llm-driven-heuristic-discovery-combinatorial-optimization-tasks-tsp-cvrp-bi-objective CLM-0207For LLM-driven heuristic discovery on combinatorial optimization tasks (TSP, CVRP, bi-objective and multi-component variants), letting an agent decide when to read, edit, diagnose and evaluate code while persisting each evaluation outcome as a retrievable experience card reaches better final heuristic quality than fixed evolutionary pipelines (EoH, ReEvo, FunSearch, MCTS-AHD, MEoH, MOTIF) at a fraction of their evaluation budget, and re-running the same task warm-started from the previous run's best code improves further round after round under a fixed budget.
Capability: Fixing its own mistakes
Observed on
Six optimization heuristic-design tasks, 500-evaluation budget, three seeds, gpt-4o-mini and DeepSeek-V4-Flash; transfer gains reported to weaken outside aligned problem families, .
Sources
- Six tasks, three seeds, unified platform and instance splits; baselines run to the full 500 evaluations while AlgoEvo commits after ~27-275. Ablations separate cold vs warm start and rounds 0-4, and one cross-task transfer table. No matched-token-compute baseline, and the agentic loop plus skill hub plus experience bank are not fully separated from each other.
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Related claims
- When an agent builds a whole Python repository from natural-language requirements alone, refining component boundaries with explicit cohesion and coupling thresholds until no split or merge is triggered produces higher functionality coverage and test pass rate than letting the LLM decide restructuring actions for a fixed budget of rounds, because unconstrained LLM-decided actions keep over-decomposing past the point of a coherent modular structure.Generating and editing working code · reviewed by AI
- When an automated search over agent-harness mechanisms is gated on token efficiency with only a tolerance band on capability, the resulting full stack buys large token savings at a small but consistent score loss, and scores below the best single discovered mechanism — on EdgeBench the four-mechanism stack cut token traffic 44.7-49.0% and cost about a third while scoring 42.0 vs 44.8 for the base harness on GPT-5.6 Sol, whereas adding ObservationPack alone scored 47.2.Improving its own scaffolding from failures · reviewed by AI
- In LLM-driven iterative prompt optimization, restricting gradient generation to examples the current prompt gets wrong and accepting a candidate edit only when its gain on hard cases exceeds a penalized regression on a preservation set of initially-correct examples yields higher final accuracy than unfiltered textual-gradient optimization, which degrades on some reasoning tasks below the zero-shot prompt.Improving its own scaffolding from failures · reviewed by AI
- Evolving an agent harness on 2,000 executable tasks disjoint from the evaluation benchmarks, by contrasting successful and failed trajectories on the same task to find recurring deficiencies and evolving five harness modules (agent loop, tool use, observation management, context management, completion detection) separately before integrating them, raised DeepSeek-V4-Flash from 47.6 to 52.4 on TerminalBench 2.0 and 73.4 to 76.5 on SWE-Bench Verified, transferred across domains and to other backbones frozen (GLM-5.2 59.6 to 61.8, MiniMax-2.5 41.6 to 44.9), while evolving the whole harness at once or without modules fell below the unevolved baseline (46.4 and 44.2).Improving its own scaffolding from failures · reviewed by AI
- For LLM agents that must compose and then execute domain tool workflows, training separate adapters for the two roles splits the gains cleanly: supervised fine-tuning of the planner on reference tool sequences mainly improves workflow-order fidelity, while node-level RL on argument-match rewards mainly improves tool-argument grounding and final-answer quality, and combining both beats either on a 9B backbone.Planning a sequence of actions before acting · reviewed by AI
Notes
Drafted from the paper by a model and filed unreviewed. Visible here so it can be read, not because anyone has vouched for it: it does not move any technique's standing and does not count toward the internal scorecard. Drafted confidence: low. Falsifier as drafted: A matched-budget comparison where the agentic loop with the experience bank does no better than the same agent with the bank disabled, or where successive warm-started rounds show no monotonic gain. Drafted stance toward a-fixed-model-can-improve-its-own-harness-with-a-regression-gated-loop: supports -- A frozen model improves its own results across successive rounds purely by accumulating and reusing structured records of its past attempts, with no weight updates. Proposed technique, not catalogued: Experience-card bank with pluggable design skills for algorithm discovery.