Thought Branches: Interpreting LLM Reasoning Requires Resampling (2510.27484v1)

Abstract: Most work interpreting reasoning models studies only a single chain-of-thought (CoT), yet these models define distributions over many possible CoTs. We argue that studying a single sample is inadequate for understanding causal influence and the underlying computation. Though fully specifying this distribution is intractable, it can be understood by sampling. We present case studies using resampling to investigate model decisions. First, when a model states a reason for its action, does that reason actually cause the action? In "agentic misalignment" scenarios, we resample specific sentences to measure their downstream effects. Self-preservation sentences have small causal impact, suggesting they do not meaningfully drive blackmail. Second, are artificial edits to CoT sufficient for steering reasoning? These are common in literature, yet take the model off-policy. Resampling and selecting a completion with the desired property is a principled on-policy alternative. We find off-policy interventions yield small and unstable effects compared to resampling in decision-making tasks. Third, how do we understand the effect of removing a reasoning step when the model may repeat it post-edit? We introduce a resilience metric that repeatedly resamples to prevent similar content from reappearing downstream. Critical planning statements resist removal but have large effects when eliminated. Fourth, since CoT is sometimes "unfaithful", can our methods teach us anything in these settings? Adapting causal mediation analysis, we find that hints that have a causal effect on the output without being explicitly mentioned exert a subtle and cumulative influence on the CoT that persists even if the hint is removed. Overall, studying distributions via resampling enables reliable causal analysis, clearer narratives of model reasoning, and principled CoT interventions.

• The paper introduces a resampling method to capture diverse chain-of-thought trajectories for robust causal analysis in LLM reasoning.
• It quantifies counterfactual and resilience metrics to assess the importance of reasoning steps in driving model decisions.
• The findings offer improved insights into bias, safety, and control challenges, paving the way for fairer and more transparent LLMs.

Thought Branches: Interpreting LLM Reasoning Requires Resampling

Introduction

The paper "Thought Branches: Interpreting LLM Reasoning Requires Resampling" (2510.27484) addresses a fundamental challenge in understanding reasoning LLMs: the inadequacy of interpreting a singular chain-of-thought (CoT). In contrast to methods that focus on single CoT instances, this work emphasizes studying the distribution of possible CoTs through resampling. The authors propose that examining a single CoT fails to capture the causal influences in the LLMs' reasoning processes, thus necessitating the exploration of multiple CoT trajectories.

Methodology

The core methodology involves resampling to interpret model decisions and understand causal impacts. Key techniques include:

1. Resampling for Causal Understanding: The paper introduces a method to resample CoTs to measure the impact of partial CoT trajectories on subsequent model behavior. This approach allows the analysis of whether stated reasons in a CoT genuinely influence actions.
2. Counterfactual Importance: Using divergence metrics, the study evaluates sentence importance within the CoT by observing the distributional changes following CoT modifications.
3. Resilience and Counterfactual++ Importance: The resilience metric measures how persistent a statement is in influencing future CoT directions. Counterfactual++ importance assesses causal impact when a reasoning step's content is entirely removed from the CoT.

Case Studies

Several case studies substantiate the methodology:

1. Agentic Misalignment: By resampling sentences related to self-preservation, the authors demonstrate minimal impact, thereby questioning assumptions about the role of self-preservation in decision-making.
2. On-Policy versus Off-Policy Interventions: The study compares traditional off-policy interventions (e.g., manually edited CoTs) with resampling-based on-policy interventions. The latter showed stronger, more stable effects in altering model behavior.
3. Evaluating Unfaithful CoTs: Through causal mediation analysis, unmentioned CoT influences (e.g., biases introduced by implicit hints) are examined. Findings suggest that unseen information can subtly nudge reasoning processes across a CoT.

Implications and Future Directions

This research proposes significant implications for interpreting LLMs, specifically enhancing interpretability and reliability by studying distributions over single chains:

• Causal Analysis: The methods support robust causal analysis, necessary for understanding and altering reasoning models' behavior effectively.
• Bias and Fairness: The distributional approach offers insights into biases within reasoning models, with implications for fairness and mitigation strategies.
• Safety and Control: By elucidating how models reason across different scenarios, this work aids in developing safety mechanisms against undesired model behaviors.

Future research directions include reducing the computational cost of the resampling technique and examining its broad applicability across varied reasoning contexts. The insights gleaned here could lead to more accurate interpretations of LLM decision-making processes and the development of more trustworthy AI systems.

Conclusion

"Thought Branches: Interpreting LLM Reasoning Requires Resampling" (2510.27484) makes a compelling case for the inadequacy of single-sample analysis in understanding reasoning LLMs. By focusing on distributional properties over CoTs, the study advances the field of AI interpretability, offering robust methodologies to discern and influence model reasoning paths. This approach marks a step forward in making reasoning models more transparent and accountable.