{
  "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": "2610.10738",
  "title": "Lossy Compressive Text Autoencoders",
  "summary": "The method replaces storing or transmitting raw text (or lossless compression such as gzip) with a learned lossy latent produced by residual downscaling along the time axis and a low-dimensional discrete bottleneck. It costs training a custom autoencoder per domain and tokenizer regime, adds encode/decode compute at inference, and accepts imperfect reconstruction rather than exact recovery. It can fail when downstream consumers need verbatim text, since the reconstruction is lossy and degradation may grow on domains, languages, or formats outside the training distribution.",
  "rationale": "Adopting this would require training a bespoke autoencoder and accepting lossy reconstruction for a compression rate only comparable to existing lossless codecs, so the engineering cost is not justified until a pretrained, domain-validated model or a clearer storage-at-scale advantage exists.",
  "technique": "Lossy discrete-bottleneck text autoencoder compression",
  "family": "memoria_e_contexto",
  "recommendation": "observar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.10738",
  "independent_implementations": 0,
  "total_implementations": 1,
  "stars": 122,
  "citations": 0,
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      "statement": "Compressed representations at 2.24 bits per byte on web text (roughly 3.6x versus 8-bit raw bytes), reported as on par with lossless text compression algorithms, with retained BLEU, LLM-judged semantic similarity, QA, and STS performance.",
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    {
      "dimension": "compatibility",
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      "dimension": "security",
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  "risks": [],
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  "report_available": false,
  "validation_tier": "unknown",
  "evidence_tier": "unknown",
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  "equations_status": "not_fetched",
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