Xuxin Cheng

Large language models are increasingly deployed in multi-agent systems to overcome context limitations by distributing information across agents. However, whether LLM-based agents can reliably coordinate when each observes only a fragment of the global problem remains unclear. Existing benchmarks...

Autoregressive (AR) large audio language models (LALMs) such as Qwen-2.5-Omni have achieved strong performance on audio understanding and interaction, but scaling them remains costly in data and computation, and strictly sequential decoding limits inference efficiency. Diffusion large language mo...

Large vision–language models (LVLMs) excel at multimodal reasoning but still suffer from object-existence hallucinations when multi-step deliberation decouples from visual evidence. Think-with-Images (TwI) attempts to counter this by generating auxiliary observations (e.g., zoomed crops or highli...

Current LLM-based multi-agent systems remain fragile under scaling, even on algorithmically trivial tasks. We introduce MAS-BENCH, a distributed-sorting benchmark that isolates coordination under explicit communication constraints: each agent observes only a local segment and must collectively pr...