3 citations · 6 across the 10 of their papers we have counts for
12 papers
Beyond Timestamps: Decision-Aligned On-Policy Distillation for Long-Horizon Agents
Mingju Chen, Can Lv, Jinrong Liu +3
Reinforcement learning with verifiable rewards (RLVR) often relies on sparse outcome rewards, providing coarse supervision for long-horizon agents. On-policy self-distillation (OPS…
Cascade: Hierarchical Recoverability Control for Large Language Model Unlearning
Qingchen Yu, Shiying Duan, Xiaodong Li +5
Large Language Model (LLM) unlearning is essential for removing sensitive or copyrighted knowledge while preserving general utility. Existing methods often leave residual knowledge…
TRCA: Transition-wise Rubric Credit Assignment for Long-horizon LLM Agents
Huan Zhang, Mingju Chen, Dongxu Zhou +5
Long-horizon large language model (LLM) agents are typically optimized with sparse terminal outcomes, making fine-grained credit assignment across multi-step interactions difficult…
Mitigating False Credit Propagation: Probabilistic Graphical Reward Aggregation for Rubric-Based Reinforcement Learning
Can Lv, Mingju Chen, Heng Chang +1
Rubric-based rewards are increasingly used for open-ended language model post-training, but criterion-level scores are often aggregated as independent utilities. This flat scalariz…
HarnessForge: Joint Harness and Policy Evolution for Adaptive Agent Systems
Mingju Chen, Can Lv, Guibin Zhang +2
LLM agents are increasingly expected to operate across heterogeneous task regimes that require distinct execution paradigms. This challenges fixed agent systems and motivates syste…
Image-to-Video Diffusion: From Foundations to Open Frontiers
Xianlong Wang, Wenbo Pan, Shijia Zhou +6
Diffusion-based \textit{image-to-video} (I2V) generation has become a central direction in generative models by turning a reference image, with optional conditions, into a temporal…
All-Mem: Agentic Lifelong Memory via Dynamic Topology Evolution
Can Lv, Heng Chang, Shengyu Tao +5
Lifelong interactive agents are expected to assist users over months or years, which requires continually writing long term memories while retrieving the right evidence for each ne…
A-MapReduce: Executing Wide Search via Agentic MapReduce
Mingju Chen, Guibin Zhang, Heng Chang +3
Contemporary large language model (LLM)-based multi-agent systems exhibit systematic advantages in deep research tasks, which emphasize iterative, vertically structured information…
Efficient Utility-Preserving Machine Unlearning with Implicit Gradient Surgery
Shiji Zhou, Tianbai Yu, Zhi Zhang +4
Machine unlearning (MU) aims to efficiently remove sensitive or harmful memory from a pre-trained model. The key challenge is to balance the potential tradeoff between unlearning e…
On the Limitations and Prospects of Machine Unlearning for Generative AI
Shiji Zhou, Lianzhe Wang, Jiangnan Ye +2
Generative AI (GenAI), which aims to synthesize realistic and diverse data samples from latent variables or other data modalities, has achieved remarkable results in various domain…
Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient
Yongliang Wu, Shiji Zhou, Mingzhuo Yang +6
Text-to-image diffusion models have achieved remarkable success in generating photorealistic images. However, the inclusion of sensitive information during pre-training poses signi…
Robust Multi-Task Learning with Excess Risks
Yifei He, Shiji Zhou, Guojun Zhang +5
Multi-task learning (MTL) considers learning a joint model for multiple tasks by optimizing a convex combination of all task losses. To solve the optimization problem, existing met…