{
  "schema_version": 1,
  "builder_plan": {
    "scenario": "Long customer conversations exhaust memory or limit concurrent requests.",
    "change": "Change only the attention cache policy, after checking model and runtime support.",
    "baseline": "The same model with its ordinary attention cache.",
    "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": "Peak cache memory, long-context answer quality, first-token latency, and requests completed.",
    "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": "2610.09476",
  "title": "CHASE: Channel-Aligned Structure Exploitation for Geometry-Aware Model Engineering",
  "summary": "CHASE replaces heuristic MHA-to-GQA conversion, layer-wise KV-cache allocation, and uniform structured pruning with geometry-driven choices: CAGA constructs shared KV heads by geometric alignment and low-rank subspace extraction, SAKV selects which adjacent layers share a low-rank KV representation and their ranks, and CAPS selects retained input channels per output-neuron group. The cost is one-off SVD/spectral analysis of trained weights plus implementation complexity across three separate method variants, with no stated inference-time overhead beyond the compressed artifacts. Failure modes include misalignment when weight spectra do not exhibit the assumed concentration, quality loss from incorrect shared-head or layer-group assignment on out-of-distribution workloads, and unverifiable benefit here because the abstract reports only qualitative superiority over baselines without numbers.",
  "rationale": "The KV-sharing and pruning methods target exactly the memory constraints of a 24 GB GPU, but with no quantified gains in the abstract and three distinct techniques to implement, the reader should validate CAGA or SAKV on their own model before adopting.",
  "technique": "GSA-based KV sharing and channel pruning (CHASE)",
  "family": "cache_kv",
  "recommendation": "testar",
  "published": "2026-10-07",
  "as_of": "2026-10-18",
  "editorial_status": "indexed",
  "source_url": "https://arxiv.org/abs/2610.09476",
  "independent_implementations": 0,
  "total_implementations": 0,
  "stars": 0,
  "citations": 0,
  "claims": [
    {
      "claim_id": "claim-01",
      "statement": "CHASE replaces heuristic MHA-to-GQA conversion, layer-wise KV-cache allocation, and uniform structured pruning with geometry-driven choices: CAGA constructs shared KV heads by geometric alignment and low-rank subspace extraction, SAKV selects which adjacent layers share a low-rank KV representation and their ranks, and CAPS selects retained input channels per output-neuron group. The cost is one-off SVD/spectral analysis of trained weights plus implementation complexity across three separate method variants, with no stated inference-time overhead beyond the compressed artifacts. Failure modes include misalignment when weight spectra do not exhibit the assumed concentration, quality loss from incorrect shared-head or layer-group assignment on out-of-distribution workloads, and unverifiable benefit here because the abstract reports only qualitative superiority over baselines without numbers.",
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      "result": "",
      "baseline": "",
      "conditions": "",
      "source_url": "https://arxiv.org/abs/2610.09476",
      "source_page": null,
      "source_excerpt": ""
    }
  ],
  "exposure_map": [
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      "dimension": "latency",
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      "dimension": "operations",
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    },
    {
      "dimension": "compatibility",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "security",
      "basis": "not_evaluated",
      "finding": "",
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      "source_page": null,
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    },
    {
      "dimension": "data_and_training",
      "basis": "not_evaluated",
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      "source_page": null,
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    },
    {
      "dimension": "reproducibility",
      "basis": "not_evaluated",
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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": "",
  "equations_source_url": "",
  "equations_source_sha256": "",
  "exposures_reviewed_at": "",
  "exposures_source_sha256": ""
}