{ "schema_version": 1, "builder_plan": { "scenario": "Your self-hosted model spends too long generating answers token by token.", "change": "Check whether a compatible draft model and serving integration exist before renting compute.", "baseline": "The same target with speculation off, then your existing drafter if you have one.", "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": "Accepted draft length, tokens per second, tail latency, output quality, and total GPU 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": "2610.01108", "title": "AgSpec: Pushing the Limits of Retrieval-Based Speculative Decoding in Coding Agent Pipelines", "summary": "AgSpec replaces learned-draft speculative decoders (e.g., EAGLE-3) and generic retrieval drafters by retrieving token continuations from session-trajectory, workspace, and global corpora indexed in the agent's emission format, with per-agent draft lengths capped by offline profiling and adapted online from verification feedback. It costs the engineering of building and maintaining three corpora plus offline profiling of draft-length caps per agent, and adds retrieval and verification overhead per step, though it avoids training a draft model entirely. 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