2 citations · 3 across the 16 of their papers we have counts for
18 papers
LIBERO-RIG: A Hardware-Setup Robustness Benchmark for Vision-Language-Action Models
Andrea Yaoyun Cui, Hanyu Wang, Xinyi Wang +6
Robot policies are expected to generalize across deployment environments, including variations in the robot itself: cameras are bumped and remounted, calibrations drift, controller…
SEAR: Self-Evolving Agentic Robotics
Xinyi Wang, Bangzheng Li, Hanyu Wang +5
Frontier language-model agents can now improve robots on their own: they write control code, test it, and keep what works as skills and memories for the next task. We introduce SEA…
Context-Aware RL for Agentic and Multimodal LLMs
Peiyang Xu, Bangzheng Li, Sijia Liu +4
Large language models (LLMs) often fail when answering requires identifying a small but decisive piece of evidence within a long or complex context, such as a single line in a tool…
VisAnalog: A Diagnostic Suite for Visual Concept Transfer on Natural Images
Zhaonan Li, Kyle R. Chickering, Bangzheng Li +13
A useful test of visual concept learning is not just whether a model can recognize a concept in a single image, but whether it can preserve and manipulate concept-level properties…
Reinforced Attention Learning
Bangzheng Li, Jianmo Ni, Chen Qu +5
Post-training with Reinforcement Learning (RL) has substantially improved reasoning in Large Language Models (LLMs) via test-time scaling. However, extending this paradigm to Multi…
Unbiased Visual Reasoning with Controlled Visual Inputs
Zhaonan Li, Shijie Lu, Fei Wang +11
End-to-end Vision-language Models (VLMs) often answer visual questions by exploiting spurious correlations instead of causal visual evidence, and can become more shortcut-prone whe…
Latent Visual Reasoning
Bangzheng Li, Ximeng Sun, Jiang Liu +7
Multimodal Large Language Models (MLLMs) have achieved notable gains in various tasks by incorporating Chain-of-Thought (CoT) reasoning in language spaces. Recent work extends this…
QLIP: A Dynamic Quadtree Vision Prior Enhances MLLM Performance Without Retraining
Kyle R. Chickering, Bangzheng Li, Muhao Chen
Multimodal Large Language Models (MLLMs) encode images into visual tokens, aligning visual and textual signals within a shared latent space to facilitate crossmodal representation…
Diagnosing and Mitigating Modality Interference in Multimodal Large Language Models
Rui Cai, Bangzheng Li, Xiaofei Wen +2
Multimodal Large Language Models demonstrate strong performance on multimodal benchmarks, yet often exhibit poor robustness when exposed to spurious modality interference, such as…
Semantic-Clipping: Efficient Vision-Language Modeling with Semantic-Guidedd Visual Selection
Bangzheng Li, Fei Wang, Wenxuan Zhou +5
Vision-Language Models (VLMs) leverage aligned visual encoders to transform images into visual tokens, allowing them to be processed similarly to text by the backbone large languag…
MetaScientist: A Human-AI Synergistic Framework for Automated Mechanical Metamaterial Design
Jingyuan Qi, Zian Jia, Minqian Liu +15
The discovery of novel mechanical metamaterials, whose properties are dominated by their engineered structures rather than chemical composition, is a knowledge-intensive and resour…
FamiCom: Further Demystifying Prompts for Language Models with Task-Agnostic Performance Estimation
Bangzheng Li, Ben Zhou, Xingyu Fu +3
Language models have shown impressive in-context-learning capabilities, which allow them to benefit from input prompts and perform better on downstream end tasks. Existing works in…
Rewarded Region Replay (R3) for Policy Learning with Discrete Action Space
Bangzheng Li, Ningshan Ma, Zifan Wang
We introduce a new on-policy algorithm called Rewarded Region Replay (R3), which significantly improves on PPO in solving environments with discrete action spaces. R3 improves samp…
Red Teaming Language Models for Processing Contradictory Dialogues
Xiaofei Wen, Bangzheng Li, Tenghao Huang +1
Most language models currently available are prone to self-contradiction during dialogues. To mitigate this issue, this study explores a novel contradictory dialogue processing tas…
BLINK: Multimodal Large Language Models Can See but Not Perceive
Xingyu Fu, Yushi Hu, Bangzheng Li +7
We introduce Blink, a new benchmark for multimodal language models (LLMs) that focuses on core visual perception abilities not found in other evaluations. Most of the Blink tasks c…
Cognitive Overload: Jailbreaking Large Language Models with Overloaded Logical Thinking
Nan Xu, Fei Wang, Ben Zhou +3
While large language models (LLMs) have demonstrated increasing power, they have also given rise to a wide range of harmful behaviors. As representatives, jailbreak attacks can pro…
Deceptive Semantic Shortcuts on Reasoning Chains: How Far Can Models Go without Hallucination?
Bangzheng Li, Ben Zhou, Fei Wang +3
Despite the recent advancement in large language models (LLMs) and their high performances across numerous benchmarks, recent research has unveiled that LLMs suffer from hallucinat…
Affective and Dynamic Beam Search for Story Generation
Tenghao Huang, Ehsan Qasemi, Bangzheng Li +4
Storytelling's captivating potential makes it a fascinating research area, with implications for entertainment, education, therapy, and cognitive studies. In this paper, we propose…