{ "schema_version": 1, "builder_plan": { "scenario": "A self-hosted assistant is too expensive because its model uses most of the GPU memory.", "change": "Try the paper's supported lower-precision weights on a copy of your serving setup.", "baseline": "The same model at its original precision, on the same GPU.", "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 memory, task pass rate, latency, and total serving cost at the same context and load.", "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": "2610.10385", "title": "OrBIT: Structure-Guided Embedding Compression", "summary": "OrBIT replaces fixed-geometry embedding compression (coordinate blocks, low-rank subspaces, unrestricted codebooks) with a learned shared-codeword codec in which reusable local geometry guides budget allocation, compiled away at inference into a compact decoder. The cost is an offline training and refinement procedure per embedding table plus a non-standard decoding path that must be integrated into the serving stack, with potential added reconstruction error versus uncompressed weights. It can fail if the learned orbit geometry does not transfer to the reader's specific table or downstream task, if small reconstruction distortions degrade model quality beyond the paper's benchmarks, or if the engineering effort of a custom codec outweighs simply using standard off-the-shelf quantization.", "rationale": "Embedding tables dominate memory in models the reader serves on a single 24 GB GPU, so a validated 23x-plus table compression is directly relevant, but the non-standard learned codec and per-table training require workload-specific quality validation before production adoption.", "technique": "Structure-guided learned embedding table compression", "family": "quantizacao", "recommendation": "testar", "published": "2026-10-07", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.10385", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "OrBIT replaces fixed-geometry embedding compression (coordinate blocks, low-rank subspaces, unrestricted codebooks) with a learned shared-codeword codec in which reusable local geometry guides budget allocation, compiled away at inference into a compact decoder. The cost is an offline training and refinement procedure per embedding table plus a non-standard decoding path that must be integrated into the serving stack, with potential added reconstruction error versus uncompressed weights. It can fail if the learned orbit geometry does not transfer to the reader's specific table or downstream task, if small reconstruction distortions degrade model quality beyond the paper's benchmarks, or if the engineering effort of a custom codec outweighs simply using standard off-the-shelf quantization.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.10385", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "37.9x compression on GPT-2 embeddings and over 23x on each 7B embedding table relative to 16-bit storage, with rate-distortion performance competitive with quantization and low-rank baselines.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.10385", "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": "" }