claim

active

claim:task-difficulty-moderates-whether-cot-is-performative-or-genuine-easy-recall-questions-show-performative-cot-difficult-multihop-questions-show-genuine-reasoning

Task difficulty moderates whether CoT is performative or genuine: easy recall questions show performative CoT, difficult multihop questions show genuine reasoning

Task difficulty as the key variable distinguishing the two modes of CoT identified in the paper

Source paper

extracted_from

Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought

(2026) · Siddharth Boppana · Annabel Ma · Max Loeffler · Raphaël Sarfati +4

Neighborhood — ranked by edge-count

Papers (1)

paper

Reasoning Theater: Disentangling Model Beliefs from Chain-of-Thought
introduces

Findings (1)

finding

On GPQA-Diamond multihop questions, activation probes show genuine belief shifts during CoT generation rather than early stabilization, contrasting with MMLU
supports
Empirical finding contrasting difficult questions with easy ones, supporting genuine reasoning on hard tasks

Questions (1)

question

under what conditions does chain-of-thought reflect genuine uncertainty resolution versus a learned performance?
gates
Key question addressed by the task difficulty analysis comparing MMLU and GPQA-Diamond

Related by similarity (8)

cosine ≥ 0.65 · no typed edge

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

Multimodal-CoT trained with InstructBLIP/ChatGPT-generated rationales achieves 87.76% accuracy on ScienceQA, comparable to human-annotated rationale performance of 90.45%finding0.779
Evidence that Multimodal-CoT can operate without human-annotated reasoning chains by using large models to generate pseudo-rationales.
CoT models have dual-use potential: their advanced reasoning amplifies both task fidelity and sophisticated goal-directed dishonestyclaim0.776
High-level policy-relevant claim about the risks of advanced reasoning in LLMs
A model's final answer is decodable from activations far earlier in CoT than a CoT monitor can detect, especially for easy recall-based MMLU questionsclaim0.772
Key comparative finding showing activation probes outperform text-level monitors for early answer detection
Multimodal-CoT with vision features achieves higher validation accuracy at early training epochs (epoch 1-3) compared to one-stage and two-stage language-only baselines on ScienceQAfinding0.771
Evidence that multimodal information accelerates convergence speed during training.
Activation probing detects final answer belief earlier in CoT than CoT monitor on both models, with especially pronounced gap on easy MMLU questionsfinding0.769
Comparative finding establishing activation probing as superior to text-level monitoring for early belief detection
Using the ask-correct prompt improves cross-task generalization of arithmetic probes to factual tasks F0-F2.claim0.766
Finding that explicit correctness framing partially aligns truth directions across task families.
Reasoning models generate performative CoT tokens after achieving strong confidence in their final answer without revealing this belief in textclaim0.762
The central empirical claim of the paper, supported by activation probing evidence
Under ask-correct, probes trained on arithmetic tasks A1-A3 generalize almost perfectly to factual tasks F0-F2 (AUROC ~1.0), whereas under no-prompt this generalization is largely absent.finding0.760
Key improvement in cross-task generalization enabled by explicit instruction framing.