Junyi Li

Recent advances in large language models (LLMs) have scaled the potential for reasoning and agentic search, wherein models autonomously plan, retrieve, and reason over external knowledge to answer complex queries. However, the iterative think–search loop accumulates long system memories, leading...

Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training methods largely rely on random masking strategies that overlook intrinsic token dependencies. In this work, we present an empirical analysis of att...

Combinatorial optimization has long been dominated by manually engineered heuristics, a paradigm requiring substantial expert intuition and implementation overhead. The advent of Large Language Models has disrupted this landscape, enabling the autonomous synthesis and optimization of algorithms....