{
  "schema_version": "1.1",
  "id": "archive:https://arxiv.org/abs/2608.06362v1",
  "slug": "2608-06362v1-04s3ir4",
  "url": "https://feed7.dev/p/2608-06362v1-04s3ir4",
  "title": "AV-AIVAT: 74x Cheaper Agent Evaluation with Certified Anytime-Valid Stopping in Imperfect-Information Games",
  "why_included": "AV-AIVAT combines variance reduction with anytime-valid stopping, cutting the game samples needed to compare agents while preserving a recheckable confidence claim.",
  "summary": "AV-AIVAT combines AIVAT corrections with continuously monitored confidence sequences. Across **15 agent configurations** and **71,439 paired HUNL hands**, AIVAT reduced variance by a median **54×**.",
  "practical_implication": "For costly agent comparisons, use sequential stopping rules instead of repeatedly checking ordinary confidence intervals. At 95% confidence and ±1 Big Blind precision, raw outcomes required a median **74×** more hands than corrected outcomes under AsympCS.",
  "agent_context": "AV-AIVAT combines AIVAT corrections with continuously monitored confidence sequences. Across **15 agent configurations** and **71,439 paired HUNL hands**, AIVAT reduced variance by a median **54×**.\n\nFor costly agent comparisons, use sequential stopping rules instead of repeatedly checking ordinary confidence intervals. At 95% confidence and ±1 Big Blind precision, raw outcomes required a median **74×** more hands than corrected outcomes under AsympCS.\n\nThe 74× result is asymptotic screening, not exact finite-sample certification. EB-CS needs an independently justified payoff bound, and descriptive HUNL runs showed only a 1.37× stopping-time ratio.",
  "source": {
    "name": "arXiv",
    "url": "https://arxiv.org/abs/2608.06362v1",
    "published_at": "2026-08-06T17:57:11.000Z"
  },
  "source_class": "blog_post",
  "content_type": "Paper",
  "layer": "benchmark",
  "domains": [],
  "topics": [
    "agent-evals",
    "agent-reliability",
    "multi-agent"
  ],
  "verification": {
    "status": "needs_review",
    "label": "Needs Review",
    "method": "unverified",
    "verified_at": null
  },
  "uncertainty": [
    "The 74× result is asymptotic screening, not exact finite-sample certification. EB-CS needs an independently justified payoff bound, and descriptive HUNL runs showed only a 1.37× stopping-time ratio."
  ],
  "connected_context": {
    "meaning": "This adds a statistical-efficiency layer to agent evaluation: once outcomes and corrections are valid, anytime-valid stopping can sharply reduce the cost of comparing noisy agents without invalid repeated confidence checks. It does not resolve the candidates’ concerns about judge quality, task validity, or trajectory coverage, and its headline efficiency gain is narrower than an exact finite-sample guarantee.",
    "corpus_size": 390,
    "generated_at": "2026-08-08T10:06:15.427Z",
    "connections": [
      {
        "title": "The Future of Evals: From LLM as a Judge to Agent as a Judge — Aparna Dhinakaran, Arize AI",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=q2JrUKBMf0w",
        "feed7_url": "https://feed7.dev/p/the-future-of-evals-from-llm-as-a-judge-to-agent-as-a-judge-aparna-dhina-1fu560o",
        "reason": "Agent-based analysis broadens what is judged; AV-AIVAT instead improves how much evidence is needed to compare agents, so the methods address complementary parts of an evaluation pipeline."
      },
      {
        "title": "Rethinking Environments for Long-Horizon Work — Rayan Garg, Theta Software",
        "source_name": "AI Engineer",
        "source_url": "https://www.youtube.com/watch?v=2aS7aKoXn64",
        "feed7_url": "https://feed7.dev/p/rethinking-environments-for-long-horizon-work-rayan-garg-theta-software-11r7wbx",
        "reason": "Queryable trajectories and final-state inspection determine meaningful outcomes, while AV-AIVAT can reduce the sampling cost only after those outcomes have been defined."
      },
      {
        "title": "PAIChecker: Uncovering and Checking PR-Issue Misalignment in SWE-Bench-Like Benchmarks",
        "source_name": "arXiv",
        "source_url": "https://arxiv.org/abs/2607.28587v1",
        "feed7_url": "https://feed7.dev/p/2607-28587v1-0u0uow2",
        "reason": "PAIChecker shows that precise scores can still measure a misaligned task; AV-AIVAT’s cheaper confidence therefore depends on benchmark-oracle integrity rather than replacing it."
      }
    ]
  },
  "lifecycle": "Current",
  "published_at": "2026-08-06T17:57:11.000Z",
  "modified_at": "2026-08-06T17:57:11.000Z",
  "supersedes": [],
  "expires_at": null,
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    "html": "https://feed7.dev/p/2608-06362v1-04s3ir4",
    "json": "https://feed7.dev/p/2608-06362v1-04s3ir4.json",
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