15 papers
VLA-Fabric: Learning Interaction for Composable Multi-Arm VLA Agents
Chenglong Zhang, Shuaijun Liu, Feiyang You +3
Vision-language-action (VLA) models are rapidly expanding what individual robots can accomplish, yet many real-world manipulation settings require multiple arms to operate as a coo…
MimicX: Policy-in-the-Loop Supervision Refinement for Video-Driven Humanoid Motion Tracking
Shuaijun Liu, Chenglong Zhang, Xuhao Liu +7
Human videos provide rich motion targets for humanoid learning, yet visually plausible references can still produce persistent failures under physics-based execution. These failure…
D-JEPA: A Decision-Aligned Latent World Model
Shuaijun Liu, Chengyu Wu, Qifu Wen +5
Latent world models predict the consequences of actions, but accurate prediction does not guarantee that latent distance reflects which candidate will execute successfully. We iden…
Beyond Appearance Shifts: Task-Semantic Action Calibration for VLA Models
Shuaijun Liu, Feiyang You, Chengyu Wu +6
Vision-language-action (VLA) models have achieved strong performance in embodied manipulation, but still struggle to distinguish between changes that should preserve behavior and t…
How Wrong Can a Good Predictor Be? Diverging Updates with Vanishing Predictive KL
Qifu Wen, Shuaijun Liu, Zihan Zhou +2
Accurate posterior prediction need not require accurate approximation of Bayesian updates. We prove that an unbounded gap between the update maps can coexist with vanishing predict…
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…
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…
Echo-LoRA: Parameter-Efficient Fine-Tuning via Cross-Layer Representation Injection
Yihang Peng, Peng Jin, Jie Gong +4
Parameter-efficient fine-tuning (PEFT) has become a practical route for adapting large language models to downstream tasks, with LoRA-style methods being particularly attractive be…
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 evaluated. Existing benchmarks exhibit two g…
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…
GradES: Significantly Faster Training in Transformers with Gradient-Based Early Stopping
Qifu Wen, Xi Zeng, Zihan Zhou +4
Early stopping monitors global validation loss and halts all parameter updates simultaneously, which is computationally costly for large transformers due to the extended time requi…
Discovering Hidden Algebraic Structures via Transformers with Rank-Aware Beam GRPO
Jaeha Lee, Gio Huh, Ning Su +1
Recent efforts have extended the capabilities of transformers in logical reasoning and symbolic computations. In this work, we investigate their capacity for non-linear latent patt…
Calibre: Towards Fair and Accurate Personalized Federated Learning with Self-Supervised Learning
Sijia Chen, Ningxin Su, Baochun Li
In the context of personalized federated learning, existing approaches train a global model to extract transferable representations, based on which any client could train personali…