{ "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": "2610.26825", "title": "MemT-1.0: Towards Memory-Native Evolution", "summary": "The method replaces full-context KV caching and external retrieval augmentation with memory internalized in model state via Gated DeltaNet-2 updates and a Mixture-of-Storage layer. It costs a full training pipeline (continued pre-training plus post-training on 34B tokens over a prepared backbone), a nonstandard architecture with custom training and inference infrastructure, and reported parity rather than superiority against retrieval-augmented methods. It can fail because the claimed gains are tied to the authors' released MemT checkpoints and infrastructure, long-horizon memory quality may degrade on workloads unlike their evaluations, and there is no drop-in path for existing API-based models.", "rationale": "The approach requires foundation-model training resources and custom infrastructure that exceed constrained infrastructure, and its benefits depend on adopting the authors' specific checkpoints rather than a transferable technique.", "technique": "Memory-native architecture with Gated DeltaNet-2 and Mixture-of-Storage", "family": "memoria_e_contexto", "recommendation": "observar", "published": "2026-10-19", "as_of": "2026-10-20", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.26825", "independent_implementations": 4, "total_implementations": 4, "stars": 304, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces full-context KV caching and external retrieval augmentation with memory internalized in model state via Gated DeltaNet-2 updates and a Mixture-of-Storage layer. It costs a full training pipeline (continued pre-training plus post-training on 34B tokens over a prepared backbone), a nonstandard architecture with custom training and inference infrastructure, and reported parity rather than superiority against retrieval-augmented methods. It can fail because the claimed gains are tied to the authors' released MemT checkpoints and infrastructure, long-horizon memory quality may degrade on workloads unlike their evaluations, and there is no drop-in path for existing API-based models.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.26825", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "At 128K-token history, MemT-1.0-Nano achieves approximately 1.86x end-to-end inference speedup and a 66x reduction in persistent storage relative to full-context KV cache.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.26825", "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": "" }