Evidence-R1: Multi-Turn Search through an Evidence-Commit Protocol

Multi-turn search enables language models to progressively acquire the information needed for complex reasoning, but the continual accumulation of retrieved content also increases context and inference costs. Existing methods compress retrieved information through summarization or memory; however, when subsequent actions can still access the original content, the model may continue to rely on the uncompressed context, making it difficult for the explicitly maintained evidence state to truly serve as the medium for information transfer across turns. To address this issue, we propose Evidence-R1, a framework for multi-turn search based on an evidence-commit protocol. The framework explicitly maintains a cross-turn evidence state throughout the search process. After each retrieval step, the model first updates this state through an independent evidence-commit segment, then removes the raw retrieved content and temporary reasoning, and carries the updated evidence state into the next turn. To further improve evidence commitment, Provenance-Routed Segment Credit assigns local rewards to the evidence-commit segments that construct the relevant state versions, based on the provenance of the finally cited evidence and source-support verification results. Across seven open-domain question answering benchmarks, the 3B and 7B variants of Evidence-R1 achieve average accuracies of 44.1% and 49.1%, respectively, both exceeding the strongest same-scale baseline MemSearcher (43.8% and 48.9%). Under a unified efficiency evaluation setting, Evidence-R1 again achieves higher accuracy than MemSearcher while reducing peak context length by 37.5%–49.7% and inference time by 16.9%–21.2%. These results show that a mandatory and traceable evidence state can reduce the cost of multi-turn search while preserving question answering performance.