activity
20232026
most citedImgTrojan: Jailbreaking Vision-Language Models with ONE Image

10 citations · 18 across the 17 of their papers we have counts for

collaborators

18 papers

cs.AI2026

CodeMidas: Scaling Agentic Coding RL Environments from Code Itself

Bowen Ye, Lei Li, Shicheng Li +16

Training capable coding agents via reinforcement learning (RL) requires diverse tasks with reliable verifiers. Open-source codebases offer a rich source of such tasks, while existi…

cs.CV2026

Semantic Head Specialization Guides Hybrid ViT Attention for Multimodal LLMs

Chenhong He, Lei Li, Shicheng Li +5

Hybrid attention dominates frontier LLMs, yet Vision Transformers (ViTs) in multimodal LLMs lack a satisfactory hybrid design, with no consensus on why certain attention patterns w…

cs.CL2026

PersonaForge: Realistic Multi-Turn User Simulation for Agentic Systems

Hanglong Lv, Dawei Zhu, Lei Li +11

Large language models are increasingly used as agentic workflow executors, yet existing training data and benchmarks largely assume informationally complete, single-turn queries. O…

cs.AI2026

Claw-Eval: Towards Trustworthy Evaluation of Autonomous Agents

Bowen Ye, Rang Li, Qibin Yang +10

Large language models are increasingly deployed as autonomous agents for multi-step workflows in real-world software environments. However, existing agent benchmarks are limited by…

cs.CV2026

TimeChat-Captioner: Scripting Multi-Scene Videos with Time-Aware and Structural Audio-Visual Captions

Linli Yao, Yuancheng Wei, Yaojie Zhang +12

This paper proposes Omni Dense Captioning, a novel task designed to generate continuous, fine-grained, and structured audio-visual narratives with explicit timestamps. To ensure de…

cs.AI2025

OS-Sentinel: Towards Safety-Enhanced Mobile GUI Agents via Hybrid Validation in Realistic Workflows

Qiushi Sun, Mukai Li, Zhoumianze Liu +11

Computer-using agents powered by Vision-Language Models (VLMs) have demonstrated human-like capabilities in operating digital environments like mobile platforms. While these agents…

cs.CV2025

TimeChat-Online: 80% Visual Tokens are Naturally Redundant in Streaming Videos

Linli Yao, Yicheng Li, Yuancheng Wei +11

The rapid growth of online video platforms, particularly live streaming services, has created an urgent need for real-time video understanding systems. These systems must process c…

cs.CV2025

TEMPLE: Incentivizing Temporal Understanding of Video Large Language Models via Progressive Pre-SFT Alignment

Shicheng Li, Lei Li, Kun Ouyang +7

Video Large Language Models (Video LLMs) have achieved significant success by adopting the paradigm of large-scale pre-training followed by supervised fine-tuning (SFT). However, e…

cs.CV2024★ 2 cited

VL-RewardBench: A Challenging Benchmark for Vision-Language Generative Reward Models

Lei Li, Yuancheng Wei, Zhihui Xie +9

Vision-language generative reward models (VL-GenRMs) play a crucial role in aligning and evaluating multimodal AI systems, yet their own evaluation remains under-explored. Current…

cs.CV2024

VLFeedback: A Large-Scale AI Feedback Dataset for Large Vision-Language Models Alignment

Lei Li, Zhihui Xie, Mukai Li +7

As large vision-language models (LVLMs) evolve rapidly, the demand for high-quality and diverse data to align these models becomes increasingly crucial. However, the creation of su…

cs.CL2024★ 1 cited

Understanding the Role of LLMs in Multimodal Evaluation Benchmarks

Botian Jiang, Lei Li, Xiaonan Li +5

The rapid advancement of Multimodal Large Language Models (MLLMs) has been accompanied by the development of various benchmarks to evaluate their capabilities. However, the true na…

cs.CL2024★ 1 cited

CoCA: Regaining Safety-awareness of Multimodal Large Language Models with Constitutional Calibration

Jiahui Gao, Renjie Pi, Tianyang Han +5

The deployment of multimodal large language models (MLLMs) has demonstrated remarkable success in engaging in conversations involving visual inputs, thanks to the superior power of…

cs.CV2024

Temporal Reasoning Transfer from Text to Video

Lei Li, Yuanxin Liu, Linli Yao +6

Video Large Language Models (Video LLMs) have shown promising capabilities in video comprehension, yet they struggle with tracking temporal changes and reasoning about temporal rel…

cs.AI2024

TMGBench: A Systematic Game Benchmark for Evaluating Strategic Reasoning Abilities of LLMs

Haochuan Wang, Xiachong Feng, Lei Li +4

The rapid advancement of large language models has accelerated their application in reasoning, with strategic reasoning drawing increasing attention. To evaluate the strategic reas…

cs.LG2024

Jailbreaking as a Reward Misspecification Problem

Zhihui Xie, Jiahui Gao, Lei Li +3

The widespread adoption of large language models (LLMs) has raised concerns about their safety and reliability, particularly regarding their vulnerability to adversarial attacks. I…

cs.CV2024★ 10 cited

ImgTrojan: Jailbreaking Vision-Language Models with ONE Image

Xijia Tao, Shuai Zhong, Lei Li +2

There has been an increasing interest in the alignment of large language models (LLMs) with human values. However, the safety issues of their integration with a vision module, or v…

cs.CV2024★ 1 cited

Multimodal ArXiv: A Dataset for Improving Scientific Comprehension of Large Vision-Language Models

Lei Li, Yuqi Wang, Runxin Xu +4

Large vision-language models (LVLMs) excel across diverse tasks involving concrete images from natural scenes. However, their ability to interpret abstract figures, such as geometr…

cs.CV2023★ 3 cited

Silkie: Preference Distillation for Large Visual Language Models

Lei Li, Zhihui Xie, Mukai Li +6

This paper explores preference distillation for large vision language models (LVLMs), improving their ability to generate helpful and faithful responses anchoring the visual contex…