7 papers
Delegation Intelligence in Deep Search: A Controllable Framework for Disentangled Capability Diagnosis
Xinhao Yao, Yuanzhuo Liu, Changhao Wang +6
Deep search is becoming a core capability of modern agent systems, yet it is typically evaluated solely based on end-to-end answer accuracy. This coupled evaluation paradigm entang…
DELTAMEM: Incremental Experience Memory for LLM Agents via Residual Trees
Haoran Tan, Zeyu Zhang, Zhicheng Cao +2
Large Language Model (LLM)-based agents increasingly rely on memory to learn from experiences over continual interactions. However, storing experiences as independent, flat units l…
From Coarse to Fine: Self-Adaptive Hierarchical Planning for LLM Agents
Haoran Tan, Zeyu Zhang, Chen Ma +3
Large language model-based agents have recently emerged as powerful approaches for solving dynamic and multi-step tasks. Most existing agents employ planning mechanisms to guide lo…
Explicit v.s. Implicit Memory: Exploring Multi-hop Complex Reasoning Over Personalized Information
Zeyu Zhang, Yang Zhang, Haoran Tan +2
In large language model-based agents, memory serves as a critical capability for achieving personalization by storing and utilizing users' information. Although some previous studi…
GenSim: A General Social Simulation Platform with Large Language Model based Agents
Jiakai Tang, Heyang Gao, Xuchen Pan +11
With the rapid advancement of large language models (LLMs), recent years have witnessed many promising studies on leveraging LLM-based agents to simulate human social behavior. Whi…
MemBench: Towards More Comprehensive Evaluation on the Memory of LLM-based Agents
Haoran Tan, Zeyu Zhang, Chen Ma +3
Recent works have highlighted the significance of memory mechanisms in LLM-based agents, which enable them to store observed information and adapt to dynamic environments. However,…