Robots can use historical observations to answer spatiotemporal queries in familiar environments, but the presence of relevant evidence does not imply that existing tools support the operations a task requires. We introduce ReSight, a task-driven self-improving memory system for embodied agents that expands the capabil...
Li-Rong Che, Yang Chen, Yi-Meng Su et al.· 2 citations· ⚡1
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...
S. Agashe, Yuzhe Yang, Gonzalo Gonzalez-Pumariega et al.· 0 citations
Pretrained vision-language-action models (VLAs) often fail midway through unseen long-horizon tasks. Recursive self-improvement (RSI) offers a remedy, but embodied RSI methods either refine the harness around a frozen executor and so cannot add a missing motion, or train the executor on persistent failures without aski...
Pei-Dong Wang, Ying Chang, Zhi-Ming Ma et al.· 4 citations
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...
Wen-Wen Zheng, Yuzhe Yang, Helen Qu et al.· 0 citations
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...
Jing-Xuan Wu, Yuzhe Yang, Yi-Qiao Huang et al.· 0 citations
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.
Jing-Xuan Wu, Zheng-Lin Wan, Xing-Rui Yu et al.· 4 citations
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.
Yang Chen, Li-Rong Che, Zhen-Yu Huang et al.· arXiv.org· 8 citations· ⚡2
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.
Yang Chen, Ye-Xin Xie, Li-Rong Che et al.· 0 citations
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