Anh Lê

Yi Ru Wang, Carter Ung, Grant Tannert et al. · uw

We present RoboEval, a simulation benchmark and structured evaluation framework designed to reveal the limitations of current bimanual manipulation policies. While prior benchmarks report only binary task success, we show that such metrics often conceal critical weaknesses in policy behavior -- such as poor coordination, slipping during grasping, or asymmetric arm usage. RoboEval introduces a suite of tiered, semantically grounded tasks decomposed into skill-specific stages, with variations that systematically challenge spatial, physical, and coordination capabilities. Tasks are paired with fine-grained diagnostic metrics and 3000+ human demonstrations to support imitation learning. Our experiments reveal that policies with similar success rates diverge in how tasks are executed -- some struggle with alignment, others with temporally consistent bimanual control. We find that behavioral metrics correlate with success in over half of task-metric pairs, and remain informative even when binary success saturates. By pinpointing when and how policies fail, RoboEval enables a deeper, more actionable understanding of robotic manipulation -- and highlights the need for evaluation tools that go beyond success alone.

Hongcheng Guo, Zheyong Xie, Shaosheng Cao et al.

With the increasing integration of visual and textual content in Social Networking Services (SNS), evaluating the multimodal capabilities of Large Language Models (LLMs) is crucial for enhancing user experience, content understanding, and platform intelligence. Existing benchmarks primarily focus on text-centric tasks, lacking coverage of the multimodal contexts prevalent in modern SNS ecosystems. In this paper, we introduce SNS-Bench-VL, a comprehensive multimodal benchmark designed to assess the performance of Vision-Language LLMs in real-world social media scenarios. SNS-Bench-VL incorporates images and text across 8 multimodal tasks, including note comprehension, user engagement analysis, information retrieval, and personalized recommendation. It comprises 4,001 carefully curated multimodal question-answer pairs, covering single-choice, multiple-choice, and open-ended tasks. We evaluate over 25 state-of-the-art multimodal LLMs, analyzing their performance across tasks. Our findings highlight persistent challenges in multimodal social context comprehension. We hope SNS-Bench-VL will inspire future research towards robust, context-aware, and human-aligned multimodal intelligence for next-generation social networking services.