{ "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": "2610.12789", "title": "EvoBelief: Falsifiable World Models for Self-Improving LLM Agents", "summary": "It replaces opaque accumulation of experience, reflections, or skills with an explicit, versioned store of environment rules stated as falsifiable hypotheses, each validated by independent observations or controlled experiments before guiding the frozen LLM's plans. It costs additional API calls and latency for hypothesis formation, validation experiments, and rule scoping, plus the engineering complexity of maintaining the evidence-linked rule store. Incorrect or prematurely validated rules can propagate across tasks, and validation experiments may be unsafe or impossible in environments without a resettable sandbox.", "rationale": "The method runs on top of frozen API-accessible LLMs with no training and fits constrained infrastructure, but the gains are demonstrated on a small set of simulated benchmarks, so workload-specific validation is required before production adoption.", "technique": "Falsifiable hypothesis-based world models for agents", "family": "memoria_e_contexto", "recommendation": "testar", "published": "2026-10-08", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.12789", "independent_implementations": 2, "total_implementations": 3, "stars": 136, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "It replaces opaque accumulation of experience, reflections, or skills with an explicit, versioned store of environment rules stated as falsifiable hypotheses, each validated by independent observations or controlled experiments before guiding the frozen LLM's plans. It costs additional API calls and latency for hypothesis formation, validation experiments, and rule scoping, plus the engineering complexity of maintaining the evidence-linked rule store. Incorrect or prematurely validated rules can propagate across tasks, and validation experiments may be unsafe or impossible in environments without a resettable sandbox.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.12789", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "On ALFWorld without actor-side transition memory, improves success over the matched Frozen World Model by 23.9% (Claude Haiku 4.5) and 31.3% (GPT-5.4); on DiscoveryWorld Reactor Lab completes 83.3% of Normal-tier and 58.3% of Challenge-tier instances versus 0% for baselines.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.12789", "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": "" }