{ "schema_version": 1, "builder_plan": { "scenario": "Tool errors or unavailable services cause your agent to abandon recoverable tasks.", "change": "Inject repeatable tool failures and change only the recovery policy.", "baseline": "Your current retry and fallback behavior under the same injected failures.", "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": "Recovered tasks, duplicate side effects, retries, time to recovery, and total 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": "2610.13672", "title": "Inject a Turn, Not a Token: Learning Where to Redirect LLM Reasoning for Self-Correction", "summary": "Replaces LLM fine-tuning for self-correction (revision/feedback training) with a 130K-parameter MLP head on a frozen encoder that selects where a failed rollout went wrong, retains the prefix, injects an instruction as a new user turn, and regenerates the suffix. Costs are modest: training the small head requires failed-rollout data labeled only by final-answer correctness, and each correction consumes an additional partial regeneration, so inference budget grows with the number of interventions. 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