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  3. Thinking in Latents: Adaptive Anchor Refinement for Implicit
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Thinking in Latents: Adaptive Anchor Refinement for Implicit Reasoning in LLMs

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Freshness: 2026-04-02T02:30:40.136932+00:00

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References: 0

Proof: unverified

Freshness: fresh

Source paper: Thinking in Latents: Adaptive Anchor Refinement for Implicit Reasoning in LLMs

PDF: https://arxiv.org/pdf/2603.15051v1

Source count: 0

Coverage: 17%

Last proof check: 2026-04-02T02:30:40.136Z

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Thinking in Latents: Adaptive Anchor Refinement for Implicit Reasoning in LLMs

Overall score: 7/10
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Last verification: 2026-04-02T02:30:40.136Z

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Coverage: 17%

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Emergent Search and Backtracking in Latent Reasoning Models
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Breaking Contextual Inertia: Reinforcement Learning with Single-Turn Anchors for Stable Multi-Turn Interaction
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Stable Adaptive Thinking via Advantage Shaping and Length-Aware Gradient Regulation
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Latent Reasoning with Supervised Thinking States
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How Do Latent Reasoning Methods Perform Under Weak and Strong Supervision?
Score 2.0down
Prior Work
SPOT: Span-level Pause-of-Thought for Efficient and Interpretable Latent Reasoning in Large Language Models
Score 7.0stable

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Related Resources

  • What are the emerging techniques for improving LLM reasoning beyond simple pattern matching?(question)
  • How do LLM reasoning traces contribute to more transparent and auditable AI systems?(question)
  • How can understanding LLM reasoning traces lead to more trustworthy AI assistants in customer service?(question)

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