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LLM-based multi-agent systems (MASs) are increasingly used to solve complex tasks through coordinated reasoning, tool use, and interaction with external resources. However, attributing failures in such systems remains challenging because the observed outcome often does not directly reveal the error responsible for the...
Embodied Agents System (EAS) are increasingly deployed in open-world physical domains, where reliability directly dictates deployment quality and human-agent trust. However, existing evaluations rely on outcome-centric metrics as success rate or safety scores that collapse diverse execution trajectories into coarse sco...
A novel Energy-Structured Latent Latent World Model to structure the ELWM latent state to explicitly carry energy and momentum, ensuring strictly causal transitions via dissipation and control ports, and demonstrates that embedding explicit physical structures into latent spaces intrinsically bridges the gap between pr...
TreeRef is proposed, a tree-based molecular representation that assigns molecular topology and topology-dependent local 3D geometry to a naturally variable-size tree and TreeRef-BFN is developed, a Bayesian Flow Network with a standard Transformer backbone that globally couples these locally defined variables and joint...
This work proposes a requirement-oriented normalization method using controlled natural language and large-language-model-based prompt engineering to strengthen semantic representation and mitigate semantic divergence in source code requirements.
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.
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...
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