This week
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Updated through the day. Every headline links straight to the source. The two lines underneath are ours.
The two-stage architecture wins decisively, and this matters for evidence synthesis at scale. If you're building systematic review tools or compliance search, this is the retrieval pattern to adopt. The cost curve means you still need to think about depth limits, but the signal quality justifies the extra stage.
Multi-hop retrieval has a real problem: early path decisions are brittle, flat retrieval is noisy. RESIN's decomposition strategy addresses both by making intermediate steps explicit and keeping alternatives live. The numbers are strong across benchmarks. For builders implementing retrieval-augmented systems, this is a pattern worth testing: entity-structured indexing plus one-hop retrieval per step tends to outperform flat search or greedy path selection. If you're shipping RAG, this is immediately relevant.
The practical constraint of keeping long tool outputs out of context windows hits different models differently. Claude handles retrieval-based evidence lookup more reliably than GPT models on this task, which matters for building agents that work with large structured reports. If you're deploying agents on real scientific data, this is a concrete signal about which model family stays accurate when you can't fit everything in context.