{
  "date": "2026-09-27",
  "category": "Tech Daily",
  "papers": [
    {
      "id": "2601.03743",
      "title": "O-Researcher: An Open Ended Deep Research Model via Multi-Agent Distillation and Agentic RL",
      "authors": ["Yi Yao", "He Zhu", "Piaohong Wang", "Jincheng Ren", "Xinlong Yang", "Qianben Chen", "Xiaowan Li", "Dingfeng Shi", "Jiaxian Li", "Qiexiang Wang", "Sinuo Wang", "Xinpeng Liu", "Jiaqi Wu", "Minghao Liu", "Wangchunshu Zhou"],
      "url": "https://arxiv.org/abs/2601.03743",
      "published": "2026-01-07",
      "summary": "Introduces an automated framework for synthesizing research-grade instructional data through collaborative multi-agent simulation of complex tool-integrated reasoning, paired with a two-stage training strategy combining SFT and novel agentic reinforcement learning to achieve state-of-the-art open-source deep research performance.",
      "apa_7": "Yao, Y., Zhu, H., Wang, P., Ren, J., Yang, X., Chen, Q., Li, X., Shi, D., Li, J., Wang, Q., Wang, S., Liu, X., Wu, J., Liu, M., & Zhou, W. (2026). O-Researcher: An open ended deep research model via multi-agent distillation and agentic RL. arXiv preprint arXiv:2601.03743. https://doi.org/10.48550/arXiv.2601.03743"
    },
    {
      "id": "2606.15367",
      "title": "S1-DeepResearch: Beyond Search, Toward Real-World Long-Horizon Research Agents",
      "authors": ["Yao Dong", "Xinglin Xiao", "Liwei Dong", "Xinlong Jin", "Zhengbo Li", "Heng Zhang", "Duyun Wang", "Nan Xu"],
      "url": "https://arxiv.org/abs/2606.15367",
      "published": "2026-06-13",
      "summary": "Proposes a unified trajectory construction paradigm for deep research agents combining closed-ended QA with open-ended exploration via graph-grounded task formulation, agentic trajectory rollout, and multi-dimensional trajectory verification, advancing knowledge synthesis and long-horizon planning.",
      "apa_7": "Dong, Y., Xiao, X., Dong, L., Jin, X., Li, Z., Zhang, H., Wang, D., & Xu, N. (2026). S1-DeepResearch: Beyond search, toward real-world long-horizon research agents. arXiv preprint arXiv:2606.15367. https://doi.org/10.48550/arXiv.2606.15367"
    },
    {
      "id": "2606.18648",
      "title": "Deep Research in Physical Sciences: A Multi-Agent Framework and Comprehensive Benchmark",
      "authors": ["Yigeng Jiang", "Tengchao Yang", "Taoyong Cui", "Jiaxing Wan", "Yuan Wang", "Weida Wang", "Zhiyu Liu", "Chuyi Peng", "Binzhao Luo", "Maoli Gao", "Huaihai Huang", "Yuqianer Zeng", "Ziyang Zheng", "Dongchen Huang", "Chao Chen", "Zichao Liu", "Weiping Shen", "Shuchen Pu", "Siyu Zhou", "Runmin Ma", "Yusong Hu", "Fei Chao", "Bo Zhang", "Xiawu Zheng", "Zifu Wang", "Lei Bai", "Yunqi Cai", "Shufei Zhang"],
      "url": "https://arxiv.org/abs/2606.18648",
      "published": "2026-06-17",
      "summary": "Establishes PhySciBench and DelveAgent, a modular multi-agent system featuring an adaptive planning loop, dual-granularity memory, and a hierarchical physics-grounded reflection mechanism, improving deep scientific research accuracy while reducing inference costs by two thirds.",
      "apa_7": "Jiang, Y., Yang, T., Cui, T., Wan, J., Wang, Y., Wang, W., Liu, Z., Peng, C., Luo, B., Gao, M., Huang, H., Zeng, Y., Zheng, Z., Huang, D., Chen, C., Liu, Z., Shen, W., Pu, S., Zhou, S., ... Zhang, S. (2026). Deep research in physical sciences: A multi-agent framework and comprehensive benchmark. arXiv preprint arXiv:2606.18648. https://doi.org/10.48550/arXiv.2606.18648"
    },
    {
      "id": "2607.06595",
      "title": "When Agents Remember Too Much: Memory Poisoning Attacks on Large Language Model Agents",
      "authors": ["George Torres", "Sharad Shrestha", "Satyajayant Misra"],
      "url": "https://arxiv.org/abs/2607.06595",
      "published": "2026-07-06",
      "summary": "Investigates memory vulnerabilities in tool-using agents equipped with long-term memory, demonstrating the GhostWriter memory poisoning attack and proposing the AM-Sentry defense architecture using memory-saving policies and memory-retrieval screening.",
      "apa_7": "Torres, G., Shrestha, S., & Misra, S. (2026). When agents remember too much: Memory poisoning attacks on large language model agents. arXiv preprint arXiv:2607.06595. https://doi.org/10.48550/arXiv.2607.06595"
    },
    {
      "id": "2603.28361",
      "title": "Deep Research of Deep Research: From Transformer to Agent, From AI to AI for Science",
      "authors": ["Yipeng Yu"],
      "url": "https://arxiv.org/abs/2603.28361",
      "published": "2026-03-30",
      "summary": "Provides a comprehensive architectural taxonomy and evolutionary roadmap of deep research agents, unifying industry deep research methodologies with academic AI for Science (AI4S) paradigms and analyzing interaction models and core challenges.",
      "apa_7": "Yu, Y. (2026). Deep research of deep research: From Transformer to agent, from AI to AI for science. arXiv preprint arXiv:2603.28361. https://doi.org/10.48550/arXiv.2603.28361"
    }
  ],
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