{
  "date": "2026-09-25",
  "category": "Tech Daily",
  "papers": [
    {
      "id": "2606.07299",
      "title": "DuMate-DeepResearch: An Auditable Multi-Agent System with Recursive Search and Rubric-Grounded Reasoning",
      "authors": ["Lingyong Yan", "Can Xu", "Yukun Zhao", "Wenxuan Li", "Qingyang Chen", "Jiulong Wu", "Wenli Song", "Xiangnan Li", "Weixian Shi", "Yiqun Chen", "Xuchen Ma", "Yuchen Li", "Jiashu Zhao", "Shuaiqiang Wang", "Jianmin Wu", "Dawei Yin"],
      "url": "https://arxiv.org/abs/2606.07299",
      "published": "2026-06-05",
      "summary": "DuMate-DeepResearch decouples the Agent Core from the Tool Ecosystem, introducing graph-based dynamic planning, recursive two-level search execution, and rubric-based test-time optimization to achieve auditable, state-of-the-art deep research.",
      "apa_7": "Yan, L., Xu, C., Zhao, Y., Li, W., Chen, Q., Wu, J., Song, W., Li, X., Shi, W., Chen, Y., Ma, X., Li, Y., Zhao, J., Wang, S., Wu, J., & Yin, D. (2026). DuMate-DeepResearch: An auditable multi-agent system with recursive search and rubric-grounded reasoning. arXiv preprint arXiv:2606.07299. https://doi.org/10.48550/arXiv.2606.07299"
    },
    {
      "id": "2510.04935",
      "title": "MARS: Co-evolving Dual-System Deep Research via Multi-Agent Reinforcement Learning",
      "authors": ["Guoxin Chen", "Zile Qiao", "Wenqing Wang", "Donglei Yu", "Xuanzhong Chen", "Hao Sun", "Minpeng Liao", "Kai Fan", "Yong Jiang", "Pengjun Xie", "Wayne Xin Zhao", "Ruihua Song", "Fei Huang"],
      "url": "https://arxiv.org/abs/2510.04935",
      "published": "2025-10-06",
      "revised": "2026-02-01",
      "summary": "MARS implements co-evolution between fast intuitive information processing (System 1) and deliberate reasoning (System 2) via multi-agent reinforcement learning with decoupled gradient computation and bin-packing optimization under Zero RL.",
      "apa_7": "Chen, G., Qiao, Z., Wang, W., Yu, D., Chen, X., Sun, H., Liao, M., Fan, K., Jiang, Y., Xie, P., Zhao, W. X., Song, R., & Huang, F. (2026). MARS: Co-evolving dual-system deep research via multi-agent reinforcement learning. arXiv preprint arXiv:2510.04935. https://doi.org/10.48550/arXiv.2510.04935"
    },
    {
      "id": "2601.18204",
      "title": "MemWeaver: Weaving Hybrid Memories for Traceable Long-Horizon Agentic Reasoning",
      "authors": ["Juexiang Ye", "Xue Li", "Xinyu Yang", "Chengkai Huang", "Lanshun Nie", "Lina Yao", "Dechen Zhan"],
      "url": "https://arxiv.org/abs/2601.18204",
      "published": "2026-01-26",
      "summary": "MemWeaver establishes a consolidation-centric tri-layer memory framework combining temporally grounded knowledge graphs, experience abstraction, and passage grounding for traceable long-horizon agent reasoning.",
      "apa_7": "Ye, J., Li, X., Yang, X., Huang, C., Nie, L., Yao, L., & Zhan, D. (2026). MemWeaver: Weaving hybrid memories for traceable long-horizon agentic reasoning. arXiv preprint arXiv:2601.18204. https://doi.org/10.48550/arXiv.2601.18204"
    },
    {
      "id": "2607.04617",
      "title": "MRMS: A Multi-Resolution Memory Substrate for Long-Lived AI Agents",
      "authors": ["Jizhizi Li", "Amy Shi-Nash"],
      "url": "https://arxiv.org/abs/2607.04617",
      "published": "2026-07-06",
      "summary": "MRMS proposes an architectural memory substrate across representational (structured-vector-graph) and temporal axes to achieve reliable, boundary-aware personalization and suppress stale memory.",
      "apa_7": "Li, J., & Shi-Nash, A. (2026). MRMS: A multi-resolution memory substrate for long-lived AI agents. arXiv preprint arXiv:2607.04617. https://doi.org/10.48550/arXiv.2607.04617"
    },
    {
      "id": "2606.06036",
      "title": "Memory is Reconstructed, Not Retrieved: Graph Memory for LLM Agents",
      "authors": ["Shuo Ji", "Yibo Li", "Bryan Hooi"],
      "url": "https://arxiv.org/abs/2606.06036",
      "published": "2026-06-04",
      "summary": "MRAgent shifts memory from static retrieval to active associative reconstruction over a Cue-Tag-Content graph, allowing dynamic adaptation and path pruning during agent reasoning.",
      "apa_7": "Ji, S., Li, Y., & Hooi, B. (2026). Memory is reconstructed, not retrieved: Graph memory for LLM agents. arXiv preprint arXiv:2606.06036. https://doi.org/10.48550/arXiv.2606.06036"
    }
  ],
  "graft": {
    "repo": "https://github.com/Alibaba-NLP/DeepResearch",
    "map_path": "profiles/r-and-d/raw_evidence/alibaba_deepresearch_map.txt",
    "saved_tokens": 269918,
    "parsed_files": 174,
    "symbols": 1091,
    "edges": 3293
  }
}