR substantially improves reasoning efficiency and self-verification.<n>Compared with existing reasoning paradigms, it reduces output token length by up to 50%.
  arXiv  Detail & Related papers  (2026-01-12T04:04:01Z)
• The Reasoning-Creativity Trade-off: Toward Creativity-Driven Problem Solving [57.652356955571065]
  State-of-the-art large language model (LLM) pipelines rely on bootstrapped reasoning loops.<n>We analyze how this design choice is sensitive to the collapse of the model's distribution over reasoning paths.<n>We introduce Distributional Creative Reasoning (DCR), a unified variational objective that casts training as gradient flow through probability measures on solution traces.
  arXiv  Detail & Related papers  (2026-01-02T17:10:31Z)
• From Efficiency to Adaptivity: A Deeper Look at Adaptive Reasoning in Large Language Models [17.96350700093472]
  Current large language models (LLMs) apply uniform reasoning strategies regardless of task complexity, generating long traces for trivial problems while failing to extend reasoning for difficult tasks.<n>This survey reframes reasoning through the lens of adaptivity: the capability to allocate reasoning effort based on input characteristics such as difficulty and uncertainty.
  arXiv  Detail & Related papers  (2025-11-13T20:25:04Z)
• In-Token Rationality Optimization: Towards Accurate and Concise LLM Reasoning via Self-Feedback [38.915062716409686]
  InTRO is a new framework that enables both token-level exploration and self-feedback for accurate and concise reasoning.<n>InTRO consistently outperforms other baselines, raising solution accuracy by up to 20% relative to the base model.<n>Its chains of thought are notably more concise, exhibiting reduced verbosity.
  arXiv  Detail & Related papers  (2025-11-13T01:47:06Z)
• ReaLM: Reflection-Enhanced Autonomous Reasoning with Small Language Models [76.28894983518164]
  Small Language Models (SLMs) are a cost-effective alternative to Large Language Models (LLMs)<n>They often struggle with complex reasoning due to their limited capacity and a tendency to produce mistakes or inconsistent answers.<n>We introduce ReaLM, a reinforcement learning framework for robust and self-sufficient reasoning in vertical domains.
  arXiv  Detail & Related papers  (2025-08-17T14:50:23Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Large Language Models as an Indirect Reasoner: Contrapositive and Contradiction for Automated Reasoning [74.90592233107712]
  We propose a Direct-Indirect Reasoning (DIR) method, which considers Direct Reasoning (DR) and Indirect Reasoning (IR) as multiple parallel reasoning paths that are merged to derive the final answer.<n>Our DIR method is simple yet effective and can be straightforwardly integrated with existing variants of CoT methods.
  arXiv  Detail & Related papers  (2024-02-06T03:41:12Z)
• SCREWS: A Modular Framework for Reasoning with Revisions [58.698199183147935]
  We present SCREWS, a modular framework for reasoning with revisions. We show that SCREWS unifies several previous approaches under a common framework. We evaluate our framework with state-of-the-art LLMs on a diverse set of reasoning tasks.
  arXiv  Detail & Related papers  (2023-09-20T15:59:54Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.