Cognitive Architecture

In the race to build more autonomous, more intelligent AI agents, we’re entering an era where “strategy” isn’t just about picking the next move—it’s about choosing the right mind for the job and deciding which version of the world to trust. Two recent arXiv papers—one on state representation in dynamic routing games, the other on self-generating agentic systems with swarm intelligence—show just how deeply this matters in practice. We’re no longer only asking: What should the agent do? We now must ask: ...

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. ...

The Crossroads of Reason: When AI Hallucinates with Purpose On this day of reflection and sacrifice, we ask not what AI can do, but what it should become. Good Friday is not just a historical commemoration—it’s a paradox made holy: a moment when failure is reinterpreted as fulfillment, when death is the prelude to transformation. In today’s Cognaptus Insights, we draw inspiration from this theme to reimagine the way we evaluate, guide, and build large language models (LLMs). ...