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

Paper recorded by Signals 4 on 2026-10-26 in cs.AI. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-26 on arXiv · recorded by Signals 4 on 2026-10-27

Category: cs.AI · 人工智能 · first seen 2026-10-27

Abstract

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 throu

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#146 most recent of 960 cs.AI papers we have recorded · ↑ newer: I-HITL: Inalienable Human-in-the-Loop · ↓ older: Reva: Real-Time Vulnerability Detection through Contextual Probing of
Cite this page: Profiling a Frontier Agent with EBench: From Direct Control to Harness with Policy: the #146 most recent of 960 cs.AI papers we have recorded (as of 2026-10-26). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/profiling-a-frontier-agent-with-ebench-from-direct-control-to-harness-with-polic.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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