{
  "date": "2026-09-24",
  "category": "Tech Daily",
  "papers": [
    {
      "id": "2602.13830",
      "title": "A Tale of Two Graphs: Separating Knowledge Exploration from Outline Structure for Open-Ended Deep Research",
      "authors": ["Zhuofan Shi", "Ming Ma", "Zekun Yao", "Fangkai Yang", "Jue Zhang", "Dongge Han", "Victor Rühle", "Qingwei Lin", "Saravan Rajmohan", "Dongmei Zhang"],
      "url": "https://arxiv.org/abs/2602.13830",
      "published": "2026-02-14",
      "revised": "2026-08-25",
      "summary": "DualGraph memory decouples what the agent knows from how it writes, maintaining an Outline Graph and Knowledge Graph to drive structured deep exploration without lost-in-the-middle degradation.",
      "apa_7": "Shi, Z., Ma, M., Yao, Z., Yang, F., Zhang, J., Han, D., Rühle, V., Lin, Q., Rajmohan, S., & Zhang, D. (2026). A tale of two graphs: Separating knowledge exploration from outline structure for open-ended deep research. arXiv preprint arXiv:2602.13830. https://doi.org/10.48550/arXiv.2602.13830"
    },
    {
      "id": "2608.11632",
      "title": "Beyond Memory: A Transactional Continuity Kernel for Long-Lived AI Agents",
      "authors": ["Jun He", "Deying Yu"],
      "url": "https://arxiv.org/abs/2608.11632",
      "published": "2026-08-12",
      "summary": "Continuity Kernel (CK) introduces an atomic state activation contract that prevents stale overwrites and privilege escalation across long-lived agent state evolution.",
      "apa_7": "He, J., & Yu, D. (2026). Beyond memory: A transactional continuity kernel for long-lived AI agents. arXiv preprint arXiv:2608.11632. https://doi.org/10.48550/arXiv.2608.11632"
    },
    {
      "id": "2602.05965",
      "title": "Learning to Share: Selective Memory for Efficient Parallel Agentic Systems",
      "authors": ["Joseph Fioresi", "Parth Parag Kulkarni", "Ashmal Vayani", "Song Wang", "Mubarak Shah"],
      "url": "https://arxiv.org/abs/2602.05965",
      "published": "2026-02-05",
      "revised": "2026-06-14",
      "summary": "Learning to Share (LTS) implements learned shared-memory admission for parallel multi-agent teams using RL, eliminating redundant sub-problem computation.",
      "apa_7": "Fioresi, J., Kulkarni, P. P., Vayani, A., Wang, S., & Shah, M. (2026). Learning to share: Selective memory for efficient parallel agentic systems. arXiv preprint arXiv:2602.05965. https://doi.org/10.48550/arXiv.2602.05965"
    },
    {
      "id": "2609.03425",
      "title": "The Civilization Framework: Sovereign-Anchored Communication Between Personal Multi-Agent Systems",
      "authors": ["Guangjun Liu"],
      "url": "https://arxiv.org/abs/2609.03425",
      "published": "2026-09-03",
      "summary": "Proposes the Civilization Framework and Embassy Protocol for sovereign-anchored communication across multi-agent systems, mitigating temporal-weight hazards.",
      "apa_7": "Liu, G. (2026). The civilization framework: Sovereign-anchored communication between personal multi-agent systems. arXiv preprint arXiv:2609.03425. https://doi.org/10.48550/arXiv.2609.03425"
    },
    {
      "id": "2609.05529",
      "title": "DART: A DAG-Based Reputation and Incentive Framework via Blockchain-Enabled Governance for Trustworthy LLM Multi-Agent Collaboration",
      "authors": ["Manoj Kumala", "Xinyun Liu", "Ronghua Xu"],
      "url": "https://arxiv.org/abs/2609.05529",
      "published": "2026-09-01",
      "summary": "DART combines DAG workflow orchestration with decentralized governance and dynamic reputation updates to isolate uncooperative or malicious agents.",
      "apa_7": "Kumala, M., Liu, X., & Xu, R. (2026). DART: A DAG-based reputation and incentive framework via blockchain-enabled governance for trustworthy LLM multi-agent collaboration. arXiv preprint arXiv:2609.05529. https://doi.org/10.48550/arXiv.2609.05529"
    }
  ],
  "graft": {
    "repo": "https://github.com/TIGER-AI-Lab/OpenResearcher",
    "map_path": "profiles/r-and-d/raw_evidence/openresearcher_map.txt",
    "saved_tokens": 29053
  }
}