{
  "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": "2610.10444",
  "title": "RunningTab: Direct Workspace Interaction with Environment-Side Tabs",
  "summary": "It replaces in-context (model-side) bookkeeping of task requirements, read files, and unopened candidates with an environment-maintained tab that pairs each requirement with best-matching excerpts, tracks unopened files, and blocks premature finishing via a finish check. The cost is extra orchestration infrastructure in the agent harness: excerpt and provenance logging, requirement-excerpt matching (which adds latency and likely additional model calls), and a finish-check gate, rather than any training or added GPU memory. It can fail if the matching logic pairs requirements with the wrong excerpts, if the agent sets requirements aside with weak reasons, or on tasks where requirements are implicit and never explicitly added to the tab.",
  "rationale": "The approach is implementable at small scale with a single GPU or API-based models, but its benefit depends on the reader's specific workspace-agent workload and the quality of requirement-excerpt matching, so it warrants a targeted pilot rather than immediate adoption.",
  "technique": "Environment-side per-task requirement ledger for file-workspace agents",
  "family": "memoria_e_contexto",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10444",
  "independent_implementations": 1,
  "total_implementations": 2,
  "stars": 121,
  "citations": null,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "It replaces in-context (model-side) bookkeeping of task requirements, read files, and unopened candidates with an environment-maintained tab that pairs each requirement with best-matching excerpts, tracks unopened files, and blocks premature finishing via a finish check. The cost is extra orchestration infrastructure in the agent harness: excerpt and provenance logging, requirement-excerpt matching (which adds latency and likely additional model calls), and a finish-check gate, rather than any training or added GPU memory. It can fail if the matching logic pairs requirements with the wrong excerpts, if the agent sets requirements aside with weak reasons, or on tasks where requirements are implicit and never explicitly added to the tab.",
      "basis": "inferred",
      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.10444",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "claim_id": "claim-02",
      "statement": "On three benchmarks with three LLMs, RunningTab consistently outperforms plain direct workspace interaction and baselines that keep the record in the model; no numeric improvement magnitude is stated in the abstract.",
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      "result": "",
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      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.10444",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
    {
      "dimension": "quality",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
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      "dimension": "compute",
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      "source_page": null,
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    },
    {
      "dimension": "latency",
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      "source_page": null,
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "next_check": "",
      "source_url": "",
      "source_page": null,
      "source_excerpt": ""
    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "limitation": "",
      "next_check": "",
      "source_url": "",
      "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": ""
}