arXiv AI
EvoCowork: A Benchmark for Real-World Office Tasks
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv AI
Oct 5
VERSE: Verified Self-Evolving Optimizer for Agent Harnesses
The paper introduces VERSE, a Verified Self‑Evolving optimizer that enhances LLM agent harnesses by allowing the optimizer to test edits, replay failures, and perturb steps while tracking fixes and regressions. VERSE builds its own tools for failure analysis, verification, training audits, and workflow control, and uses this feedback to revise the harness’s prompts, skills, tools, hooks, and notes without changing model weights. In experiments across five executors and multiple languages, VERSE improves all evaluated harness optimizers, achieving higher accuracy on held‑out and out‑of‑distribution tasks compared to the strongest baselines.
arXiv AI
Oct 15
Harness^2: Recursive Agent Harnessing for an Open World
arXiv:2610.14224v1 Announce Type: new Abstract: LLM agents' capabilities depend not only on their underlying models but also on their execution harnesses. Recursive self-improvement (RSI) offers a pr...
arXiv AI
Oct 5
Scaling Trajectories for Complex Tasks through Recursive Self-Rewrite
arXiv:2610.02826v1 Announce Type: new Abstract: Successful trajectories on difficult tasks provide valuable supervision for model improvement, but specialized harnesses introduce interventions that m...
arXiv AI
Aug 11
Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses
arXiv:2608. 08466v1 Announce Type: new Abstract: Modern LLM agents are often improved by modifying prompts, tools, or workflows manually, while the executable scaffold surrounding the model---the \emph{harness}---is typically treated as a fixed artifact after deployment.
arXiv Computation and Language
Oct 12
Evo$^2$: Evolving the Evolver through Weight-Harness Co-Evolution
Evo$^2$ is a framework that jointly evolves a language model’s weights and its harness—the set of prompts, tools, and feedback mechanisms—using a dual-role pipeline where a single policy acts as both a Proposer and a Reflector. The training pipeline EvoTIDE combines supervised fine-tuning with interleaved GRPO to refine both roles based on harness edits and execution outcomes. Experiments on coding benchmarks show Evo$^2$ outperforms existing self‑evolution methods, achieving higher scores on Polyglot and SWE‑bench Verified, and its evolved harnesses transfer across tasks and models.
arXiv Machine Learning
Jun 11
Claw-SWE-Bench: A Benchmark for Evaluating OpenClaw-style Agent Harnesses on Coding Tasks
arXiv:2606. 12344v1 Announce Type: new Abstract: General-purpose agents such as OpenClaw are increasingly used as autonomous tool users, but their coding ability is difficult to measure under SWE-bench: a generic agent does not by itself satisfy the clean Docker workspace, patch, and prediction contract required for scoring.