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Thought Branches: Interpreting LLM Reasoning Requires Resampling

ICLR 2026 · Large language models › Reasoning and chain of thought · approach: Long CoT dynamics and efficiency · 0 citations (OpenAlex)

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Argues that reasoning-model interpretability needs resampling over CoT distributions, and uses on-policy resampling to test causal effects in blackmail, CoT intervention, and hinted/biased MMLU and resume settings.

Problem

Interpreting reasoning models from a single chain-of-thought is inadequate for causal attribution, since models define distributions over many CoTs and can regenerate or repeat removed content.

Approach

Approximate the distribution of trajectories by on-policy resampling from selected CoT prefixes, and introduce resilience (number of resamples needed to eliminate a sentence's content) and counterfactual++ importance that measures effect when content is fully absent; compare these with off-policy edits and use transplant resampling for causal mediation of hints.

Key results

• Self-preservation sentences have the lowest resilience (~1-4 iterations) and negligible counterfactual++ importance (~0.001-0.003 KL).
• On-policy resampled insertions produce larger directional effects than off-policy edits, with up to 100% reduction in blackmail/whistleblow rates for some targets; resampled insertions have 11.4% no-effect vs ~25% for off-policy in blackmail.
• In resume screening, sentence-cluster causal effects correlate with frequency differences across candidate groups (r=.25, p=.004), and sentence mediators explain 77.5% of the ethnicity/gender total effect on average.

Surprising findings

• Self-preservation statements have negligible causal impact on blackmail decisions, suggesting they function as post-hoc rationalizations.
• Hints affecting the answer subtly and cumulatively shape CoT without being mentioned, and the token 'Wait' appears 30% less often under hint.

Limitations

• Resampling is computationally costly, making it suited to offline analysis.
• Case studies focus on specific prompts (blackmail) rather than prompt-agnostic self-preservation analysis.
• Black-box measures may not reflect internal representations; findings are proof-of-concept.

Experimental setup

Datasets: blackmail agentic misalignment scenario whistleblowing scenario MMLU with professor hints resume screening prompts

Benchmarks: blackmail scenario whistleblow scenario MMLU hinted questions resume screening

Metrics: KL divergence (counterfactual importance) resilience score (resamples to eliminate) blackmail/whistleblow rate change local plan shift rate mediation share of total effect

Baselines: off-policy handwritten sentence insertion cross-model sentence insertion same-model cross-context insertion baseline counterfactual importance

Builds on: agentic misalignment blackmail scenario activation patching counterfactual CoT interventions hinted MMLU unfaithfulness setup

Closest papers by meaning

• Causal Sufficiency and Necessity Improves Chain-of-Thought Reasoning (NeurIPS 2025 · similarity 0.89)
• Reasoning Models Sometimes Output Illegible Chains of Thought (NeurIPS 2025 · similarity 0.89)
• Chain-of-Thought Reasoning In The Wild Is Not Always Faithful (ICML 2026 · similarity 0.89)
• What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT (ICML 2026 · similarity 0.88)
• The CoT Encyclopedia: Analyzing, Predicting, and Controlling how a Reasoning Model will Think (ICLR 2026 · similarity 0.88)
• How Far Ahead Do LLMs Plan? Uncovering the Latent Horizon in Chain-of-Thought Reasoning (ICML 2026 · similarity 0.88)