2 citations · 4 across the 31 of their papers we have counts for
16 papers · 1 filter
Prospective Memory for Proactive LLM Agents
Yuanchen Bei, Zhining Liu, Tianxin Wei +6
Most memory systems for LLM agents are retrospective, preserving and retrieving past information to inform current decisions. Proactive agents, however, also require prospective me…
PolicyMem: Geometric Policy Memory for LLM Governance
Yuanchen Bei, Zhengzhang Chen, Yanjun Zhao +3
Effective LLM governance extends beyond detecting unsafe responses to guiding their correction through closed-loop assessment, revision, and verification. Learning-based guards pro…
Predict, Don't Iterate: Efficient Adaptive-Length Infilling for Diffusion Language Models
Haobo Xu, Sirui Chen, Yuanchen Bei +5
Diffusion language models (DLMs) have emerged as a promising alternative to the auto-regressive paradigm. With bidirectional attention and any-order generation, DLMs naturally fit…
Beyond LLM-Based Reasoning: Lightweight GNNs for Agent Failure Attribution
Ting-Wei Li, Yuanchen Bei, Xiao Lin +1
Large language model (LLM)-based multi-agent systems (MAS) often exhibit complex failure modes, which frequently cause agents to produce incorrect outcomes. This motivates the task…
TAG-DLM: Diffusion Language Models for Text-Attributed Graph Learning
Lingjie Chen, Yuanchen Bei, Haobo Xu +3
Text-attributed graphs (TAGs), where each node carries a natural language description, require models to jointly reason over text and graph topology. Existing approaches often hand…
Code as Agent Harness
Xuying Ning, Katherine Tieu, Dongqi Fu +39
Recent large language models (LLMs) have demonstrated strong capabilities in understanding and generating code, from competitive programming to repository-level software engineerin…
VerifyMAS: Hypothesis Verification for Failure Attribution in LLM-based Multi-Agent Systems
Hezhe Qiao, Hanghang Tong, Ee-Peng Lim +2
Large language model-driven multi-agent systems (LLM-MAS) excel at complex tasks, yet unreliable agents remain a key bottleneck to system-level reliability. Automatic failure attri…
RubricEM: Meta-RL with Rubric-guided Policy Decomposition beyond Verifiable Rewards
Gaotang Li, Bhavana Dalvi Mishra, Zifeng Wang +9
Deep research agents plan, search, evaluate evidence, and synthesize long-form reports. Training these agents with reinforcement learning (RL) is challenging because they operate o…
EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation
Ting-Wei Li, Sirui Chen, Jiaru Zou +4
Adapting large language models (LLMs) to a targeted task efficiently and effectively remains a fundamental challenge. Such adaptation often requires iteratively improving the model…
TRIMS: Trajectory-Ranked Instruction Masked Supervision for Diffusion Language Models
Lingjie Chen, Ruizhong Qiu, Yuyu Fan +2
Diffusion language models (DLMs) offer a promising path toward low-latency generation through parallel decoding, but their practical efficiency depends heavily on the decoding traj…
Prune as You Generate: Online Rollout Pruning for Faster and Better RLVR
Haobo Xu, Sirui Chen, Ruizhong Qiu +5
Reinforcement Learning with Verifiable Rewards (RLVR) has significantly advanced the reasoning capabilities of Large Language Models (LLMs). However, methods such as GRPO and DAPO…
A Survey of Agent Memory in the Second Half: Towards Self-Evolving and Long-Horizon Agents
Wei-Chieh Huang, Weizhi Zhang, Yueqing Liang +56
Research in artificial intelligence is shifting from model innovations and benchmark scores towards problem definition and rigorous real-world evaluation. As the field enters the "…
Mem-Gallery: Benchmarking Multimodal Long-Term Conversational Memory for MLLM Agents
Yuanchen Bei, Tianxin Wei, Xuying Ning +7
Long-term memory is a critical capability for multimodal large language model (MLLM) agents, particularly in conversational settings where information accumulates and evolves over…
Latent Collaboration in Multi-Agent Systems
Jiaru Zou, Ruizhong Qiu, Gaotang Li +10
Multi-agent systems (MAS) extend large language models (LLMs) from independent single-model reasoning to coordinative system-level intelligence. While existing LLM agents depend on…
Beyond Log Likelihood: Probability-Based Objectives for Supervised Fine-Tuning across the Model Capability Continuum
Gaotang Li, Ruizhong Qiu, Xiusi Chen +2
Supervised fine-tuning (SFT) is the standard approach for post-training large language models (LLMs), yet it often shows limited generalization. We trace this limitation to its def…
RM-R1: Reward Modeling as Reasoning
Xiusi Chen, Gaotang Li, Ziqi Wang +9
Reward modeling is essential for aligning large language models with human preferences through reinforcement learning. To provide accurate reward signals, a reward model (RM) shoul…