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GRIP: Grounded Reasoning via Information-Restricted Premises

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TL;DR - GRIP is a RAG method that restricts the capacity of the retrieved-evidence channel so models encode information missing from the query. It improves reasoning performance while reducing hallucination by 73% across five benchmarks.

  • Keeps full-dimensional query access but applies a severe stochastic bottleneck to retrieved evidence.
  • Reduces query–latent mutual information about 30Ă—, from 14.8 to 0.47 bits.
  • Outperforms strong iterative baselines on five reasoning benchmarks.
  • Produces evidence representations less aligned with query-dominated subspaces.

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GRIP: Grounded Reasoning via Information-Restricted Premises

arXiv cs.AI Lirui Teng 2026-08-17 arXiv:2608.16776
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-09-17 14:33:12.734834 UTC

TL;DR - GRIP is a RAG method that restricts the capacity of the retrieved-evidence channel so models encode information missing from the query. It improves reasoning performance while reducing hallucination by 73% across five benchmarks.

  • Keeps full-dimensional query access but applies a severe stochastic bottleneck to retrieved evidence.
  • Reduces query–latent mutual information about 30Ă—, from 14.8 to 0.47 bits.
  • Outperforms strong iterative baselines on five reasoning benchmarks.
  • Produces evidence representations less aligned with query-dominated subspaces.
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