{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "An agent reads external documents or tool responses that may contain malicious instructions.",
    "change": "Replay attacks and legitimate lookalikes in an isolated copy of your agent workflow.",
    "baseline": "Your existing checks with the same tasks, permissions, and tool responses.",
    "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": "Successful attacks missed, legitimate actions blocked, detection delay, and review 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": "2609.23587",
  "title": "On the Efficiency-Safety Dilemma in Large Reasoning Models",
  "summary": "This is an evaluation framework rather than a new method; it replaces the implicit assumption that a quantized or pruned model's lower jailbreak success rate reflects genuine alignment, substituting explicit tests that separate true refusal behavior from capability-degraded 'attempted but failed' outputs. The cost is additional evaluation effort: mechanistic representational-drift analysis and attack testing must be run on each compressed checkpoint before deployment, adding engineering time and inference cost. What can fail is misreading the signal: apparent robustness after quantization or pruning may come from broken reasoning rather than safety, so the compressed model remains unsafe whenever it can still sustain a malicious trajectory.",
  "rationale": "The reader deploys quantized or pruned models on constrained infrastructure and must validate that observed jailbreak resistance is genuine alignment, not degraded reasoning, before trusting it in production.",
  "technique": "Safety evaluation of compressed reasoning models",
  "family": "seguranca_e_guardrails",
  "recommendation": "testar",
  "published": "2026-09-20",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.23587",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "This is an evaluation framework rather than a new method; it replaces the implicit assumption that a quantized or pruned model's lower jailbreak success rate reflects genuine alignment, substituting explicit tests that separate true refusal behavior from capability-degraded 'attempted but failed' outputs. The cost is additional evaluation effort: mechanistic representational-drift analysis and attack testing must be run on each compressed checkpoint before deployment, adding engineering time and inference cost. What can fail is misreading the signal: apparent robustness after quantization or pruning may come from broken reasoning rather than safety, so the compressed model remains unsafe whenever it can still sustain a malicious trajectory.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.23587",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "compute",
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    },
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      "dimension": "latency",
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    },
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
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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": ""
}