experience reconstruction 1large language models 1memory-augmented agents 1out-of-distribution robustness 1reinforcement learning 1
From the 1 of 5 linked papers with an AI index.
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cs.AI2026
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…
cs.AI2024
Planning with Multi-Constraints via Collaborative Language Agents
Cong Zhang, Derrick Goh Xin Deik, Dexun Li +2
The rapid advancement of neural language models has sparked a new surge of intelligent agent research. Unlike traditional agents, large language model-based agents (LLM agents) hav…
cs.AI2024
Aligning Crowd Feedback via Distributional Preference Reward Modeling
Dexun Li, Cong Zhang, Kuicai Dong +3
Deep Reinforcement Learning is widely used for aligning Large Language Models (LLM) with human preference. However, the conventional reward modelling is predominantly dependent on…