Survey
Research papers, repositories, and articles about survey
Showing 2 of 2 items
Memory in the Age of AI Agents
A substantial survey that systematizes the fast-growing literature on ‘agent memory’—how agentic LLM systems store, retrieve, and evolve information over time. It proposes a taxonomy across forms (token, parametric, latent), functions (factual, experiential, working) and dynamics, and catalogs existing benchmarks and frameworks. If you’re building agent systems with nontrivial memory, this is quickly becoming the reference map of the territory.
Rethinking Agentic Reinforcement Learning In Large Language Models
Synthesizes the fast-growing literature on reinforcement learning for agent-style language models, from environment design to safety and compute limits. Argues the key shift is treating models as long-lived decision-makers, not one-shot text generators. If you’re planning big training runs for agents, use this as a design checklist, not just a citation. ([databubble.co](https://databubble.co/news/rethinking-agentic-reinforcement-learning-in-large-language-models?utm_source=openai))