Research Tools & Code·arXiv cs.CL·1d ago

RealClawBench: Live OpenClaw Benchmarks from Real Developer-Agent Sessions

RealClawBench shifts agent evaluation away from synthetic tasks toward actual developer workflows by reconstructing execution environments and building deterministic scorers from live OpenClaw sessions. This addresses a critical gap in how the field measures deployed agent performance: existing benchmarks miss the messy reality of underspecified requests, environment dependencies, and verification challenges that define production use. The 281-task dataset captures authentic distribution and difficulty, making it a meaningful calibration point for teams building and evaluating code agents in real conditions.

Modelwire context

Explainer

The harder engineering problem here isn't the 281 tasks themselves but the deterministic scorers: building verifiable pass/fail criteria from sessions that were never designed to be evaluated requires reconstructing environment state well enough that the scorer and the original developer would agree on the outcome. That reproducibility constraint is where most live-session benchmarks quietly fail.

This sits inside a dense week of agent evaluation work. SPADE-Bench (covered June 1) asked whether agents misrepresent their actions to operators, and AgentCL (also June 1) asked whether agents genuinely retain knowledge across tasks. RealClawBench is asking a more foundational question that precedes both: are the tasks we use to measure agents representative of what agents actually encounter? Without that baseline, scores on deception or continual learning benchmarks may be calibrated against a distribution that doesn't exist in production. The PROVE paper from June 2, which introduced 343 live tools for RL training, is the closest complement here, since both papers are pushing against the same synthetic-data ceiling from different directions, one on the training side and one on the evaluation side.

Watch whether teams building on OpenClaw adopt RealClawBench as a standard reporting requirement in the next two to three release cycles. Adoption by even one major code agent provider would confirm the field is treating live-session grounding as a credibility floor rather than an optional extra.

Coverage we drew on

Synthesize and Reward -- Reinforcement Learning for Multi-Step Tool Use in Live Environments · arXiv cs.CL

This analysis is generated by Modelwire’s editorial layer from our archive and the summary above. It is not a substitute for the original reporting. How we write it.

MentionsRealClawBench · OpenClaw

Read full story at arXiv cs.CL →(arxiv.org)

Modelwire Editorial

This synthesis and analysis was prepared by the Modelwire editorial team. We use advanced language models to read, ground, and connect the day’s most significant AI developments, providing original strategic context that helps practitioners and leaders stay ahead of the frontier.

Our mission How we write

Modelwire summarizes, we don’t republish. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.