From the 2 of 12 linked papers with an AI index.
12 papers
Quo Vadis, World Modeling?
Yu Yang, Xuemeng Yang, Licheng Wen +17
Continually improving agents require dynamic interaction feedback beyond static supervision, yet direct real-environment interaction is costly, slow, unsafe, and hard to paralleliz…
MemHarness: Memory Is Reconstructed, Not Replayed
Rong Wu, Daocheng Fu, Licheng Wen +10
The paper introduces MemHarness, a framework that lets large language model agents reconstruct and adapt retrieved past experiences to the current context instead of replaying them…
Proxy OPD: On-Policy Distillation with Transferable Relative Proxy Update
Daocheng Fu, Rong Wu, Yu Yang +7
The paper introduces PUST, a framework that uses a lightweight proxy model to explore high‑reward behaviors and then transfers the relative improvement signals to a larger primary…
SemFlowRAG: Directed Semantic Flow from Abstraction to Evidence for Complex Reasoning
Houyuan Qin, Rong Wu, Qinyuan Qin +4
Retrieval-Augmented Generation (RAG) enhanced by Knowledge Graphs has shown promise in complex multi-hop reasoning tasks. However, existing graph-based retrieval methods typically…
The Agent's First Day: Benchmarking Learning, Exploration, and Scheduling in the Workplace Scenarios
Daocheng Fu, Jianbiao Mei, Rong Wu +7
The rapid evolution of Multi-modal Large Language Models (MLLMs) has advanced workflow automation; however, existing research mainly targets performance upper bounds in static envi…
EvolveR: Self-Evolving LLM Agents through an Experience-Driven Lifecycle
Rong Wu, Xiaoman Wang, Jianbiao Mei +8
Current Large Language Model (LLM) agents show strong performance in tool use, but lack the crucial capability to systematically learn from their own experiences. While existing fr…