{ "schema_version": 1, "builder_plan": { "scenario": "A self-hosted model uses more compute or memory than your workload can justify.", "change": "Check for a usable pruned checkpoint and runtime support before attempting model surgery.", "baseline": "The original unpruned model at the same precision, context, and load.", "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 quality, actual GPU memory, serving throughput, and preparation cost.", "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": "2610.26754", "title": "TokenGate: Adapting LLMs for Fast and Memory-Efficient Prefill with Learned Token Pruning", "summary": "TokenGate replaces full-sequence prefill, in which every input token passes through all layers, with a learned gating component that scores and prunes tokens via token-gated attention added to an existing pretrained model. The cost is a lightweight adaptation step comparable to parameter-efficient fine-tuning, plus the integration and maintenance of a nonstandard attention mechanism in the serving stack. It can fail on workloads where pruned tokens carry task-critical information, where the pruning distribution shifts from the adaptation data, or where short-to-medium contexts make the TTFT gain too small to justify the added complexity.", "rationale": "The claimed prefill speed and memory gains are directly relevant to a single 24 GB GPU serving long contexts, but the method requires a task-specific adaptation step and its accuracy preservation must be validated on the reader's own workloads before production adoption.", "technique": "Learned token pruning for prefill (TokenGate)", "family": "esparsidade_e_poda", "recommendation": "testar", "published": "2026-10-19", "as_of": "2026-10-20", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2610.26754", "independent_implementations": 0, "total_implementations": 1, "stars": 122, "citations": null, "claims": [ { "claim_id": "claim-01", "statement": "TokenGate replaces full-sequence prefill, in which every input token passes through all layers, with a learned gating component that scores and prunes tokens via token-gated attention added to an existing pretrained model. The cost is a lightweight adaptation step comparable to parameter-efficient fine-tuning, plus the integration and maintenance of a nonstandard attention mechanism in the serving stack. It can fail on workloads where pruned tokens carry task-critical information, where the pruning distribution shifts from the adaptation data, or where short-to-medium contexts make the TTFT gain too small to justify the added complexity.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.26754", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "1.4-11.3x faster time-to-first-token and 1.5-3.4x lower peak memory at reported no accuracy degradation; up to 6.4x faster prefill and 2x lower memory than prior token-pruning methods.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2610.26754", "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": "" }