10 papers
CoWAM: Coordination Contracts for Selective Policy Intervention with WAMs
Shuaijun Liu, Qifu Wen, Shuyang Hao +5
World Action Models (WAMs) augment robot policies with action-conditioned predicted futures, but a plausible future alone does not justify changing the action that a bimanual polic…
When Replanning Becomes the Bottleneck: Budgeted Replanning for Embodied Agents
Shuaijun Liu, Feiyang You, Xingwei Chen +1
Embodied agents replan frequently to recover from execution drift, partial observability, and coordination hazards, but each LLM-based replanning call can consume an accumulated te…
The Gate, Not the Cache: Gate Provenance Bounds the Closed-Loop Reliability of Training-Free VLA Token Skipping
Qi Luo, Shuaijun Liu, Hao Zhao +5
Token skipping is a widely used training-free way to accelerate vision--language--action (VLA) models by bypassing computation for most visual tokens at each control step according…
PortBench: A Correlation-Aware, Full-Pipeline Benchmark for LLM-Driven Portfolio Management
Yuxuan Zhao, Sijia Chen, Ningxin Su
Large language models (LLMs) have shown strong performance across diverse financial tasks, yet portfolio management (PM) remains poorly benchmarked. Existing benchmarks exhibit two…
When Does Multi-Agent Collaboration Help? An Entropy Perspective
Yuxuan Zhao, Sijia Chen, Ningxin Su
Multi-agent systems (MAS) have emerged as a prominent paradigm for leveraging large language models (LLMs) to tackle complex tasks. However, the mechanisms governing the effectiven…
EnvSimBench: A Benchmark for Evaluating and Improving LLM-Based Environment Simulation
Yi Liu, TingFeng Hui, Wei Zhang +4
Scalable AI agents training relies on interactive environments that faithfully simulate the consequences of agent actions. Manually crafted environments are expensive to build, bri…