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// When to Retrieve During Reasoning // Pay attention to this one, AI devs. (bookmark it) Most RAG systems retrieve once, before the model s…
// When to Retrieve During Reasoning // Pay attention to this one, AI devs. (bookmark it) Most RAG systems retrieve once, before the model starts reasoning. Large reasoning models like o1 and R1 don't
// When to Retrieve During Reasoning // Pay attention to this one, AI devs. (bookmark it) Most RAG systems retrieve once, before the model starts reasoning. Large reasoning models like o1 and R1 don't work that way. They generate 12k-25k token chains of thought and hit knowledge gaps mid-inference, long after the retrieval window closed. ReaLM-Retrieve is a reasoning-aware retrieval framework that injects evidence during multi-step inference. It detects uncertainty at reasoning-step granularity (not token or sentence level), learns a policy for when external evidence actually helps, and cuts per-retrieval overhead by 3.2x. This approach achieves +10.1% absolute F1 over standard RAG across MuSiQue, HotpotQA, and 2WikiMultiHopQA, with 47% fewer retrieval calls than fixed-interval IRCoT. On 2-4 hop MuSiQue it hits 71.2% F1 with only 1.8 retrieval calls per question. If you're shipping reasoning-model RAG, your retrieval needs to know when to fire, not just what to fetch. Paper: https://arxiv.org/abs/2604.26649 Learn to build effective AI agents in our academy: https://academy.dair.ai/
Source: DAIR.AI (X) | 2026-04-30