{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Tool errors or unavailable services cause your agent to abandon recoverable tasks.",
    "change": "Inject repeatable tool failures and change only the recovery policy.",
    "baseline": "Your current retry and fallback behavior under the same injected failures.",
    "steps": [
      "Locate the paper's implementation and check its license, model support, data needs, and hardware requirements. Stop here if you cannot obtain the required artifacts.",
      "Save a small set of representative product tasks and expected outcomes. Keep evaluation tasks out of any training or tuning, and choose quality and cost limits before running.",
      "Run the baseline and the proposed change on identical inputs. Keep other settings fixed and repeat paired runs to expose variation.",
      "Save per-task outputs, failures, timings, and all usage costs. Review regressions before expanding the test."
    ],
    "measure": "Recovered tasks, duplicate side effects, retries, time to recovery, and total cost.",
    "decision": "Keep the change only if it meets your preselected quality and latency limits and improves the metric you care about after setup and operating costs. Otherwise retain the baseline. This is an exploration plan, not a validated recommendation."
  },
  "builder_review": null,
  "arxiv_id": "2610.03036",
  "title": "WebFovea: When the Model Is Right but the Click Is Wrong -- Reliable Round Trips for Vision-Based Web Agents on Live Websites",
  "summary": "The method replaces a naive model-to-browser loop with a hardened harness that verifies action parsing, coordinate mapping, effect confirmation, and observation fidelity, plus guardrails for rules and budget. It costs engineering effort in the agent scaffold rather than model capacity: it works with API-hosted multimodal models, adds modest per-step latency for verification and retries, and some fixes are model- or site-specific. After adoption, failures can persist on native dropdowns, iframes, silently failing inputs, coordinate mismatches after UI or resolution changes, and token contamination from chat-template handling.",
  "rationale": "Most observed failures occur in the glue code rather than model reasoning, so hardening the harness is a low-cost, high-leverage improvement fully compatible with API-only infrastructure.",
  "technique": "Harness hardening for vision-based web agents",
  "family": "recuperacao_de_falha",
  "recommendation": "adotar",
  "published": "2026-10-02",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.03036",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 10,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces a naive model-to-browser loop with a hardened harness that verifies action parsing, coordinate mapping, effect confirmation, and observation fidelity, plus guardrails for rules and budget. It costs engineering effort in the agent scaffold rather than model capacity: it works with API-hosted multimodal models, adds modest per-step latency for verification and retries, and some fixes are model- or site-specific. After adoption, failures can persist on native dropdowns, iframes, silently failing inputs, coordinate mismatches after UI or resolution changes, and token contamination from chat-template handling.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.03036",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "With the same underlying model, hidden-set score on the WebRetriever Challenge rose from 31.0 to 57.0 (of 100) through harness fixes alone, up to run-to-run variance on live sites.",
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      "source_url": "https://arxiv.org/abs/2610.03036",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
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      "finding": "",
      "interpretation": "",
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      "next_check": "",
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    },
    {
      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "risks": [],
  "minimum_test": [],
  "open_questions": [],
  "independent_tests": [],
  "lab_reproductions": [],
  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
  "infrastructure_basis": "unknown",
  "equations": [],
  "equations_status": "not_fetched",
  "core_kind": "",
  "equations_fetched_at": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}