{
  "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": "2610.29183",
  "title": "Resuming Vision-Language Inference across Onboard Interruptions",
  "summary": "It replaces naive restart-from-scratch inference after interruptions with phase-aware checkpointing (vision encoding, prefill, decoding) and dependency-aware partial-state recovery. It costs additional SSD write traffic, checkpoint storage, scheduling complexity, and a recovery-planning component that must integrate with the inference stack. It can fail if checkpoint overhead exceeds recomputation savings on rarely interrupted workloads, if storage or bandwidth is constrained, or if the scheduling model misestimates layer and token-chunk restore costs.",
  "rationale": "The technique targets satellite-onboard inference with hard power and thermal interruptions, a deployment environment the reader does not face, though the checkpoint-resume idea is worth monitoring for preemptible or spot GPU serving.",
  "technique": "Checkpoint-resume for VLM inference across interruptions",
  "family": "recuperacao_de_falha",
  "recommendation": "observar",
  "published": "2026-10-21",
  "as_of": "2026-10-22",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.29183",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 4,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces naive restart-from-scratch inference after interruptions with phase-aware checkpointing (vision encoding, prefill, decoding) and dependency-aware partial-state recovery. It costs additional SSD write traffic, checkpoint storage, scheduling complexity, and a recovery-planning component that must integrate with the inference stack. It can fail if checkpoint overhead exceeds recomputation savings on rarely interrupted workloads, if storage or bandwidth is constrained, or if the scheduling model misestimates layer and token-chunk restore costs.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.29183",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "Up to 4.07x end-to-end speedup and 3.56x reduction in SSD write volume versus baselines on satellite payload hardware under repeated shutdowns.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.29183",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
      "interpretation": "",
      "limitation": "",
      "next_check": "",
      "source_url": "",
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    },
    {
      "dimension": "compute",
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      "finding": "",
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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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      "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": ""
}