{ "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": "2609.29144", "title": "Scope Before You Persist: Preventing Cross-Family Interference in Agent Memory", "summary": "The method replaces globally retrieved persistent skill edits with retrieval scoped to the task family where each edit was certified, layered on an execution-grounded acceptance gate (ORC). It costs an additional routing mechanism to classify incoming tasks into families and tag stored edits, plus reduced edit reuse across families; no extra model training or GPU memory is required. It can fail when task families are poorly defined or drifting, causing misclassification that either blocks valid edits or recreates the cross-family interference it is meant to prevent.", "rationale": "The technique is cheap to implement on constrained infrastructure and directly addresses a demonstrated failure mode of persistent memory, but the evidence comes from a single 12-round code-repair benchmark, so the reader should validate scope matching on their own recurring-task workload before production adoption.", "technique": "Scope-matched retrieval for persistent agent memory", "family": "memoria_e_contexto", "recommendation": "testar", "published": "2026-09-24", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.29144", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces globally retrieved persistent skill edits with retrieval scoped to the task family where each edit was certified, layered on an execution-grounded acceptance gate (ORC). It costs an additional routing mechanism to classify incoming tasks into families and tag stored edits, plus reduced edit reuse across families; no extra model training or GPU memory is required. It can fail when task families are poorly defined or drifting, causing misclassification that either blocks valid edits or recreates the cross-family interference it is meant to prevent.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.29144", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Retrieving each accepted skill only for its originating task family raises mean hidden trajectory utility from 0.713 (global memory) to 0.816, and in 27 paired streams Scoped-ORC improves mean utility by 0.063 [0.037, 0.094] with 0/63 harmful acceptances versus six harmful deployments under global retrieval.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.29144", "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": "" }