{
  "schema_version": "1.1",
  "id": "archive:https://arxiv.org/abs/2608.14528v1",
  "slug": "2608-14528v1-184x94t",
  "url": "https://feed7.dev/p/2608-14528v1-184x94t",
  "title": "Handover of In-Context Learning State Across Session Boundaries",
  "why_included": "Agent handoffs should preserve task-relevant predictive state, not blindly compress the transcript. The paper offers a three-part record for carrying decisions, evidence, and irreducible observations across sessions.",
  "summary": "The paper frames session handover as transferring a task-relative in-context learning state. Its **three-part record** keeps decisions and constraints exactly, summarizes repeated evidence with task-justified statistics, and retains observations whose effects those statistics lose.",
  "practical_implication": "For coding-agent workflows, design handoff files around the continuation task rather than generic transcript summaries. Preserve hard constraints verbatim, compress only evidence with a known sufficient representation, and keep raw observations when their downstream effect is uncertain.",
  "agent_context": "The paper frames session handover as transferring a task-relative in-context learning state. Its **three-part record** keeps decisions and constraints exactly, summarizes repeated evidence with task-justified statistics, and retains observations whose effects those statistics lose.\n\nFor coding-agent workflows, design handoff files around the continuation task rather than generic transcript summaries. Preserve hard constraints verbatim, compress only evidence with a known sufficient representation, and keep raw observations when their downstream effect is uncertain.\n\nThe sufficiency result assumes **exogeneity**, while exact finite-dimensional handover is demonstrated for Gaussian linear regression. The nonparametric case provides memory-versus-error bounds, not a universal recipe for software tasks.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2608.14528v1",
    "published_at": "2026-08-14T17:47:13.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "coding"
  ],
  "topics": [
    "agent-memory",
    "context-engineering",
    "multi-agent"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The sufficiency result assumes **exogeneity**, while exact finite-dimensional handover is demonstrated for Gaussian linear regression. The nonparametric case provides memory-versus-error bounds, not a universal recipe for software tasks."
  ],
  "connected_context": {
    "meaning": "This gives durable agent state a task-relative compression rule: preserve decisions and constraints exactly, summarize only evidence with a justified sufficient representation, and retain raw observations when effects are uncertain. It strengthens reset and shared-plan patterns while narrowing them with formal assumptions that do not establish a universal coding-agent handoff format.",
    "corpus_size": 468,
    "generated_at": "2026-08-17T10:04:35.619Z",
    "connections": [
      {
        "title": "Chained Recursive Language Models for Multi-Iteration Reasoning",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.05124v1",
        "feed7_url": "https://feed7.dev/p/2608-05124v1-05haobv",
        "reason": "Chained RLM provides the reset-and-resume harness in which this three-part handover record could replace undifferentiated summaries and blackboard state."
      },
      {
        "title": "huangruiteng/loopx",
        "source_name": "GitHub",
        "source_url": "https://github.com/huangruiteng/loopx",
        "feed7_url": "https://feed7.dev/p/loopx-0j0o7ux",
        "reason": "LoopX supplies durable goals, gates, evidence, and ownership across turns; the paper clarifies which parts of that continuation state should remain exact or compressed."
      },
      {
        "title": "Velocity Sickness: What Happens When Your Whole Team Gets 10x Faster — Matt Dailey, Ref.",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=Kz4QJmNrVXU",
        "feed7_url": "https://feed7.dev/p/velocity-sickness-what-happens-when-your-whole-team-gets-10x-faster-matt-1vpiun9",
        "reason": "Durable shared plans identify the coordination need, while this paper narrows how decisions, constraints, evidence, and uncertain observations should cross session boundaries."
      },
      {
        "title": "Blast Radius",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2608.07440v1",
        "feed7_url": "https://feed7.dev/p/2608-07440v1-1gi5t78",
        "reason": "Blast Radius preserves reversibility by archiving unused history, complementing the paper’s lossy task-relative compression where no sufficient representation is known."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-14T17:47:13.000Z",
  "modified_at": "2026-08-14T17:47:13.000Z",
  "supersedes": [],
  "expires_at": null,
  "formats": {
    "html": "https://feed7.dev/p/2608-14528v1-184x94t",
    "json": "https://feed7.dev/p/2608-14528v1-184x94t.json",
    "markdown": "https://feed7.dev/p/2608-14528v1-184x94t.md"
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}