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

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
Mechanistic InterpretabilityModel EditingCircuit Reshaping
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 that 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://anonymous.4open.science/r/REdit-DBD8.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces Reasoning Editing, a paradigm for selectively modifying specific reasoning patterns in LLMs while preserving other capabilities. It resides in the Circuit-Level Reasoning Pattern Editing leaf, which contains only two papers total (including this one). This represents a sparse and emerging research direction within the broader Parameter-Based Model Modification branch. The sibling paper focuses on patching compositional reasoning errors, suggesting this leaf addresses surgical interventions at the neural circuit level rather than broad behavioral modifications.

The taxonomy reveals that Circuit-Level Reasoning Pattern Editing sits adjacent to other parameter modification approaches: Activation and Representation Steering uses steering vectors on internal activations, while Parameter Weight Editing targets behavioral changes like detoxification. The paper's focus on circuit reshaping to manage interference between reasoning patterns distinguishes it from these neighboring methods, which either steer representations without structural modification or edit weights for behavioral control rather than reasoning-specific patterns. The scope_note emphasizes selective modification of reasoning patterns, excluding general parameter editing.

Among 30 candidates examined across three contributions, none were found to clearly refute the work. The Reasoning Editing Paradigm examined 10 candidates with 0 refutable; the Circuit-Interference Law examined 10 with 0 refutable; and the REdit Framework examined 10 with 0 refutable. This suggests that within the limited search scope, the core ideas—particularly the circuit-interference principle and the contrastive reshaping approach—appear relatively novel. The sparse taxonomy leaf (only 1 sibling paper) corroborates this impression of limited direct prior work in circuit-level reasoning pattern editing.

Given the limited search scope of 30 semantically similar candidates, the analysis captures nearby work but cannot claim exhaustive coverage of all relevant literature. The sparse taxonomy leaf and absence of refutable candidates suggest the work occupies a relatively unexplored niche at the intersection of circuit analysis and reasoning modification. However, the broader Parameter-Based Model Modification branch contains related techniques that may share conceptual overlap not fully captured by semantic search alone.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
30
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Selective modification of specific reasoning patterns in large language models. The field encompasses diverse approaches to controlling and refining how LLMs reason, structured into several major branches. Adaptive Reasoning Strategy Selection and Computational Efficiency focuses on dynamically choosing reasoning methods and managing computational costs, with works like Adaptive Solver[5] and DynamicMind[6] exploring strategy routing. Reasoning Pattern Enhancement and Diversification aims to improve or expand the variety of reasoning behaviors through techniques such as Synergy of Thoughts[3] and Neural Symbolic Reasoning[4]. Parameter-Based Model Modification and Behavioral Control targets direct intervention in model weights or circuits to steer reasoning, exemplified by Circuit Reshaping[0] and Patching Compositional Reasoning[25]. Knowledge Editing and Updating addresses factual corrections using methods like Easyedit[14] and Fineedit[10], while Text Editing and Modification Tasks handle surface-level text transformations. Post-Training Adaptation and Transfer Learning covers broader fine-tuning strategies to adjust reasoning capabilities after initial training, as surveyed in Post Training Survey[28]. A particularly active line of work explores circuit-level interventions that surgically modify internal reasoning pathways without full retraining, contrasting with broader adaptation methods that rely on fine-tuning or prompting. Circuit Reshaping[0] exemplifies this precise approach by targeting specific computational subgraphs responsible for reasoning patterns, closely aligning with Patching Compositional Reasoning[25], which also intervenes at the circuit level to fix compositional errors. These methods differ from adaptive routing strategies like Adaptive Solver[5] or DynamicMind[6], which select among existing reasoning modes rather than editing the underlying mechanisms. The trade-off centers on precision versus flexibility: circuit-level editing offers fine-grained control over particular reasoning behaviors but requires identifying the relevant components, while adaptive selection and post-training methods provide broader adjustments at the cost of less targeted modification. Open questions remain about the robustness and generalization of circuit edits across diverse reasoning tasks.

Claimed Contributions

Reasoning Editing Paradigm

The authors propose a new paradigm called Reasoning Editing that extends model editing from factual knowledge correction to the selective modification of logical inference patterns. This paradigm formally identifies a fundamental generality-locality trade-off in editing reasoning patterns.

10 retrieved papers
Circuit-Interference Law

The authors discover a fundamental principle showing that the degree to which editing one reasoning pattern affects another is directly proportional to the overlap between their respective neural circuits. This law provides theoretical grounding for their circuit reshaping approach.

10 retrieved papers
REdit Framework with Circuit Reshaping

The authors introduce REdit, a novel framework that actively reshapes neural circuits prior to reasoning editing through three components: Contrastive Circuit Reshaping, Meta-Contrastive Learning, and Dual-Level Protection. This represents the first approach to deliberately modulate neural circuits to improve reasoning editing outcomes.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Reasoning Editing Paradigm

The authors propose a new paradigm called Reasoning Editing that extends model editing from factual knowledge correction to the selective modification of logical inference patterns. This paradigm formally identifies a fundamental generality-locality trade-off in editing reasoning patterns.

Contribution

Circuit-Interference Law

The authors discover a fundamental principle showing that the degree to which editing one reasoning pattern affects another is directly proportional to the overlap between their respective neural circuits. This law provides theoretical grounding for their circuit reshaping approach.

Contribution

REdit Framework with Circuit Reshaping

The authors introduce REdit, a novel framework that actively reshapes neural circuits prior to reasoning editing through three components: Contrastive Circuit Reshaping, Meta-Contrastive Learning, and Dual-Level Protection. This represents the first approach to deliberately modulate neural circuits to improve reasoning editing outcomes.

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