title: Knowing Before Saying: LLM Representations Encode Information About
    Chain-of-Thought Success Before Completion
summary: We investigate whether the success of a zero-shot Chain-of-Thought (CoT)
  process can be predicted before completion. We discover that a probing
  classifier, based on LLM representations, performs well even before a
  single token is generated, suggesting that crucial information about the
  reasoning process is already present in the initial steps representations. In
  contrast, a strong BERT-based baseline, which relies solely on the generated
  tokens, performs worse, likely because it depends on shallow linguistic cues
  rather than deeper reasoning dynamics. Surprisingly, using later reasoning
  steps does not always improve classification. When additional context is
  unhelpful, earlier representations resemble later ones more, suggesting LLMs
  encode key information early. This implies reasoning can often stop early
  without loss. To test this, we conduct early stopping experiments, showing that
  truncating CoT reasoning still improves performance over not using CoT at all,
  though a gap remains compared to full reasoning. However, approaches like
  supervised learning or reinforcement learning designed to shorten CoT chains
  could leverage our classifier's guidance to identify when early stopping is
  effective. Our findings provide insights that may support such methods, helping
  to optimize CoT's efficiency while preserving its benefits.
paper_authors._id: 68401045dd25841d998788cc
paper_authors.name: Anum Afzal
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Anum Afzal
paper_authors.type: user
paper_authors.user: anumafzal94
paper_authors._id: 68401045dd25841d998788cd
paper_authors.name: Florian Matthes
paper_authors._id: 68401045dd25841d998788ce
paper_authors.name: Gal Chechik
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paper_authors.fullname: Gal Chechik
paper_authors.type: user
paper_authors.user: galchechik
paper_authors._id: 68401045dd25841d998788cf
paper_authors.name: Yftah Ziser
paper_authors.status: extracted_pending
paper_authors.statusLastChangedAt: 2025-06-04T09:22:13.900Z
paper_authors._id: 66810e5877ed01ba880a4b40
paper_authors.avatarUrl: /avatars/3068f4b16f03a51772e652d76b37f9c3.svg
paper_authors.fullname: Yftah Ziser
paper_authors.type: user
paper_authors.user: yziser
paper_title: Knowing Before Saying: LLM Representations Encode Information About
    Chain-of-Thought Success Before Completion
paper_summary: We investigate whether the success of a zero-shot Chain-of-Thought (CoT)
  process can be predicted before completion. We discover that a probing
  classifier, based on LLM representations, performs well even before a
  single token is generated, suggesting that crucial information about the
  reasoning process is already present in the initial steps representations. In
  contrast, a strong BERT-based baseline, which relies solely on the generated
  tokens, performs worse, likely because it depends on shallow linguistic cues
  rather than deeper reasoning dynamics. Surprisingly, using later reasoning
  steps does not always improve classification. When additional context is
  unhelpful, earlier representations resemble later ones more, suggesting LLMs
  encode key information early. This implies reasoning can often stop early
  without loss. To test this, we conduct early stopping experiments, showing that
  truncating CoT reasoning still improves performance over not using CoT at all,
  though a gap remains compared to full reasoning. However, approaches like
  supervised learning or reinforcement learning designed to shorten CoT chains
  could leverage our classifier's guidance to identify when early stopping is
  effective. Our findings provide insights that may support such methods, helping
  to optimize CoT's efficiency while preserving its benefits.
paper_ai_summary: A classifier based on LLM representations predicts successful zero-shot CoT processes before generating tokens, suggesting that key reasoning information is captured early, and early stopping can improve performance.
paper_ai_keywords: zero-shot Chain-of-Thought LLM representations BERT-based baseline shallow linguistic cues deeper reasoning dynamics early stopping experiments supervised learning reinforcement learning