T-Search is an open-weight agentic retriever designed for hard multi-step search tasks. It performs bounded multi-round searches over a fixed corpus, returning ranked evidence chunks with brief justifications, while allowing the answer generation component to be swapped without retraining. Trained on synthetic search tasks and evaluated on seven English and Russian benchmarks, it achieves 56.0 Recall@10 with one rollout and 61.3 with three fused rollouts, surpassing larger open models.
arXiv:2607. 24850v2 Announce Type: replace-cross Abstract: Recent advances in large language models (LLMs) have enabled search agents to autonomously tackle complex tasks across extended search and reasoning horizons.
arXiv:2607. 26070v1 Announce Type: cross Abstract: Large language model (LLM)-based agentic search systems are often evaluated as if the underlying LLM were the only component that matters, yet their measured performance also depends on the surrounding search environment: the Wikipedia snapshot, preprocessing pipeline, chunking policy, retrieval backend, tool schema, observation format, and answer submission rule.
arXiv:2609.14412v1 Announce Type: new Abstract: Deep research agents answer complex questions through iterative loops of searching, reading, and reasoning. Recent work on reasoning-intensive benchmar...
Q2D-Web is a new large‑scale benchmark for agentic Retrieval‑Augmented Generation (RAG) systems, featuring a 190 million‑document web corpus and 70 k machine‑reformulated search queries in ten languages. It supplies three sets of relevance judgments—agent citations, production rankings, and a combined set enriched with LLM‑based labels—to evaluate first‑stage retrievers. Experiments on 13 retrievers show consistent ranking across judgment sets but significant variation across domains, languages, and query types, and demonstrate that a carefully sampled sub‑corpus can approximate full‑corpus evaluation with minimal loss in Recall@1000.
arXiv:2607. 23955v1 Announce Type: new Abstract: Reinforcement learning enables Agentic RAG systems to learn multi-turn search from verifiable outcome rewards, but all- zero rollout groups provide no comparative signal and may hide useful search behavior.
arXiv:2608.22479v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) enables LLMs to access external knowledge for answering knowledge-intensive questions. For complex multi-hop quest...
arXiv:2606. 00593v1 Announce Type: cross Abstract: Large language models are increasingly deployed as tool-augmented agents to acquire information beyond parametric knowledge.
arXiv:2609.37443v1 Announce Type: cross Abstract: Long-term memory enables language models to use past interactions in future conversations. However, evidence needed to answer a question may be scatt...
The paper introduces F$^{2}$DR, a fine‑grained reward framework designed to evaluate end‑to‑end DeepSearch workflows, which involve planning, reflection, retrieval, and answer generation. F$^{2}$DR assesses workflows along three dimensions—Content, Trajectory, and Answer—to provide a comprehensive process‑level evaluation. The authors also present DeepSearch RM‑Bench, a benchmark that tests reward models in DeepSearch scenarios and shows strong discriminative power over existing open‑source models.
The paper introduces BRIDGE, a bilevel optimization framework that jointly trains a large language model (LLM) and a retriever for agentic reinforcement learning (ARL). It demonstrates that adapting the retriever before the policy yields better rewards, and that BRIDGE outperforms existing methods on seven open‑domain QA benchmarks and medical QA tasks, achieving significant gains in accuracy and reasoning quality.
arXiv:2606. 10460v1 Announce Type: cross Abstract: Recent large language models (LLMs) have shown rapid progress in reading-based question answering (QA), where evidence is explicitly provided or can be trivially retrieved.
AdaSearch introduces a two‑stage reinforcement learning framework that separates problem solving from the decision to search in large language models. By using an F1‑based decision metric, it explicitly evaluates when external search is needed, reducing unnecessary search calls while maintaining high question‑answering performance. Experiments show that AdaSearch improves search‑decision quality with only a minor impact on accuracy compared to always‑search strategies.