Zouying Cao

While natural language is the de facto communication medium for LLM-based agents, it presents a fundamental constraint. The process of downsampling rich, internal latent states into discrete tokens inherently limits the depth and nuance of information that can be transmitted, thereby hindering co...

Procedural memory enables large language model (LLM) agents to internalize ”how-to” knowledge and thus reduce redundant trial-and-error. However, existing frameworks predominantly suffer from a ”passive accumulation” paradigm, treating memory as a static append-only archive. To bridge the gap bet...

Large Language Models (LLMs) exhibit strong reasoning abilities for planning long-horizon, real-world tasks, yet existing agent benchmarks focus on task completion while neglecting time efficiency in parallel and asynchronous operations. To address this, we present ParaCook, a benchmark for time-...

Designing and optimizing multi-agent systems (MAS) is a complex, labor-intensive process of “Agent Engineering.” Existing automatic optimization methods, primarily focused on flat prompt tuning, lack the structural awareness to debug the intricate web of interactions in MAS. More critically, thes...