{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent looks good in demos, but you cannot tell which product tasks it fails.",
    "change": "Use the study's evaluation idea to inspect a small set of tasks with human-checked answers.",
    "baseline": "Your current evaluator and a manually reviewed reference set.",
    "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": "Agreement with human review, missed failures, false alarms, and evaluation cost per task.",
    "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": "2610.21304",
  "title": "VALCON: Benchmarking and Enhancing Value Consistency in Large Language Models via Dual-Role Variation",
  "summary": "VALCON replaces compliance-only alignment evaluation with paired generator/evaluator probes that expose inconsistencies when one model both produces and judges outputs, plus a representation-directed intervention to reduce them. Adopting it costs benchmark construction and inference time across role-paired prompts, and the mitigation requires internal activation access, which rules it out for API-only models; the evaluation suites remain usable with black-box access. It can fail because consistency findings are benchmark-specific, mechanistic conclusions may not transfer across model families, and representation interventions risk degrading general capability if applied without held-out validation.",
  "rationale": "Teams running self-evaluation or LLM-as-judge pipelines on constrained infrastructure can cheaply test their deployed models for generator/evaluator inconsistency using the released benchmark, but should validate findings on their own workload before adopting the intervention.",
  "technique": "Dual-role value consistency benchmark and representation intervention",
  "family": "avaliacao_de_agente",
  "recommendation": "testar",
  "published": "2026-10-15",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.21304",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "VALCON replaces compliance-only alignment evaluation with paired generator/evaluator probes that expose inconsistencies when one model both produces and judges outputs, plus a representation-directed intervention to reduce them. Adopting it costs benchmark construction and inference time across role-paired prompts, and the mitigation requires internal activation access, which rules it out for API-only models; the evaluation suites remain usable with black-box access. It can fail because consistency findings are benchmark-specific, mechanistic conclusions may not transfer across model families, and representation interventions risk degrading general capability if applied without held-out validation.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.21304",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
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      "dimension": "compute",
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    },
    {
      "dimension": "latency",
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    },
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      "dimension": "operations",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": ""
}