Physically consistent motion planning remains a fundamental challenge in embodied AI, as generated trajectories must strictly conform to real-world execution dynamics. While latent world models offer a promising approach by predicting these dynamics, existing methods learn unconstrained future representations where abs...
Yapeng Liu, Yuan-Zhao Zhai, Bo Ding et al.· 0 citations
Traceability link recovery between requirements and source code is vital for software quality assurance and evolution analysis. Although automated traceability techniques have advanced greatly, the large semantic gap between vague natural-language requirements and precise source code still hinders accurate link recover...
Luoyuan Shi, Yuan-Zhao Zhai, Da-Wei Feng et al.· 0 citations
A novel benchmark for evaluating the task-solving capabilities of LLM agents under dynamic toolset evolution, and proposes 11 mutation operators to simulate realistic tool evolution within 123 MCP servers, establishing MCPEvol-Bench as a standard for evaluating agent adaptability in dynamic tool environments.
Huanxi Liu, Kun Hu, Jiaqi Liao et al.· 0 citations
This paper examines audio self-supervised learning through the alignment between pretraining objectives, architectural inductive biases, and downstream applications, and relates these demands to the biases of CNNs, recurrent and State Space Models, Transformers, and hybrid architectures.
ReFrame is a training-free multimodal input reframing framework where two agents share a lightweight locally deployed MLLM: the evidence-generation agent constructs complementary risk and utility evidence, and the rewrite-and-routing agent converts it into a safe proxy prompt and image-routing decision before calling t...
Wenzheng Jiang, Xuan-Kun Rong, Yuan-Zhao Zhai et al.· 0 citations
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