Retrieval shifts from content to reasoning skeletons: PatternBloom lets a 7B agent compound gains as multi-hop depth grows
PatternBloom changes the retrieval unit of agentic retrieval-augmented generation from what content to read to how to reason, using typed graph skeletons distilled from high-scoring Stage-I reinforcement-learning trajectories into an external Graph Pattern Memory, then coupling the policy to that frozen memory with a pattern-coverage reward. Across seven QA benchmarks, the 7B version ranks first on EM and F1 on all seven datasets, leads R1-Searcher on average out-of-distribution EM, and the same frozen pattern memory transfers to a Qwen3-8B backbone.