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MemHarness: Memory Is Reconstructed, Not Replayed

14 upvotes · 30 JUL 2026 · Rong Wu, Daocheng Fu, Licheng Wen et al.

This paper proposes a new approach to memory-augmentation in large language model agents, allowing them to actively reconstruct and adapt past experiences to fit the current context, rather than simply replaying them. Practitioners might care because this approach can improve the robustness and intrinsic reasoning capabilities of agents in complex scenarios.

memory-augmented agentscontinual learningreconstructive learningreinforcement learninglanguage modelcontext-grounded guidance