1 citations · 1 across the 6 of their papers we have counts for
7 papers
AesCode: Aesthetic Code Generation with Decoupled Cross-Modal Rewards
Xin Gao, Yifan Yang, Xin Li +13
Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable, and verifiable. Yet code mod…
ReDeck: Step-Level Render-Grounded Refinement for Document-to-Slide Generation
Muzhao Tian, Zezi Zeng, Yifan Yang +14
Document-to-slide generation is challenging because slides are dense editable artifacts that require both faithful content selection and precise spatial layout. Recent slide agents…
RESOURCE2SKILL: Distilling Executable Agent Skills from Human-Created Multimodal Resources
Yijia Fan, Zonglin Di, Zimo Wen +8
Skills are a useful abstraction for software agents, turning human and agent experience into reusable procedural knowledge. Yet existing skill libraries are mostly hand-written, te…
Toward Autonomous Laboratory Safety Monitoring with Vision Language Models: Learning to See Hazards Through Scene Structure
Trishna Chakraborty, Udita Ghosh, Aldair Ernesto Gongora +5
Laboratories are prone to severe injuries from minor unsafe actions, yet continuous safety monitoring -- beyond mandatory pre-lab safety training -- is limited by human availabilit…
HEAL: An Empirical Study on Hallucinations in Embodied Agents Driven by Large Language Models
Trishna Chakraborty, Udita Ghosh, Xiaopan Zhang +5
Large language models (LLMs) are increasingly being adopted as the cognitive core of embodied agents. However, inherited hallucinations, which stem from failures to ground user ins…
Adversarial Attacks on Parts of Speech: An Empirical Study in Text-to-Image Generation
G M Shahariar, Jia Chen, Jiachen Li +1
Recent studies show that text-to-image (T2I) models are vulnerable to adversarial attacks, especially with noun perturbations in text prompts. In this study, we investigate the imp…
LaMMA-P: Generalizable Multi-Agent Long-Horizon Task Allocation and Planning with LM-Driven PDDL Planner
Xiaopan Zhang, Hao Qin, Fuquan Wang +2
Language models (LMs) possess a strong capability to comprehend natural language, making them effective in translating human instructions into detailed plans for simple robot tasks…