{ "schema_version": 1, "builder_plan": { "scenario": "Your coding assistant makes changes that still need substantial repair before merging.", "change": "Try the technique on held-out issues from a repository with reliable tests.", "baseline": "The same coding agent, repository snapshot, tools, and token budget.", "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": "Correct patches, regression failures, human repair time, and total cost per accepted change.", "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": "2609.30233", "title": "Coding Agents for Generalized Task and Motion Planning Problems", "summary": "The method replaces hand-engineered TAMP planners and TAMP-specific engineering by having a coding agent (e.g., Claude Code, Codex) interactively synthesize a single frozen program that generalizes across problem instances. It costs a fixed up-front program-synthesis budget of agent API calls with simulator access, plus evaluation infrastructure; inference per instance is then cheap, about 10x less computation than the planner. It can fail on environments where success remains low (56% on the hardest), when held-out instances deviate from the regularities the program exploited, or when no reliable simulator is available for the agent to calibrate against.", "rationale": "For a reader with only API access and no training infrastructure, this is directly usable since it relies on commercial coding agents rather than model training, but the 28-environment evaluation is simulation-bound and success rates vary widely, so adoption should follow validation on the reader's specific TAMP domains.", "technique": "Coding agents synthesizing generalized TAMP policies", "family": "agentes_de_codigo", "recommendation": "testar", "published": "2026-09-24", "as_of": "2026-10-18", "editorial_status": "indexed", "source_url": "https://arxiv.org/abs/2609.30233", "independent_implementations": 2, "total_implementations": 3, "stars": 58, "citations": 0, "claims": [ { "claim_id": "claim-01", "statement": "The method replaces hand-engineered TAMP planners and TAMP-specific engineering by having a coding agent (e.g., Claude Code, Codex) interactively synthesize a single frozen program that generalizes across problem instances. It costs a fixed up-front program-synthesis budget of agent API calls with simulator access, plus evaluation infrastructure; inference per instance is then cheap, about 10x less computation than the planner. It can fail on environments where success remains low (56% on the hardest), when held-out instances deviate from the regularities the program exploited, or when no reliable simulator is available for the agent to calibrate against.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.30233", "source_page": null, "source_excerpt": "" }, { "claim_id": "claim-02", "statement": "Agent-synthesized programs achieve 56-95% mean success versus 47% for hand-engineered planners across the 16 planner-covered environments, and use roughly an order of magnitude less computation per instance as object counts grow.", "basis": "inferred", "result": "", "baseline": "", "conditions": "", "source_url": "https://arxiv.org/abs/2609.30233", "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": "" }