can activation probing enable efficient adaptive computation by detecting when a model's belief has stabilized during CoT generation?

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• Activation probing detects final answer belief earlier in CoT than CoT monitor on both models, with especially pronounced gap on easy MMLU questionsfinding0.820
  Comparative finding establishing activation probing as superior to text-level monitoring for early belief detection
• On GPQA-Diamond multihop questions, activation probes show genuine belief shifts during CoT generation rather than early stabilization, contrasting with MMLUfinding0.805
  Empirical finding contrasting difficult questions with easy ones, supporting genuine reasoning on hard tasks
• Attention probing can serve as an efficient tool for detecting performative reasoning and enabling adaptive computation in reasoning modelshypothesis0.799
  Forward-looking hypothesis positioned as a conclusion and future direction of the paper
• Reasoning models generate performative CoT tokens after achieving strong confidence in their final answer without revealing this belief in textclaim0.767
  The central empirical claim of the paper, supported by activation probing evidence
• Direct probes over learned activations in standard basis may fail to reveal the actual causal role of representations because they are highly distributedclaim0.753
  Supported by the finding that non-trivial rotations are required to find aligned representations.
• Can natural language explanations of activations generated through unsupervised reconstruction genuinely capture model cognition?question0.750
  Core research question motivating NLA development and validation through case studies and causal interventions.
• Multimodal-CoT trained with InstructBLIP/ChatGPT-generated rationales achieves 87.76% accuracy on ScienceQA, comparable to human-annotated rationale performance of 90.45%finding0.747
  Evidence that Multimodal-CoT can operate without human-annotated reasoning chains by using large models to generate pseudo-rationales.
• Inflection points (backtracking, 'aha' moments) occur almost exclusively in CoT responses where probes show large belief shifts, across DeepSeek-R1 671B and GPT-OSS 120Bfinding0.745
  Empirical finding linking textual CoT behaviors to internal belief dynamics