arXiv Machine Learning
CoArena: Evaluating Computer-Use and Multi-Agent Systems in Real Time
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ClawProBench: Trace-Aware Evaluation of AI Agents with Runtime Coverage and Frozen Workplace-Style Holdouts
arXiv:2608.22510v1 Announce Type: new Abstract: Agent benchmarks often evaluate only final answers even when agents run on stateful runtimes. We argue this under-specifies what is being evaluated: th...
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Agent Evaluation Reliability: More Tasks Won't (Always) Fix An Agent Leaderboard
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K-Bench: measuring model performance on real scientific agent requests
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Ask the Tool, Don't Guess: Agent Tool Calls Hold Their Progress, and the Serving System Should Read It
The paper argues that agentic systems waste time and memory by guessing how long tool calls will take, rather than using explicit progress signals from the tools themselves. It demonstrates that tools can report their remaining work or imminent completion, and that incorporating this feedback into serving systems dramatically improves cache decisions and reduces token latency. The authors show that this approach outperforms existing predictors and works robustly across different environments.
arXiv AI
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Evaluating Skills, Not Just Agents: Agentic Continuous Evaluation of Skills
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