{ "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": "2610.09832", "title": "SkillForge: Co-Evolving Skills and Agents via Dynamic Skill Lifecycles", "summary": "The method replaces indiscriminate accumulation of skill memory in memory-augmented agent RL with a lifecycle that trials, stabilizes, mutates, and retires skills based on measured fitness, including a pre-RL filtering phase before supervised fine-tuning. It costs a full RL training pipeline with repeated model rollouts, fitness evaluation, and LLM-guided mutation at each iteration, which demands substantial compute and infrastructure beyond a single 24 GB GPU or API budget. It can fail through mislabeled skill fitness that retires useful skills or retains harmful ones, and the co-evolution of library and policy may not transfer to a frozen API model the reader cannot fine-tune.", "rationale": "The technique presupposes end-to-end RL fine-tuning of the agent with iterated rollouts and skill evolution, which the reader's constrained infrastructure and API-based workflow cannot support, though the released SkillFurnace dataset and the lifecycle concept merit monitoring.", "technique": "Fitness-driven skill lifecycle for RL-trained LLM agents", "family": "memoria_e_contexto", "recommendation": "observar", "published": "2026-10-07", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.09832", "independent_implementations": 3, "total_implementations": 4, "stars": 132, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces indiscriminate accumulation of skill memory in memory-augmented agent RL with a lifecycle that trials, stabilizes, mutates, and retires skills based on measured fitness, including a pre-RL filtering phase before supervised fine-tuning. It costs a full RL training pipeline with repeated model rollouts, fitness evaluation, and LLM-guided mutation at each iteration, which demands substantial compute and infrastructure beyond a single 24 GB GPU or API budget. It can fail through mislabeled skill fitness that retires useful skills or retains harmful ones, and the co-evolution of library and policy may not transfer to a frozen API model the reader cannot fine-tune.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.09832", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Up to 7.8% relative improvement in aggregate success rate over the strongest baseline across interactive agent benchmarks, while keeping the skill library compact.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.09832", "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": "" }