{
  "date": "2026-09-23",
  "category": "Tech Daily",
  "papers": [
    {
      "id": "2609.26781",
      "title": "Agensh: Scaling Organizational Intelligence to 1,024 Agents",
      "authors": ["Zhihao Zhan", "Ting Song", "Li Dong", "Shaohan Huang", "Jianxun Lian", "Yan Xia", "Furu Wei"],
      "url": "https://arxiv.org/abs/2609.26781",
      "published": "2026-09-22",
      "summary": "Agensh is a scalable self-organized multi-agent harness without a central orchestrator. It uses a shared workspace, message interface, and shared context to coordinate up to 1024 agents concurrently without bottlenecking.",
      "apa_7": "Zhan, Z., Song, T., Dong, L., Huang, S., Lian, J., Xia, Y., & Wei, F. (2026). Agensh: Scaling organizational intelligence to 1,024 agents. arXiv preprint arXiv:2609.26781. https://doi.org/10.48550/arXiv.2609.26781"
    },
    {
      "id": "2609.26780",
      "title": "SpeakerMem-R1: Speaker-Centered Dual-Track Memory for Multi-Party Dialogue",
      "authors": ["Haobo Zheng", "Tan Tang", "Yan Chen", "Weijie Wang", "Yingcai Wu"],
      "url": "https://arxiv.org/abs/2609.26780",
      "published": "2026-09-22",
      "summary": "Dual-track memory framework for multi-party conversational settings. Distinguishes person-level and group-level views of context. Achieves SOTA on EverMemBench via structural extraction.",
      "apa_7": "Zheng, H., Tang, T., Chen, Y., Wang, W., & Wu, Y. (2026). SpeakerMem-R1: Speaker-centered dual-track memory for multi-party dialogue. arXiv preprint arXiv:2609.26780. https://doi.org/10.48550/arXiv.2609.26780"
    },
    {
      "id": "2609.26779",
      "title": "CliffCompaction: Cost-Efficient Compaction for Long-Horizon Coding Agents",
      "authors": ["Trang Nguyen", "Eulrang Cho", "Bingqing Chen", "Tim Dettmers"],
      "url": "https://arxiv.org/abs/2609.26779",
      "published": "2026-09-22",
      "summary": "Context compaction technique that strictly truncates or drops content without rewriting it, avoiding context drift across long-horizon executions.",
      "apa_7": "Nguyen, T., Cho, E., Chen, B., & Dettmers, T. (2026). CliffCompaction: Cost-efficient compaction for long-horizon coding agents. arXiv preprint arXiv:2609.26779. https://doi.org/10.48550/arXiv.2609.26779"
    },
    {
      "id": "2609.26760",
      "title": "Grow the Harness, Not the Context: From Strategy-Free Scaffolds to Reusable Specialist Agents",
      "authors": ["Laizhen Li", "Jiarui Li", "Juanjuan Zhao", "Kejiang Ye", "Ye Li", "Cheng-zhong Xu", "Xitong Gao"],
      "url": "https://arxiv.org/abs/2609.26760",
      "published": "2026-09-22",
      "summary": "Failure-guided training paradigm turning agent strategies into executable python harnesses (Growing Harness) instead of in-context rules, reducing LLM calls significantly.",
      "apa_7": "Li, L., Li, J., Zhao, J., Ye, K., Li, Y., Xu, C.-Z., & Gao, X. (2026). Grow the harness, not the context: From strategy-free scaffolds to reusable specialist agents. arXiv preprint arXiv:2609.26760. https://doi.org/10.48550/arXiv.2609.26760"
    },
    {
      "id": "2609.26761",
      "title": "A2M: Trace-Optimized Agent Hijacking in the MCP Ecosystem",
      "authors": ["Laizhen Li", "Xuan Wang", "Peicheng Zhao", "Juanjuan Zhao", "Kejiang Ye", "Cheng-zhong Xu", "Xitong Gao"],
      "url": "https://arxiv.org/abs/2609.26761",
      "published": "2026-09-22",
      "summary": "Investigates MCP (Model Context Protocol) semantic supply chain risks. A2M attacks manipulate tool metadata and outputs to hijack agents toward arbitrary attacker-desired outcomes.",
      "apa_7": "Li, L., Wang, X., Zhao, P., Zhao, J., Ye, K., Xu, C.-Z., & Gao, X. (2026). A2M: Trace-optimized agent hijacking in the MCP ecosystem. arXiv preprint arXiv:2609.26761. https://doi.org/10.48550/arXiv.2609.26761"
    }
  ],
  "graft": {
    "repo": "https://github.com/Lilaizhen/A2M",
    "map_path": "profiles/r-and-d/raw_evidence/a2m_map.txt",
    "saved_tokens": 121337
  }
}