{
  "schema_version": "1.1",
  "id": "archive:https://arxiv.org/abs/2608.14509v1",
  "slug": "2608-14509v1-1rcfsxo",
  "url": "https://feed7.dev/p/2608-14509v1-1rcfsxo",
  "title": "Split the Labor: Separating Evidence Interpretation from Decision Aggregation",
  "why_included": "Multi-source agents should separate source interpretation from decision aggregation. A fixed evidence tuple plus calibrated likelihood pooling avoids vote thresholds that drift as more sources arrive.",
  "summary": "The paper splits multi-source reasoning into source interpretation and decision aggregation, joined by a **four-field evidence tuple**: hypothesis, reliability bucket, rationale, and provenance. It identifies count-scale drift when unnormalized scores are summed.",
  "practical_implication": "For research agents, make each reader emit the same typed record, then aggregate outside the model with calibrated log-likelihood ratios. This preserves comparability, supports abstention, and prevents the decision threshold from silently moving as source count changes.",
  "agent_context": "The paper splits multi-source reasoning into source interpretation and decision aggregation, joined by a **four-field evidence tuple**: hypothesis, reliability bucket, rationale, and provenance. It identifies count-scale drift when unnormalized scores are summed.\n\nFor research agents, make each reader emit the same typed record, then aggregate outside the model with calibrated log-likelihood ratios. This preserves comparability, supports abstention, and prevents the decision threshold from silently moving as source count changes.\n\nThe empirical system reaches **0.921 AUPRC** versus **0.805** for a hand-crafted baseline on one longitudinal corpus. The authors also report confounded comparisons and say domain-specific quantities still require re-estimation.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2608.14509v1",
    "published_at": "2026-08-14T17:24:55.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "agent",
  "domains": [
    "research",
    "data"
  ],
  "topics": [
    "harness-engineering",
    "multi-agent",
    "agent-reliability"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The empirical system reaches **0.921 AUPRC** versus **0.805** for a hand-crafted baseline on one longitudinal corpus. The authors also report confounded comparisons and say domain-specific quantities still require re-estimation."
  ],
  "connected_context": {
    "meaning": "This turns multi-source research into a typed interface between model judgment and deterministic aggregation. It strengthens evidence-backed approval and claim-level provenance, while explaining a specific failure of naive multi-agent voting: unnormalized scores move the effective threshold as sources accumulate. The reported gain remains confined to one corpus and requires domain recalibration.",
    "corpus_size": 468,
    "generated_at": "2026-08-17T10:04:35.619Z",
    "connections": [
      {
        "title": "Build for the Memo, Not the Demo — Shawn Chan, China Resources Holdings",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=tJFjeMBKbIY",
        "feed7_url": "https://feed7.dev/p/build-for-the-memo-not-the-demo-shawn-chan-china-resources-holdings-0i3s3oo",
        "reason": "The evidence tuple implements claim-level provenance, explicit reliability, and rationale in a form that can support the auditability contract proposed for finance agents."
      },
      {
        "title": "Why We Killed Our Multi-Agent Pipeline — Subbiah Sethuraman and Abhilash Asokan, ZS Associates",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=u6jJcIFDLE4",
        "feed7_url": "https://feed7.dev/p/why-we-killed-our-multi-agent-pipeline-subbiah-sethuraman-and-abhilash-a-0fmz3z3",
        "reason": "It supports bounded specialist investigations while avoiding a fixed chain of agents: readers interpret sources independently and a separate calibrated mechanism aggregates their records."
      },
      {
        "title": "What Does Done Even Mean? Agents and Paperclip's Liveness Model - Dotta, Paperclip",
        "source_name": "YouTube",
        "source_url": "https://www.youtube.com/watch?v=7P0elyLIxXo",
        "feed7_url": "https://feed7.dev/p/what-does-done-even-mean-agents-and-paperclip-s-liveness-model-dotta-pap-0lx8wfc",
        "reason": "Calibrated aggregation and abstention provide concrete evidence for a completion or escalation decision without allowing an agent to declare success from an unsupported Boolean."
      },
      {
        "title": "CausalForge: A Formally Grounded, Self-Improving Agentic Framework for Automated Research in Causal Inference",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2607.22511v1",
        "feed7_url": "https://feed7.dev/p/2607-22511v1-0mgsdh3",
        "reason": "Both separate evidence processing from final judgment; CausalForge’s warning that proof does not establish intended meaning parallels the need to preserve rationale and provenance alongside aggregation."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-14T17:24:55.000Z",
  "modified_at": "2026-08-14T17:24:55.000Z",
  "supersedes": [],
  "expires_at": null,
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    "html": "https://feed7.dev/p/2608-14509v1-1rcfsxo",
    "json": "https://feed7.dev/p/2608-14509v1-1rcfsxo.json",
    "markdown": "https://feed7.dev/p/2608-14509v1-1rcfsxo.md"
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}