{
  "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": "2609.19743",
  "title": "Syndrome Decoding for Silent Data Corruption in Quantized Integer GPU Arithmetic",
  "summary": "SProbe replaces binary checksum-based ABFT (which detects but cannot localize or correct errors, and is blind to axis-cancelling error patterns) with a trailing verification kernel that pinpoints the column and exact magnitude of errors via Reed-Solomon syndrome decoding, then repairs or recomputes the GEMM. The cost is 11-49% of vendor GEMM time depending on size, roughly 30% end-to-end inference throughput, plus integration of a nontrivial verification and decoding pipeline. It can fail to be worth adopting at all: it addresses transient hardware faults in INT32 tensor-core accumulators, which are rare events whose relevance depends on deployment criticality, and the method was validated on an H100 that the reader may not possess, with correctness gains that matter mainly for safety-critical quantized inference rather than typical LLM serving.",
  "rationale": "The technique solves a real but narrow reliability problem (silent data corruption in unprotected tensor-core accumulators) that is only a production priority for safety-critical or regulated quantized inference, so a small team with one GPU should monitor it rather than absorb a 30% throughput penalty by default.",
  "technique": "Syndrome-decoded fault tolerance for quantized GPU GEMMs",
  "family": "recuperacao_de_falha",
  "recommendation": "observar",
  "published": "2026-09-17",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.19743",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "SProbe replaces binary checksum-based ABFT (which detects but cannot localize or correct errors, and is blind to axis-cancelling error patterns) with a trailing verification kernel that pinpoints the column and exact magnitude of errors via Reed-Solomon syndrome decoding, then repairs or recomputes the GEMM. The cost is 11-49% of vendor GEMM time depending on size, roughly 30% end-to-end inference throughput, plus integration of a nontrivial verification and decoding pipeline. It can fail to be worth adopting at all: it addresses transient hardware faults in INT32 tensor-core accumulators, which are rare events whose relevance depends on deployment criticality, and the method was validated on an H100 that the reader may not possess, with correctness gains that matter mainly for safety-critical quantized inference rather than typical LLM serving.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19743",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Detects every injected fault across seven fault classes with a Freivalds gate error-miss probability of at most 2^-141, correcting up to four colliding errors per row; costs 49% of GEMM time at N=16384, 11% at N=65536, and 30% throughput in an INT8 medical LLM.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2609.19743",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compute",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "latency",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "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": ""
}