Zhijiang Guo

Learning high-level strategies can boost vulnerability reproduction success rates by over 20%, revolutionizing how we approach software security tasks.

The current RL checkpoint outperforms larger LLMs in redesigning training environments, revealing that iterative learning enhances diagnostic capabilities.

A single pair of boundary tokens transforms hidden-state reasoning into a trainable and interpretable framework, revealing causal insights that were previously obscured.

CoT fine-tuning can slash long-range recall by over 57% in hybrid LLMs, but a simple parameter restoration method can reverse this trend without additional training.

ReaLB achieves 1.29x faster multimodal MoE inference by dynamically adjusting expert precision, proving that real-time adaptation can overcome modality-induced load imbalances.

LLMs that ace code generation often fail to grasp intended program semantics, as evidenced by a stark performance decline when generating executable behavioral specifications on the new CodeSpecBench benchmark.