{ "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": "2608.07855", "title": "CommitKV: Lifecycle-Aware KV Cache Compression via Commit Transitions for Multi-Turn Agents", "summary": "Replaces instantaneous attention-score eviction such as H2O or SnapKV with a policy that measures each page's deletion effect around a tool-call commit and retires pages whose observed role has ended. It adds deletion-effect measurements, paging, joint greedy tests, and checkpoints to the inference loop. It requires clearly delimited tool-call commits in multiturn ReAct agents and access to the KV cache and inference pipeline, excluding closed third-party APIs.", "rationale": "Potentially useful for long-running, self-hosted agents on 24 GB GPUs, but requires inference-loop integration and workload validation; the abstract does not quantify the benefit.", "technique": "CommitKV: KV-cache compression at tool-call commits", "family": "cache_kv", "recommendation": "testar", "published": "2026-08-08", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2608.07855", "independent_implementations": 0, "total_implementations": 0, "stars": 0, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "Replaces instantaneous attention-score eviction such as H2O or SnapKV with a policy that measures each page's deletion effect around a tool-call commit and retires pages whose observed role has ended. It adds deletion-effect measurements, paging, joint greedy tests, and checkpoints to the inference loop. It requires clearly delimited tool-call commits in multiturn ReAct agents and access to the KV cache and inference pipeline, excluding closed third-party APIs.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2608.07855", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "The abstract claims lower memory use, faster end-to-end inference, and higher accuracy than existing methods, without figures.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2608.07855", "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": [ { "anchor": "S3.E6", "label": "(6)", "section": "3 Methodology", "role": "proposed_method", "latex": "\\boldsymbol{o}_{p}^{\\setminus E}=\\frac{\\boldsymbol{o}_{p}-\\sum_{\\begin{subarray}{c}i\\in E\\\\\np_{i}\\leq p\\end{subarray}}a_{pi}\\boldsymbol{v}_{i}}{1-\\sum_{\\begin{subarray}{c}i\\in E\\\\\np_{i}\\leq p\\end{subarray}}a_{pi}},", "mathml": "𝒐p∖E=𝒐p−∑i∈Epi≤pap​i​𝒗i1−∑i∈Epi≤pap​i,", "context": "For the previously defined event page E, deleting its tokens and renormalizing the remaining attention weights gives", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S3.E7", "label": "(7)", "section": "3 Methodology", "role": "proposed_method", "latex": "R_{h}(E;\\mathcal{Q}) =\\max_{p\\in\\mathcal{Q}}\\frac{\\|\\boldsymbol{o}_{p}-\\boldsymbol{o}_{p}^{\\setminus E}\\|_{2}}{\\|\\boldsymbol{o}_{p}\\|_{2}}, \\\\ R(E;\\mathcal{Q}) =\\max_{h}R_{h}(E;\\mathcal{Q}).", "mathml": "Rh​(E,𝒬)=maxp∈𝒬⁡‖𝒐p−𝒐p∖E‖2‖𝒐p‖2,R⁡(E,𝒬)=maxh⁡Rh​(E,𝒬).", "context": "where 𝒐p∖E is the output after deleting E. For a query window 𝒬, we first compute the maximum relative output change for each head and then take the maximum across heads:", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" }, { "anchor": "S3.E11", "label": "(11)", "section": "3 Methodology", "role": "proposed_method", "latex": "R_{c}^{+}\\left(\\mathcal{D}_{c}\\cup\\{E\\}\\right)\\leq\\tau_{\\mathrm{joint}}.", "mathml": "Rc+​(𝒟c∪{E})≤τjoint.", "context": "Starting from an empty retirement set 𝒟c=∅, CommitKV examines the candidates in this order. A candidate page E is added only if its joint deletion with the pages already accepted in 𝒟c satisfies", "headline": "", "explanation": "", "objective": "", "symbols": {}, "evidence": "", "evidence_url": "", "source_url": "" } ], "equations_status": "selected", "core_kind": "formula", "equations_fetched_at": "2026-09-16", "equations_source_url": "", "equations_source_sha256": "", "exposures_reviewed_at": "", "exposures_source_sha256": "" }