LiteEvo: Automated, Cost-Efficient Harness Evolution for Generalization to Unseen Tasks

arXiv:2609.33146 · cs.AI · Submitted 2026-09-27 · Read on arXiv

cs.AI

Submitted: 2026-09-27

Updated: 2026-09-27

Terminology

• Benchmark — 154× in this paper · explained in 2 episodes
• Reinforcement Learning — 10× in this paper · explained in 4 episodes
• Failure Modes — 9× in this paper · explained in 3 episodes
• Affordance — 5× in this paper · explained in 1 episode
• Fine-Tuning — 4× in this paper · explained in 5 episodes
• Trace — 4× in this paper · explained in 2 episodes
• AI Agents — 3× in this paper · explained in 2 episodes
• Agent Harness — 3× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Multi-Agent System — 3× in this paper · explained in 6 episodes
• Provenance — 3× in this paper · explained in 3 episodes
• Ablation — 2× in this paper · explained in 1 episode
• Adam — 2× in this paper · explained in 2 episodes
• Benchmarking — 2× in this paper · explained in 3 episodes
• Generative AI — 2× in this paper · explained in 2 episodes
• In International Conference on Learning Representations — 2× in this paper
• Thinking mode — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Agentic System — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frozen model — 1× in this paper · explained in 3 episodes
• GRPO — 1× in this paper · explained in 6 episodes
• Introspection — 1× in this paper · explained in 2 episodes

Sources

• Training-Free Group Relative Policy Optimization
• Evaluating Large Language Models Trained on Code
• Magentic-One: A Generalist Multi-Agent System for Solving Complex Tasks
• Evo-Bench: Can Language Models Improve Agent Harness?
• Meta-Harness: End-to-End Optimization of Model Harnesses
• Agentic Harness Engineering: Observability-Driven Automatic Evolution of Coding-Agent Harnesses
• Harness Updating Is Not Harness Benefit: Disentangling Evolution Capabilities in Self-Evolving LLM Agents
• AutoHarness: improving LLM agents by automatically synthesizing a code harness
• A Self-Improving Coding Agent
• AgentSquare: Automatic LLM Agent Search in Modular Design Space
• Rethinking the Evaluation of Harness Evolution for Agents
• Agent Workflow Memory
• Better Harnesses, Smaller Models: Building 90% Cheaper Agents via Automated Harness Adaptation
• DAPO: An Open-Source LLM Reinforcement Learning System at Scale
• AFlow: Automating Agentic Workflow Generation
• HarnessCompass: Guiding Automatic Harness Evolution toward Generalizable and Effective Agent Harnesses

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