4 citations · 4 across the 13 of their papers we have counts for
13 papers
SciToolAgent-Evo: An Ontology-Aware Self-Evolving Agent for Open-World Scientific Tool Acquisition
Yuqi Tang, Chenyi Zhou, Libin Wang +3
Large language model (LLM) agents have been increasingly adopted in scientific research for organizing and invoking specialized computational tools. However, their reliance on pred…
Beacon: Knowing When and How to Perform Agentic Visual Reasoning
Qixun Wang, Yang Shi, Letian Cheng +11
The fundamental goal of agentic visual reasoning is to improve the success rate of multimodal large language models (MLLMs) on complex tasks. We rethink agentic visual reasoning th…
RefCaptioner: Multi-Reference Image-Grounded Video Captioning
Tengfei Liu, Yang Shi, Yuran Wang +16
Existing video captioning models generate natural descriptions of video content but cannot explicitly ground local visual elements to multiple reference images. We introduce multi-…
KeyFrame-Compass: Towards Comprehensive Evaluation of Keyframe-Conditioned Video Generation
Yuqi Tang, Tengfei Liu, Yizheng Lai +18
Video generation increasingly relies on keyframe-based workflows, where creators specify a sequence of reference images to guide generation. Although recent models support multi-ke…
MultiRef-Compass: Towards Comprehensive Evaluation of Multi-Reference-to-Audio-Video Generation
Xiaohan Zhang, Yuqing Wen, Junlin Chen +9
Multi-reference-to-audio-video (MR2AV) generation aims to generate coherent audio-video content conditioned on multiple references and textual instructions. Existing benchmarks mai…
LongAV-Compass: Towards Unified Evaluation of Minute-Scale Audio-Visual Generation Across T2AV, I2AV, and V2AV
Tengfei Liu, Yang Shi, Xuanyu Zhu +17
Audio-visual generation is rapidly advancing from short clips to minute-long content, while existing evaluation protocols remain largely confined to short-form settings. Existing b…
Artifact-Bench: Evaluating MLLMs on Detecting and Assessing the Artifacts of AI-Generated Videos
Yuqi Tang, Yang Shi, Zhuoran Zhang +21
Recent video generative models have greatly improved the realism of AI-generated videos, yet their outputs still exhibit artifacts such as temporal inconsistencies, structural dist…
Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLMs via Interpretable Bi-Causal Steering
Shuliang Liu, Songbo Yang, Dong Fang +7
Object hallucination critically undermines the reliability of Multimodal Large Language Models, often stemming from a fundamental failure in cognitive introspection, where models b…
ClinDEF: A Dynamic Evaluation Framework for Large Language Models in Clinical Reasoning
Yuqi Tang, Jing Yu, Zichang Su +7
Clinical diagnosis begins with doctor-patient interaction, during which physicians iteratively gather information, determine examination and refine differential diagnosis through p…
LightMem: Lightweight and Efficient Memory-Augmented Generation
Jizhan Fang, Xinle Deng, Haoming Xu +9
Despite their remarkable capabilities, Large Language Models (LLMs) struggle to effectively leverage historical interaction information in dynamic and complex environments. Memory…
Learning an Efficient Multi-Turn Dialogue Evaluator from Multiple LLM Judges
Yuqi Tang, Kehua Feng, Yunfeng Wang +6
Evaluating the conversational abilities of large language models (LLMs) remains a challenging task. Current mainstream approaches primarily rely on the "LLM-as-a-judge" paradigm, w…
SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models
Jing Yu, Yuqi Tang, Kehua Feng +8
Large Language Models (LLMs) have shown impressive capabilities in contextual understanding and reasoning. However, evaluating their performance across diverse scientific domains r…
SciKnowEval: Evaluating Multi-level Scientific Knowledge of Large Language Models
Kehua Feng, Xinyi Shen, Weijie Wang +4
Large language models (LLMs) are playing an increasingly important role in scientific research, yet there remains a lack of comprehensive benchmarks to evaluate the breadth and dep…