{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "A support or data assistant repeats work across related customer requests.",
    "change": "Try the paper's memory change in a replay of past tasks, with separate memory per customer.",
    "baseline": "Your current memory pipeline plus a no-memory control using the same agent.",
    "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": "Task success, stale memories, retrieval misses, response latency, and all memory maintenance costs.",
    "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": "2610.28612",
  "title": "TraceLite: Efficient Trace Reduction for SWE Agents via Optical Compression and Redundancy Removal",
  "summary": "TraceLite replaces full-text retention of SWE-agent interaction histories, and compression methods that rely on model training or auxiliary LLM calls, with a pipeline that keeps recent observations as text, renders older observations as images, removes cross-step overlap, and discards payloads that expire after code editing. It costs only local rendering and image-encoding overhead (0.438 s per observation), but increases prompt image-token usage and implementation complexity in the agent loop. It can fail when vision encoders lose fidelity on dense or unusual text, when task phases are misdetected so needed payloads are discarded, and its gains may not transfer beyond the two evaluated frontier backbones and benchmarks.",
  "rationale": "The method is training-free and cheap to run, making it feasible on constrained infrastructure, but the evidence is a single preliminary study on specific proprietary models and coding benchmarks, so workload-specific validation is warranted before production adoption.",
  "technique": "Training-free optical compression of agent traces",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-20",
  "as_of": "2026-10-21",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.28612",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "TraceLite replaces full-text retention of SWE-agent interaction histories, and compression methods that rely on model training or auxiliary LLM calls, with a pipeline that keeps recent observations as text, renders older observations as images, removes cross-step overlap, and discards payloads that expire after code editing. It costs only local rendering and image-encoding overhead (0.438 s per observation), but increases prompt image-token usage and implementation complexity in the agent loop. It can fail when vision encoders lose fidelity on dense or unusual text, when task phases are misdetected so needed payloads are discarded, and its gains may not transfer beyond the two evaluated frontier backbones and benchmarks.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.28612",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On SWE-bench Verified, TraceLite reduces average input-token consumption by 33.05% and API cost by 21.30% relative to uncompressed traces, while achieving a 78.25% resolve rate that exceeds the evaluated compression baselines; compression adds 0.438 seconds of latency per observation.",
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      "result": "",
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      "source_url": "https://arxiv.org/abs/2610.28612",
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      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "compute",
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
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      "source_url": "",
      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
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      "next_check": "",
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      "source_page": null,
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    }
  ],
  "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": "",
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}