{
  "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": "2609.18842",
  "title": "Infinite-Parameter LLMs: Generating and Adapting Weights from Live Data",
  "summary": "The method replaces runtime prompt-based knowledge injection (retrieval or instruction re-read each request) with a hypernetwork that compiles session data into low-rank modulations of a shared base network, updated online via a Bayesian belief over the generator's latent code. It costs a hypernetwork and online belief-update machinery on top of the base model, adding inference-time adaptation complexity and training burden for the weight generator itself. It can fail because the abstract presents no empirical results, offers only an evaluation protocol, and online weight updates risk catastrophic interference with base-model behaviour and unvalidated reliability in production.",
  "rationale": "This is an architecture proposal without reported results, requiring foundation-model-adjacent training of a hypernetwork that exceeds the reader's infrastructure, so it warrants monitoring rather than adoption or testing.",
  "technique": "Online weight generation from live data via hypernetwork",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-09-16",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2609.18842",
  "independent_implementations": 3,
  "total_implementations": 4,
  "stars": 2,
  "citations": 0,
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  "validation_tier": "unknown",
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  "equations_status": "not_fetched",
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}