arXiv AI
AutoSaddler: Automatic Harness Optimization with Durable Updates from Agent Execution Traces
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arXiv Computation and Language
Aug 21
Self-Harness: Harnesses That Improve Themselves
arXiv:2606. 09498v3 Announce Type: replace Abstract: The performance of LLM-based agents is jointly shaped by their base models and the harnesses that mediate their interaction with the environment.
arXiv AI
Jul 28
Co-Harness: Co-Evolving Harnesses and Model Weights for LLM Agents
arXiv:2607. 22688v1 Announce Type: new Abstract: Post-training agents for automated AI research requires optimizing not only model parameters, but also the runtime harness that shapes how research trajectories are generated, evaluated, and learned from.
arXiv Machine Learning
Jun 5
Retrospective Harness Optimization: Improving LLM Agents via Self-Preference over Trajectory Rollouts
arXiv:2606. 05922v1 Announce Type: cross Abstract: AI agents rely on a harness of skills, tools, and workflows to solve complex problems.
Hugging Face Trending Papers
Sep 10
Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Ecdysis is a framework for training runtime harnesses for large language model agents more efficiently. It distinguishes between model‑specific issues and systematic harness deficiencies by aggregating failures across multiple task instances and uses Failure‑Driven Collaborative Refinement to diagnose and fix harness problems. The approach reduces training time by up to 1.84× and improves harness reasoning accuracy by 18.56%.
arXiv AI
6d ago
Ecdysis: Efficient and Effective Training of Runtime Harnesses for LLM Agents
Ecdysis is a framework for training runtime harnesses for large language model agents that reduces training time and improves performance. It distinguishes between model‑specific issues and systematic harness deficiencies by aggregating failures across multiple task instances and uses Failure‑Driven Collaborative Refinement to diagnose and correct harness problems. Experiments show up to a 1.84× speedup in harness training and an 18.56% increase in reasoning accuracy.
arXiv AI
Sep 2
HarnessEvolve: Learning from Reference Trajectories for Reliable Agent Self-Evolution
HarnessEvolve is a self‑evolving framework that improves agent harnesses—prompts, skills, tools, and execution logic—by learning from reference trajectories. It separates execution, evaluation, optimization, and gating into independent modules, addressing credit assignment failure, shortcut learning, and catastrophic forgetting. The approach uses reference trajectories to extract error signals, applies quality and performance gates to candidate updates, and validates updates on held‑out data, consistently outperforming state‑of‑the‑art baselines across diverse benchmarks.