collaborators

7 papers

cs.AI2026

OntoAR-HAR: Ontology-Guided Agentic RAG with Personalized Memory for Human Activity Recognition

Xiangqing Lu, Tao Zhu, Liang Yu +6

LLMs increasingly support sensor-based human activity recognition (HAR) by linking motion signals with activity semantics and physical knowledge. Retrieval-augmented generation (RA…

cs.RO2026

RMMBench: A Comprehensive Benchmark for Robotic Mobile Manipulation

Huapeng Li, Fuxiang Feng, Jinqiu Fan +5

Although the advancement of vision-language models (VLMs) has endowed robots with enhanced environmental understanding and task reasoning, a comprehensive evaluation methodology is…

cs.SE2026

Asuka-Bench: Benchmarking Code Agents on Underspecified User Intent and Multi-Round Refinement

Xin Wang, Liangtai Sun, Yaoming Zhu +8

Existing code-generation benchmarks score a single mapping from a complete prompt to a one-shot output. However, real web development is different. Users seldom write a full spec a…

cs.CV2026

WBench: A Comprehensive Multi-turn Benchmark for Interactive Video World Model Evaluation

Kaining Ying, Hengrui Hu, Siyu Ren +6

Interactive world models are advancing rapidly, yet existing benchmarks cover only part of the required competencies, leaving no unified standard for systematic evaluation. To fill…

cs.CL2025

UniHetero: Could Generation Enhance Understanding for Vision-Language-Model at Large Data Scale?

Fengjiao Chen, Minhao Jing, Weitao Lu +3

Vision-language large models are moving toward the unification of visual understanding and visual generation tasks. However, whether generation can enhance understanding is still u…

cs.CV2025

EvalTalker: Learning to Evaluate Real-Portrait-Driven Multi-Subject Talking Humans

Yingjie Zhou, Xilei Zhu, Siyu Ren +11

Speech-driven Talking Human (TH) generation, commonly known as "Talker," currently faces limitations in multi-subject driving capabilities. Extending this paradigm to "Multi-Talker…

cs.CV2025

Q-REAL: Towards Realism and Plausibility Evaluation for AI-Generated Content

Shushi Wang, Zicheng Zhang, Chunyi Li +7

Quality assessment of AI-generated content is crucial for evaluating model capability and guiding model optimization. However, most existing quality assessment datasets and models…