Probe-based early-exit

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• Probe-Guided Early Exitconcept0.918
  Using activation probes to terminate CoT generation early when the model's belief is already stable, saving compute
• Probesconcept0.747
  Interpretability tools that decode information from internal model activations; here, linear probes are used for data attribution.
• Probe-guided early exit reduces tokens by up to 80% on MMLU and 30% on GPQA-Diamond with similar accuracyclaim0.746
  Practical efficiency claim for using activation probes to enable adaptive computation
• Probe-Based Data Attributionmethod0.736
  Linear classifier approach applied to model activations to identify which training datapoints caused undesired behaviors in post-training.
• Probe-guided early exit reduces tokens by up to 30% on GPQA-Diamond with similar accuracy on DeepSeek-R1 671B and GPT-OSS 120Bfinding0.733
  Quantitative efficiency result on hard benchmark, smaller reduction reflecting genuine reasoning need
• Probe-based method bridges interpretability (probes/activations) with data-centric alignment workclaim0.729
  Assertion from the paper's notes that the work connects two previously separate areas: interpretability tools and data-centric alignment.
• Probe scoreconcept0.721
  Dot product between hidden state and concept vector averaged across 5-layer window around best layer; measures model's internal emotive state
• Probe Generalizationconcept0.709
  The ability of probes trained on one dataset to transfer accurately to topically and structurally different datasets