25 citations · 147 across the 70 of their papers we have counts for
76 papers
Cross-Embodiment Causal Transfer from Human Videos to Robot Manipulation
Xingyu Zhang, Yingchao Li, Peizheng Guo +3
Scaling robot manipulation models requires large amounts of interaction data, yet collecting diverse real-robot experience remains costly. Human manipulation videos, in contrast, p…
CAG-WAM: Causal Action-Gain World Action Models for Robot Manipulation
Peizheng Guo, Cheng Zhang, Xingyu Zhang +2
World Action Models (WAMs) combine action generation with future prediction, allowing predicted futures to support action evaluation and robot decision making. However, the future…
GAPrompt++: Multi-Granular Geometry-Aware Point Cloud Prompt for 3D Vision Model
Zixiang Ai, Zhenyu Cui, Yufei Guo +4
Pre-trained 3D vision models have substantially advanced point cloud analysis, yet adapting them to downstream tasks via full fine-tuning is computationally expensive and storage-i…
Hyper-LLaVA: Hyperbolic Uncertainty-aware Modality-Balanced Routing for Multimodal Continual Instruction Tuning
Kunlun Xu, Yanqin Zhang, Wenwen Qiang +1
Multimodal Continual Instruction Tuning (MCIT) aims to exploit the incrementally accumulated knowledge to process multimodal inputs of diverse tasks, where parameter routing plays…
GUPO: Gradient Uncertainty-aware Policy Optimization for Post-Training Large Language Models
Peizheng Guo, Jianqi Zhang, Xingyu Zhang +4
Group Relative Policy Optimization (GRPO) has become a widely used approach for post-training Large Language Models (LLMs) for reasoning. In GRPO, the group gradients induced by di…
Temporal GRPO: Beyond Trajectory-Level Credit in Vision-Language-Action Reinforcement Learning
Yao Zhou, Hang Gao, Fengge Wu +2
Outcome-driven reinforcement learning offers a scalable way to post-train vision-language-action (VLA) policies from sparse task-success feedback. In common GRPO-based VLA post-tra…
Ground-Truth Neighborhood Regularization for Reinforcement Learning Post-Training of Time Series Foundation Models
Jianqi Zhang, Xingyu Zhang, Zeen Song +3
Time series forecasting (TSF) plays an important role in a wide range of real-world applications. Recently, time series foundation models (TSFMs), pretrained on large-scale dataset…
Dirichlet-Guided Group Forecasting for Alleviating Over-smoothing in Time Series Forecasting
Xingyu Zhang, Jingyao Wang, Xin Yu +4
Time series forecasting often suffers from over-smoothing, especially when future dynamics are multi-modal. Forecasts may follow the coarse trend of the observed future, but fail t…
PAPO-VLA: Planning-Aware Policy Optimization for Vision-Language-Action Models
Peizheng Guo, Jingyao Wang, Changwen Zheng +1
Vision-Language-Action (VLA) models show promising ability in language-guided robotic tasks. However, making VLA policies reliable remains challenging, because a manipulation task…
CAMD: Coverage-Aware Multimodal Decoding for Efficient Reasoning of Multimodal Large Language Models
Huijie Guo, Jingyao Wang, Lingyu Si +3
Recent advances in Multimodal Large Language Models (MLLMs) have shown impressive reasoning capabilities across vision-language tasks, yet still face the challenge of compute-diffi…
From Shallow to Deep: Pinning Semantic Intent via Causal GRPO
Shuyi Zhou, Zeen Song, Wenwen Qiang +4
Large Language Models remain vulnerable to adversarial prefix attacks (e.g., ``Sure, here is'') despite robust standard safety. We diagnose this vulnerability as Shallow Safety Ali…
Adaptive Uncertainty-Aware Tree Search for Robust Reasoning
Zeen Song, Zihao Ma, Wenwen Qiang +2
Inference-time reasoning scaling has significantly advanced the capabilities of Large Language Models (LLMs) in complex problem-solving. A prevalent approach involves external sear…
On the Plasticity and Stability for Post-Training Large Language Models
Wenwen Qiang, Ziyin Gu, Jiahuan Zhou +4
Training stability remains a critical bottleneck for Group Relative Policy Optimization (GRPO), often manifesting as a trade-off between reasoning plasticity and general capability…
Causal Front-Door Adjustment for Robust Jailbreak Attacks on LLMs
Yao Zhou, Zeen Song, Wenwen Qiang +4
Jailbreak attacks on safety-aligned LLMs are increasingly effective, yet attack success alone does not reveal whether compliance is driven by preserved task intent, suppressed safe…
Towards Generalizable Reasoning: Group Causal Counterfactual Policy Optimization for LLM Reasoning
Jingyao Wang, Peizheng Guo, Wenwen Qiang +4
Large language models (LLMs) excel at complex tasks with advances in reasoning capabilities. However, existing reward mechanisms remain tightly coupled to final correctness and pay…
Enhancing Large Language Models for Time-Series Forecasting via Vector-Injected In-Context Learning
Jianqi Zhang, Jingyao Wang, Wenwen Qiang +2
The World Wide Web needs reliable predictive capabilities to respond to changes in user behavior and usage patterns. Time series forecasting (TSF) is a key means to achieve this go…
Exploring Transferability of Self-Supervised Learning by Task Conflict Calibration
Huijie Guo, Jingyao Wang, Peizheng Guo +3
In this paper, we explore the transferability of SSL by addressing two central questions: (i) what is the representation transferability of SSL, and (ii) how can we effectively mod…
A Generalized Learning Framework for Self-Supervised Contrastive Learning
Lingyu Si, Jingyao Wang, Wenwen Qiang
Self-supervised contrastive learning (SSCL) has recently demonstrated superiority in multiple downstream tasks. In this paper, we generalize the standard SSCL methods to a Generali…
Group Causal Policy Optimization for Post-Training Large Language Models
Ziyin Gu, Jingyao Wang, Ran Zuo +4
Recent advances in large language models (LLMs) have broadened their applicability across diverse tasks, yet specialized domains still require targeted post training. Among existin…
Causal Reward Adjustment: Mitigating Reward Hacking in External Reasoning via Backdoor Correction
Ruike Song, Zeen Song, Huijie Guo +1
External reasoning systems combine language models with process reward models (PRMs) to select high-quality reasoning paths for complex tasks such as mathematical problem solving.…
COPO: Causal-Oriented Policy Optimization for Hallucinations of MLLMs
Peizheng Guo, Jingyao Wang, Wenwen Qiang +3
Despite Multimodal Large Language Models (MLLMs) having shown impressive capabilities, they may suffer from hallucinations. Empirically, we find that MLLMs attend disproportionatel…
On the Transferability and Discriminability of Repersentation Learning in Unsupervised Domain Adaptation
Wenwen Qiang, Ziyin Gu, Lingyu Si +4
In this paper, we addressed the limitation of relying solely on distribution alignment and source-domain empirical risk minimization in Unsupervised Domain Adaptation (UDA). Our in…
Reward Model Generalization for Compute-Aware Test-Time Reasoning
Zeen Song, Wenwen Qiang, Siyu Zhao +2
External test-time reasoning enhances large language models (LLMs) by decoupling generation and selection. At inference time, the model generates multiple reasoning paths, and an a…
On the Out-of-Distribution Generalization of Self-Supervised Learning
Wenwen Qiang, Jingyao Wang, Zeen Song +2
In this paper, we focus on the out-of-distribution (OOD) generalization of self-supervised learning (SSL). By analyzing the mini-batch construction during the SSL training phase, w…