12 papers
Evaluating the Hidden Costs of Personalization in Large Language Models
Yumeng Wang, Yuchen Wu, Cheng Qian +6
While Large language models (LLMs) incorporate user personalization signals to improve usability and helpfulness, they increasingly shift from providing balanced, informative respo…
Code2Math: Can Your Code Agent Effectively Evolve Math Problems Through Exploration?
Dadi Guo, Yuejin Xie, Qingyu Liu +11
As large language models (LLMs) advance their mathematical capabilities toward the IMO and research level, the scarcity of challenging, high-quality problems has become a significa…
Empowering Reliable Visual-Centric Instruction Following in MLLMs
Weilei He, Feng Ju, Zhiyuan Fan +3
Evaluating the instruction-following (IF) capabilities of Multimodal Large Language Models (MLLMs) is essential for rigorously assessing how faithfully model outputs adhere to user…
Environment Scaling for Interactive Agentic Experience Collection: A Survey
Yuchen Huang, Sijia Li, Minghao Liu +5
LLM-based agents can autonomously accomplish complex tasks across various domains. However, to further cultivate capabilities such as adaptive behavior and long-term decision-makin…
Towards Self-Evolving Benchmarks: Synthesizing Agent Trajectories via Test-Time Exploration under Validate-by-Reproduce Paradigm
Dadi Guo, Tianyi Zhou, Dongrui Liu +8
Recent advances in large language models (LLMs) and agent system designs have empowered agents with unprecedented levels of capability. However, existing agent benchmarks are showi…
CultureCLIP: Empowering CLIP with Cultural Awareness through Synthetic Images and Contextualized Captions
Yuchen Huang, Zhiyuan Fan, Zhitao He +3
Pretrained vision-language models (VLMs) such as CLIP excel in general multimodal comprehension but often struggle to capture nuanced, context-dependent visual cues. This makes it…