{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A product assistant retrieves plausible documents but gives unsupported answers.",
    "change": "Change one retrieval or ranking step while keeping the document snapshot fixed.",
    "baseline": "Your current retrieval pipeline with identical questions and answer model.",
    "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": "Supported answers, retrieval misses, correct abstentions, index cost, and response latency.",
    "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": "2610.28176",
  "title": "Temporal Evidence-Parameter Co-Alignment for LLM Reasoning over Temporal Knowledge Graphs",
  "summary": "TEPCA replaces standard semantic-relevance RAG retrieval and fixed, input-agnostic LoRA adapters with time-constrained path pruning plus mutual-information fact filtering, and a timestamp-gated LoRA that modulates the adaptation subspace per temporal context. The cost is a more complex retrieval pipeline (graph path construction, MI scoring, temporal constraints) and training the DG-LoRA gating mechanism, adding latency and engineering overhead beyond off-the-shelf RAG. It can fail if the underlying TKG has sparse or noisy timestamps, if queries lack clear temporal grounding, or if the extra pruning discards evidence that simpler retrieval would have kept.",
  "rationale": "The dual retrieval-plus-adaptation design is plausible and parameter-efficient for a single GPU, but the gain depends on having a temporal knowledge graph workload, so it warrants small-scale validation before production adoption.",
  "technique": "Temporal evidence retrieval with timestamp-conditioned LoRA",
  "family": "recuperacao_e_rag",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28176",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 122,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TEPCA replaces standard semantic-relevance RAG retrieval and fixed, input-agnostic LoRA adapters with time-constrained path pruning plus mutual-information fact filtering, and a timestamp-gated LoRA that modulates the adaptation subspace per temporal context. The cost is a more complex retrieval pipeline (graph path construction, MI scoring, temporal constraints) and training the DG-LoRA gating mechanism, adding latency and engineering overhead beyond off-the-shelf RAG. It can fail if the underlying TKG has sparse or noisy timestamps, if queries lack clear temporal grounding, or if the extra pruning discards evidence that simpler retrieval would have kept.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28176",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "The paper reports state-of-the-art performance on two temporal knowledge graph benchmarks, but no multiplicative factor is stated in the abstract.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28176",
      "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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      "finding": "",
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      "limitation": "",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "finding": "",
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      "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": ""
}