{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Start with one repeated product task that has a measurable failure or cost.",
    "change": "Read the methods and identify one change you can isolate in that workflow.",
    "baseline": "Your current implementation on the same held-out tasks.",
    "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": "Successful outcomes, response time, human rework, and total cost per successful task.",
    "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": "2610.11115",
  "title": "Dynamics as Code: On Model Compression via Dynamic System",
  "summary": "The method replaces conventional compression pipelines (quantization, pruning, distillation, low-rank decomposition) with encoding weights as indices into a trajectory of a dynamic system, recovering parameters at load time via decompression rather than storing them directly. Costs include a decompression step at deployment, extra indexing structures (KD-tree, coordinate templates) for large models, and engineering effort for a nonstandard format with no established tooling or hardware support. It can fail through accuracy degradation when the trajectory's epsilon-net resolution is too coarse for sensitive layers, through outlier weights that break the bounded-error guarantee, and through limited validation so far (ResNet-18 and models up to 7B), leaving behavior on larger production LLMs unproven.",
  "rationale": "The approach is retraining-free and fits a 24 GB GPU workflow, but its novel encoding lacks ecosystem support and has only small-to-mid-scale validation, so it warrants a controlled pilot against a quantization baseline before any production adoption.",
  "technique": "Dynamic-system weight encoding for model compression",
  "family": "outro",
  "recommendation": "testar",
  "published": "2026-10-08",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.11115",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "The method replaces conventional compression pipelines (quantization, pruning, distillation, low-rank decomposition) with encoding weights as indices into a trajectory of a dynamic system, recovering parameters at load time via decompression rather than storing them directly. Costs include a decompression step at deployment, extra indexing structures (KD-tree, coordinate templates) for large models, and engineering effort for a nonstandard format with no established tooling or hardware support. It can fail through accuracy degradation when the trajectory's epsilon-net resolution is too coarse for sensitive layers, through outlier weights that break the bounded-error guarantee, and through limited validation so far (ResNet-18 and models up to 7B), leaving behavior on larger production LLMs unproven.",
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      "statement": "Achieves competitive compression ratios without post-hoc retraining on ResNet-18 and Qwen2.5-1.5B/Qwen1.5-7B, with controllable decompression error; no specific factor is stated in the abstract.",
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  ],
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      "dimension": "latency",
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    },
    {
      "dimension": "operations",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "compatibility",
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      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
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    },
    {
      "dimension": "data_and_training",
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    },
    {
      "dimension": "reproducibility",
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      "source_page": null,
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  ],
  "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": "",
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  "equations_source_sha256": "",
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}