3 papers
cs.AI2026
CaveAgent: Transforming LLMs into Stateful Runtime Operators
Maohao Ran, Zhenglin Wan, Cooper Lin +21
LLM-based agents are increasingly capable of complex task execution, yet current agentic systems remain constrained by text-centric paradigms that struggle with long-horizon tasks…
cs.CL2026
Time-Annealed Perturbation Sampling: Diverse Generation for Diffusion Language Models
Jingxuan Wu, Zhenglin Wan, Xingrui Yu +4
Diffusion language models (Diffusion-LMs) introduce an explicit temporal dimension into text generation, yet how this structure can be leveraged to control generation diversity for…
cs.CL2026
CCR-Bench: A Comprehensive Benchmark for Evaluating LLMs on Complex Constraints, Control Flows, and Real-World Cases
Xiaona Xue, Yiqiao Huang, Jiacheng Li +9
Enhancing the ability of large language models (LLMs) to follow complex instructions is critical for their deployment in real-world applications. However, existing evaluation metho…