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【论文速递】Profiling a Frontier Agent with EBench: From Direct Control to Harness with Poli

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华人空间新闻 新手上路 周二 10月 27, 2026 8:00 pm #1
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英文标题:Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy arXiv编号:2610.35832 作者:Yifei Yao, Yude Zou, Guanbo Jiao 来源:arXi…
【论文速递】Profiling a Frontier Agent with EBench: From Direct Control to Harness with Poli

英文标题:Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy

arXiv编号:2610.35832
作者:Yifei Yao, Yude Zou, Guanbo Jiao
来源:arXiv
原文链接:https://arxiv.org/abs/2610.35832

中文摘要:
Frontier multimodal large language models (MLLMs) can control robots without robot-specific training, but how their capabilities compare and interact with post-trained vision-language-action (VLA) and world action model (WAM) policies across manipulation demands remains to be thoroughly evaluated. In this report, we evaluate GPT-6-Astra on EBench as the core of an agent that controls a robot through an agent harness under the same visual and proprioceptive observation protocol as post-trained policies. We study two agent configurations: direct control, in which the agent generates waypoint commands directly, and harness with policy, in which it can also invoke a post-trained VLA or WAM policy for action execution. Under direct control, the agent achieves competitive overall performance relative to post-trained policies. It leads on short-horizon tasks and exhibits the least performance variation across generalization dimensions, whereas it underperforms post-trained policies on long-horizon tasks, tabletop manipulation, and high-precision contact-rich tasks. In the harness with policy, invoking a post-trained VLA or WAM combines the complementary strengths of both components, raising overall task success to the level of the strongest compared post-trained policies while substantially reducing token cost. However, performance remains below the per-task upper bound of the standalone components due to suboptimal policy invocation, fixed-instruction execution without subgoals, and grasp disruption when the agent resumes control. Overall, our study characterizes the capability boundary of a frontier MLLM agent and suggests directions for improving future agent harness design. Project page: https://internrobotics.github.io/EBench ... ch-Report/

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