activity
20242026
collaborators

8 papers

cs.CL2026

Perception Without Engagement: Dissecting the Causal Discovery Deficit in LMMs

Jiafeng Liang, Zhihao Zhu, Zihan Zhang +7

Although Large Multimodal Models (LMMs) have achieved strong performance on general video understanding, their susceptibility to textual prior shortcuts during causal discovery has…

cs.AI2026

Schema-Aware Planning and Hybrid Knowledge Toolset for Reliable Knowledge Graph Triple Verification

Xinyan Ma, Xianhao Ou, Weihao Zhang +9

Knowledge Graphs (KGs) provide a critical foundation for many AI systems, yet their automated construction inevitably introduces noisy triples that undermine data trustworthiness.…

cs.CL2026

LoopRPT: Reinforcement Pre-Training for Looped Language Models

Guo Tang, Shixin Jiang, Heng Chang +7

Looped language models (LoopLMs) perform iterative latent computation to refine internal representations, offering an alternative to explicit chain-of-thought (CoT) reasoning. Howe…

cs.CV2026

Analyzing Reasoning Consistency in Large Multimodal Models under Cross-Modal Conflicts

Zhihao Zhu, Jiafeng Liang, Shixin Jiang +5

Large Multimodal Models (LMMs) have demonstrated impressive capabilities in video reasoning via Chain-of-Thought (CoT). However, the robustness of their reasoning chains remains qu…

cs.CL2025

AI Meets Brain: Memory Systems from Cognitive Neuroscience to Autonomous Agents

Jiafeng Liang, Hao Li, Chang Li +12

Memory serves as the pivotal nexus bridging past and future, providing both humans and AI systems with invaluable concepts and experience to navigate complex tasks. Recent research…

cs.CV2025

Investigating and Enhancing the Robustness of Large Multimodal Models Against Temporal Inconsistency

Jiafeng Liang, Shixin Jiang, Xuan Dong +7

Large Multimodal Models (LMMs) have recently demonstrated impressive performance on general video comprehension benchmarks. Nevertheless, for broader applications, the robustness o…

cs.AI2024

From Specific-MLLMs to Omni-MLLMs: A Survey on MLLMs Aligned with Multi-modalities

Shixin Jiang, Jiafeng Liang, Jiyuan Wang +6

To tackle complex tasks in real-world scenarios, more researchers are focusing on Omni-MLLMs, which aim to achieve omni-modal understanding and generation. Beyond the constraints o…

cs.CV2024

GUIDE: A Guideline-Guided Dataset for Instructional Video Comprehension

Jiafeng Liang, Shixin Jiang, Zekun Wang +7

There are substantial instructional videos on the Internet, which provide us tutorials for completing various tasks. Existing instructional video datasets only focus on specific st…