8 citations · 17 across the 16 of their papers we have counts for
6 papers · 1 filter
PoseGen: In-Context LoRA Finetuning for Pose-Controllable Long Human Video Generation
Jingxuan He, Busheng Su, Finn Wong
Generating temporally coherent, long-duration videos with precise control over subject identity and movement remains a fundamental challenge for contemporary diffusion-based models…
CyberGym: Evaluating AI Agents' Real-World Cybersecurity Capabilities at Scale
Zhun Wang, Tianneng Shi, Jingxuan He +3
AI agents have significant potential to reshape cybersecurity, making a thorough assessment of their capabilities critical. However, existing evaluations fall short, because they a…
Type-Constrained Code Generation with Language Models
Niels Mündler, Jingxuan He, Hao Wang +3
Large language models (LLMs) have achieved notable success in code generation. However, they still frequently produce uncompilable output because their next-token inference procedu…
VERINA: Benchmarking Verifiable Code Generation
Zhe Ye, Zhengxu Yan, Jingxuan He +3
Large language models (LLMs) are increasingly integrated in software development, but ensuring correctness in LLM-generated code remains challenging and often requires costly manua…
Reasoning Models Can Be Effective Without Thinking
Wenjie Ma, Jingxuan He, Charlie Snell +3
Recent LLMs have significantly improved reasoning capabilities, primarily by including an explicit, lengthy Thinking process as part of generation. In this paper, we question wheth…
Progent: Securing AI Agents with Privilege Control
Tianneng Shi, Jingxuan He, Zhun Wang +4
AI agents interact with external environments through tool calls, exposing them to attacks like indirect prompt injection that can trigger unauthorized actions. Securing these agen…