AI Odyssey

• When AI Agents Gossip: The Secret Language of Economic Stability

  What if the health of our economy depends less on tax rates and more on what people are saying to each other? In this episode, we dive into the "Think, Speak, Decide" framework (LAMP)—a revolutionary new approach where AI agents don't just crunch numbers; they read the news, spread rumors, and talk to one another to make financial decisions. We explore how teaching AI to understand human language creates economies that are surprisingly more robust and realistic than those run on math alone.Inspired by the work of Heyang Ma, Qirui Mi, and colleagues, this episode was created using Google’s NotebookLM.Read the original paper here: https://arxiv.org/pdf/2511.12876

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• The Manager in the Machine: Introducing Agentic Organization

  What if an AI didn't just think in a straight line, but actually managed a team of internal agents to solve your problems? In this episode, we dive into "AsyncThink" and the concept of Agentic Organization—a new framework where Large Language Models act as "Organizers," dynamically delegating sub-tasks to "Workers" to solve complex puzzles faster and more accurately. It is not just about thinking harder; it is about thinking together.Inspired by the work of Zewen Chi, Li Dong, and their colleagues at Microsoft Research, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/abs/2510.26658

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• The End of the Cloud? The Rise of Local AI

  What if 88% of your AI queries didn't need a massive data center, but could run directly on your laptop? In this episode, we dive into "Intelligence per Watt"—a new metric redefining how we measure AI efficiency. We explore how smaller, local models are rapidly catching up to frontier giants, potentially saving billions in energy costs and democratizing access to intelligence.Inspired by the work of Jon Saad-Falcon, Avanika Narayan, and their team at Stanford and Together AI, this episode was created using Google’s NotebookLM.Read the original paper here: https://arxiv.org/abs/2511.07885v1

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• When AI Learns From Its Own Context — Self-Improving Language Models

  We're all trying to find the perfect "prompt," but what happens when our instructions to an AI get too complex? New research shows they can suddenly fail or "collapse," losing all their knowledge. In this episode, we explore "Agentic Context Engineering," a new framework that avoids this. Instead of a static prompt, it builds an "evolving playbook" that allows the AI to learn from every single task, failure, and success.Inspired by the work of Qizheng Zhang, Changran Hu, and colleagues, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/abs/2510.04618

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• Will Your Next Prompt Engineer Be an AI?

   What if you could get the performance of a massive, 100-example prompt, but with 13 times fewer tokens?That’s the breakthrough promise of "instruction induction" —teaching an AI to be the prompt engineer.This week, we dive into PROMPT-MII , a new framework that essentially meta-learns how to write compact, high-performance instructions for LLMs. It’s a reinforcement learning approach that could make AI adaptation both cheaper and more effective.This episode explores the original research by Emily Xiao, Yixiao Zeng, Ada Chen, Chin-Jou Li, Amanda Bertsch, and Graham Neubig from Carnegie Mellon University.Read the full paper here for a deeperdive: https://arxiv.org/abs/2510.16932

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