GRIP: Grounded Reasoning via Information-Restricted Premises

Abstract:High-capacity encoders in retrieval-augmented generation (RAG) can let the query dominate the latent state, leaving retrieved evidence functionally irrelevant. We call this failure mode query dominance. To address it, we introduce \textbf{GRIP} (Grounded Reasoning via Information-Restricted Premises), which imposes capacity asymmetry: the decoder keeps full-dimensional access to the query, while retrieved evidence passes through a severe stochastic bottleneck. This forces the evidence channel to encode only the residual information unavailable from the query. Across five reasoning benchmarks, GRIP outperforms strong iterative baselines, cuts a query--latent mutual-information diagnostic by roughly 30$\times$ (14.8 $\to$ 0.47 bits), and reduces hallucination by 73\%. Residual-alignment analysis further shows that the bottleneck output occupies subspaces less aligned with the query than baseline representations.

Artificial Intelligence (cs.AI)

arXiv:2608.16776 [cs.AI]

(or arXiv:2608.16776v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2608.16776 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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