ReAct

What if your AI agent could remember the last time it made a mistake—and plan better this time? From Reaction to Reflection: Why Memory Matters Most language model agents today operate like goldfish—brilliant at reasoning in the moment, but forgetful. Whether navigating virtual environments, answering complex questions, or composing multi-step strategies, they often repeat past mistakes simply because they lack a memory of past episodes. That’s where the paper “Agentic Episodic Control” by Zhihan Xiong et al. introduces a critical upgrade to today’s LLM agents: a modular episodic memory system inspired by human cognition. Instead of treating each prompt as a blank slate, this framework allows agents to recall, adapt, and refine prior reasoning paths—without retraining the underlying model. ...

If ReAct was a spark, Pre-Act is a blueprint. In the paper Pre-Act: Multi-Step Planning and Reasoning Improves Acting in LLM Agents, Mrinal Rawat et al. challenge the single-step cognitive paradigm of ReAct, offering instead a roadmap for how agents should plan, reason, and act—especially when tool use and workflow coherence matter. What Is ReAct? A Quick Primer The ReAct framework—short for Reasoning and Acting—is a prompting strategy that allows an LLM to alternate between thinking and doing in a loop. Each iteration follows this pattern: ...