6 papers
Reading is not Reasoning: Bridging the Agentic Policy Gap in Vision-Text Compression
Cheng Fan, Junyi Zhou, Tingzhang Luo +5
Multi-step language-model agents repeatedly process growing interaction histories, leading to substantial context costs. Vision--text compression reduces these costs by rendering h…
RoRA: Role-Oriented Regional Allocation for Visual Token Pruning in MLLMs
Qiyanhui Lu, Han Wu, Rongjian Xu +6
Multimodal large language models (MLLMs) encode images as long visual token sequences, making prefilling and KV-cache storage expensive. Existing training-free pruning methods sele…
From Question Answering to Task Completion: A Survey on Agent System and Harness Design
Jianyuan Guo, Zhiwei Hao, Chengcheng Wang +14
LLM-based agents mark a shift from passive question answering to active task completion: they perceive environments, invoke tools, maintain state, and act over extended horizons. A…
Efficient Hierarchical Implicit Flow Q-learning for Offline Goal-conditioned Reinforcement Learning
Zhiqiang Dong, Teng Pang, Rongjian Xu +1
Offline goal-conditioned reinforcement learning (GCRL) is a practical reinforcement learning paradigm that aims to learn goal-conditioned policies from reward-free offline data. De…
Equivariant Efficient Joint Discrete and Continuous MeanFlow for Molecular Graph Generation
Rongjian Xu, Teng Pang, Zhiqiang Dong +1
Graph-structured data jointly contain discrete topology and continuous geometry, which poses fundamental challenges for generative modeling due to heterogeneous distributions, inco…
Value-Guidance MeanFlow for Offline Multi-Agent Reinforcement Learning
Teng Pang, Zhiqiang Dong, Yan Zhang +3
Offline multi-agent reinforcement learning (MARL) aims to learn the optimal joint policy from pre-collected datasets, requiring a trade-off between maximizing global returns and mi…