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The Chinese University of Hong Kong, Shenzhen
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Computer-use agents are limited by the interfaces humans give them. An agent may type a spreadsheet cell by cell when a single line of code would do, or print all two thousand lines of a file through a shell to find one function. Both the interfaces and the knowledge of when to use each are built by hand, which cannot...
Nontrivial dynamics can emerge in large language model (LLM)-based multi-agent systems, and preliminary evidence exists that formalisms from statistical mechanics can be effective at modeling and predicting such behaviors. In parallel, designing multi-agent communication topology for optimal task-solving is an active r...
An assistant that serves the same user over a long horizon has to answer from what that user has revealed: which preferences still hold, which were revised, and which constraints apply now. Retaining that information is not the same as acting on it, and the two are usually optimized as if they were. Keeping the informa...
Time-Annealed Perturbation Sampling (TAPS) is proposed, a training-free inference strategy that samples nearby conditioning trajectories through time-aware, manifold-constrained perturbations, and consistently improves semantic and lexical diversity across open-ended and instruction-following generation tasks.
This work introduces HarnessVLN, a zero-shot, training-free framework that unifies instruction-following and object-goal navigation through a shared Agent Harness and demonstrates its applicability to both navigation tasks in real-world environments.
CoBench is introduced, a construct-level benchmark for evaluating multi-agent embodied coordination in executable household tasks and shows that coordination ability is highly construct-specific: strong overall performance does not imply balanced competence across different coordination types.
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