6 citations · 8 across the 12 of their papers we have counts for
7 papers · 1 filter
Capability-Driven Self-Evolution of Agent Memory
Yaoqi Chen, Yuru Feng, Qianxi Zhang +8
Memory self-evolution uses task feedback to iteratively improve executable memory programs that store and retrieve information from past interactions. Existing approaches typically…
MESA:Task-Adaptive Multi-Structure Evidence Selection for Long-Horizon Agent Memory
Beidi Zhao, Yaoqi Chen, Yuru Feng +10
Long-horizon agents accumulate trajectories spanning hundreds of interleaved reasoning, action, and observation steps, where answering a query may depend on evidence buried far bac…
DMG: A Scalable and Efficient Memory-Disaggregated Graph Processing System
Yizou Chen, Tsun-Yu Yang, Zhisheng Hu +2
Traditional graph processing systems are built on monolithic servers, which couple a fixed ratio of compute and memory resources but often result in resource under-utilization in d…
Beyond Semantic Organization: Memory as Execution State Management for Long-Horizon Agents
Yaoqi Chen, Haibin Lai, Yuru Feng +12
LLM-based agents increasingly tackle long-horizon tasks with interdependent decisions, where each action reshapes future constraints and intermediate errors can cascade. Existing R…
Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving
Zihan Zhao, Baotong Lu, Shengjie Lin +8
Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent s…
KEEP: A KV-Cache-Centric Memory Management System for Efficient Embodied Planning
Zebin Yang, Tong Xie, Baotong Lu +3
Memory-augmented Large Language Models (LLMs) have demonstrated remarkable capability for complex and long-horizon embodied planning. By keeping track of past experiences and envir…
DualSpec: Accelerating Deep Research Agents via Dual-Process Action Speculation
Shuzhang Zhong, Baotong Lu, Qi Chen +3
Large language model-based deep research agents have been increasingly popular for addressing long-horizon information-seeking tasks, but they often incur high end-to-end latency d…