5 papers
Test-time Reinforcement Learning for Anomalous Video Understanding
Huining Li, Yuxiang Duan, Jiyang Tan +5
Anomalous video understanding aims to identify abnormal events in videos and interpret their semantic meanings beyond simple anomaly detection. Recent video large language models (…
KnowHal: A Knowledge-Driven Benchmark for Comprehensive Multimodal Hallucination Evaluation
Ruihan Li, Jiyang Tan, Kailin Jiang +5
Hallucination remains a critical challenge for developing trustworthy Multimodal Large Language Models (MLLMs). While existing benchmarks mainly focus on entity, attribute, and rel…
GraphDx: A Cost-Aware Knowledge-Enhanced Multi-Agent Framework for Sequential Diagnosis
Shaoting Tan, Ning Liu, Yuntao Du +7
Sequential diagnosis requires balancing diagnostic accuracy against resource costs through iterative information gathering. Existing Large Language Model (LLM) approaches exhibit a…
Delineating Knowledge Boundaries for Honest Large Vision-Language Models
Junru Song, Yimeng Hu, Yijing Chen +4
Large Vision-Language Models (VLMs) have achieved remarkable multimodal performance yet remain prone to factual hallucinations, particularly in long-tail or specialized domains. Mo…
Can Visual Input Be Compressed? A Visual Token Compression Benchmark for Large Multimodal Models
Tianfan Peng, Yuntao Du, Pengzhou Ji +9
Large multimodal models (LMMs) often suffer from severe inference inefficiency due to the large number of visual tokens introduced by image encoders. While recent token compression…