arXiv AI
Glasseek: A Transparent Recipe for Training and Trustworthy Evaluation of DeepSearch Agents
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OpenResearcher: A Fully Open Pipeline for Long-Horizon Deep Research Trajectory Synthesis
OpenResearcher is a fully open, reproducible pipeline for generating long‑horizon deep research trajectories that interleave search, evidence aggregation, and multi‑step reasoning. It decouples corpus bootstrapping from trajectory synthesis and runs the search‑and‑browse loop offline using three browser primitives over a 15M‑document corpus. Using GPT‑OSS‑120B as a teacher, the pipeline produced over 97K trajectories, enabling a 30B‑A3B model to achieve 54.8% accuracy on BrowseComp‑Plus and providing insights into pipeline design through controlled analysis.
arXiv Machine Learning
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SearchArt: Training Long-Horizon Search Agent with Scalable Synthetic and Verified Task
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 AI
Aug 5
Video-DeepResearch: Towards the Next-Generation Multimodal Deepresearch Agent
arXiv:2608. 03979v1 Announce Type: cross Abstract: We introduce Video-DeepResearch (Video-DR), extending multimodal agents from static images to continuous video streams, a setting that demands dense spatiotemporal grounding coupled with open-web exploration.
arXiv AI
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Search-Time Contamination in Deep Research Agents: Measuring Performance Inflation in Public Benchmark Evaluation
arXiv:2606. 05241v1 Announce Type: cross Abstract: Public benchmarks enable fair and reproducible evaluation of LLM reasoning, but they become fragile for deep research agents that actively search the web during inference.
arXiv AI
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SearchSwarm: Towards Delegation Intelligence in Agentic LLMs for Long-Horizon Deep Research
arXiv:2606. 09730v1 Announce Type: new Abstract: Large language models are increasingly expected to handle complex, long-horizon real-world tasks whose context demands can grow without bound, yet model context windows remain inherently finite.
arXiv Computation and Language
Sep 18
F$^{2}$DR: A Fine-Grained Full-Pipeline Reward Framework for DeepSearch Workflows
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.