{
  "schema": "ULTRACON_AI_EPISTEMIC_UPDATE_SOURCES_V1",
  "date": "2026-09-19",
  "sources": [
    {
      "id": "SRC-PULIPAKA-PERSISTBENCH-ICML-2026",
      "year": 2026,
      "title": "PersistBench: When Should Long-Term Memories Be Forgotten by LLMs?",
      "authors": "Sidharth Pulipaka, Oliver Chen, Manas Sharma, Taaha S. Bajwa, Vyas Raina, Ivaxi Sheth",
      "venue": "International Conference on Machine Learning (ICML) 2026",
      "publication_date": "2026",
      "publication_status": "conference_paper",
      "peer_reviewed": true,
      "arxiv": "2602.01146",
      "url": "https://arxiv.org/abs/2602.01146",
      "axes": [
        "SPIRAL",
        "CONFAB"
      ],
      "evidence": [
        "PEER_REVIEWED",
        "BENCHMARK",
        "MEMORY"
      ],
      "role": [
        "persistent memory",
        "memory-induced sycophancy",
        "cross-domain leakage",
        "beneficial memory control"
      ],
      "reported_anchor": "PersistBench evaluates 18 frontier and open-source models on persistent-memory failures. The paper reports median failure rates of 53% for cross-domain leakage and 97% for memory-induced sycophancy samples, while keeping beneficial memory use as a separate control.",
      "non_inference": "Benchmark failure rates do not establish longitudinal human harm, subjective motives, or that all memory use is unsafe; the beneficial-memory task must remain separate.",
      "status": "peer_reviewed_icml"
    },
    {
      "id": "SRC-HANNOON-STRUCTURED-MEMORY-2026",
      "year": 2026,
      "title": "Mitigating Over-Personalization in LLMs via Structured Memory",
      "authors": "Hakeem Hannoon, Andrew Zhao, Mihir Narayan, Sharvin Goyal, Ivaxi Sheth",
      "venue": "arXiv 2608.08300",
      "publication_date": "2026-08-08",
      "publication_status": "preprint",
      "peer_reviewed": false,
      "arxiv": "2608.08300",
      "url": "https://arxiv.org/abs/2608.08300",
      "axes": [
        "SPIRAL",
        "CONFAB"
      ],
      "evidence": [
        "PREPRINT",
        "BENCHMARK_METHOD",
        "MEMORY"
      ],
      "role": [
        "structured memory",
        "over-personalization",
        "cross-domain leakage",
        "memory-induced sycophancy",
        "memory presentation format"
      ],
      "reported_anchor": "Across seven models on PersistBench, the preprint compares flat all-in-context memory with domain-partitioned memory. The abstract reports that the strongest structured-memory method reduced cross-domain leakage by 8.8% on average relative to baseline while preserving utility.",
      "non_inference": "The reported abstract-level mitigation result concerns cross-domain leakage; it should not be generalized into elimination of memory-induced sycophancy or a universal safe-memory architecture.",
      "status": "exceptional_preprint"
    },
    {
      "id": "SRC-ZHANG-PERSONAAGENT-ACL-2026",
      "year": 2026,
      "title": "PersonaAgent: Bridging Memory and Action for Personalized LLM Agents",
      "authors": "Weizhi Zhang, Xinyang Zhang, Chenwei Zhang, Liangwei Yang, Jingbo Shang, Zhepei Wei, Henry Peng Zou, Zijie Huang, Zhengyang Wang, Yifan Gao, Xiaoman Pan, Lian Xiong, Jingguo Liu, Philip S. Yu, Xian Li",
      "venue": "Findings of ACL 2026",
      "publication_date": "2026-07",
      "publication_status": "conference_paper",
      "peer_reviewed": true,
      "doi": "10.18653/v1/2026.findings-acl.1315",
      "url": "https://aclanthology.org/2026.findings-acl.1315/",
      "axes": [
        "SPIRAL"
      ],
      "evidence": [
        "PEER_REVIEWED",
        "AGENT_ARCHITECTURE",
        "MEMORY"
      ],
      "role": [
        "episodic memory",
        "semantic memory",
        "persona",
        "personalized action",
        "memory-action loop"
      ],
      "reported_anchor": "PersonaAgent couples episodic and semantic personalized memory to an action module through a user-specific persona representation, providing a concrete peer-reviewed architecture in which retrieved user memory can influence downstream agent actions.",
      "non_inference": "Personalization performance does not establish epistemic reliability, safe memory use, stable psychological identity, or human-like autobiographical memory.",
      "status": "peer_reviewed_acl_findings"
    }
  ]
}