# T-Search: An Open Agentic Retriever and Playground for Hard Multi-Step Search

arXiv: https://arxiv.org/abs/2610.06782 · HTML: https://mlsanity.com/paper/2610.06782/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Web, search, and research agents, one of 16 directions in LLM agents and tool use.
- Builds on 15 of its 43 references that are in the map.
- First posted on arXiv 5 Oct 2026.

## Placed in

- LLM agents and tool use / [Web, search, and research agents](https://mlsanity.com/agents/web-search-and-research-agents/)

## Builds on (most cited first, 15 of 15)

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- [jina-embeddings-v5-text: Task-Targeted Embedding Distillation](https://mlsanity.com/paper/2602.15547/) (2026)
- [Harness-1: Reinforcement Learning for Search Agents with State-Externalizing Harnesses](https://mlsanity.com/paper/2606.02373/) (2026)
- [Essential-Web v1.0: 24T tokens of organized web data](https://mlsanity.com/paper/2506.14111/) (2025)
- [KARL: Knowledge Agents via Reinforcement Learning](https://mlsanity.com/paper/2603.05218/) (2026)
- [From Production Traffic to Post-Training: Building a Self-Hosted LLM That Covers the Corporate Request Mix](https://mlsanity.com/paper/2609.01572/) (2026)

## Abstract (arXiv)

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 with three fused rollouts, outperforming larger open models. We release the model, harness, live demo, and three benchmarks, including TRuST, the first native-Russian hard-search benchmark.