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All SourcesarXiv
#retrieval

Memex(RL): Scaling Long-Horizon LLM Agents via Indexed Experience Memory

Beginner
Zhenting Wang, Huancheng Chen et al.Mar 4arXiv

This paper teaches long-horizon AI agents to remember everything exactly without stuffing their whole memory at once.

#indexed memory#LLM agents#long-horizon tasks

Exploratory Memory-Augmented LLM Agent via Hybrid On- and Off-Policy Optimization

Intermediate
Zeyuan Liu, Jeonghye Kim et al.Feb 26arXiv

This paper teaches a language-model agent to explore smarter by combining two ways of learning (on-policy and off-policy) with a simple, self-written memory.

#EMPO#memory-augmented agents#on-policy learning

LatentMem: Customizing Latent Memory for Multi-Agent Systems

Intermediate
Muxin Fu, Guibin Zhang et al.Feb 3arXiv

LatentMem is a new memory system that helps teams of AI agents remember the right things for their specific jobs without overloading them with text.

#multi-agent systems#latent memory#role-aware memory

Do Reasoning Models Enhance Embedding Models?

Intermediate
Wun Yu Chan, Shaojin Chen et al.Jan 29arXiv

The paper asks a simple question: if a language model becomes better at step-by-step reasoning (using RLVR), do its text embeddings also get better? The short answer is no.

#text embeddings#RLVR#contrastive learning