T-Search: An Open Agentic Retriever and Playground for Hard Multi-Step Search
We present T-Search, an open-weight agentic retriever for hard multi-step search. Given a question and a search tool over a fixed corpus, it runs a bounded multi-round search and returns a ranked list of evidence chunks with short justifications, leaving answer generation to a downstream model, so backend and generator can be swapped without retraining. T-Search is built on Qwen3.6-35B-A3B and trained on adversarially filtered synthetic search tasks with round-sliced supervised fine-tuning followed by GSPO on a recall reward. Averaged over seven English and Russian benchmarks with gold evidence annotations, it reaches 56.0 Recall@10 with one rollout, 14.4 points above its base, and 61.3 w...
| Authors | Olga Tsymboi, Ramil Latypov, Aleksandr Medvedev et al. |
|---|---|
| Primary category | cs.CL |
| Published | 5 October 2026 |
| Last updated at source | 5 October 2026 |
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