AI Odyssey

Anlie Arnaudy, Daniel Herbera and Guillaume Fournier

AI Odyssey is your journey through the vast and evolving world of artificial intelligence. Powered by AI, this podcast breaks down both the foundational concepts and the cutting-edge developments in the field. Whether you're just starting to explore the role of AI in our world or you're a seasoned expert looking for deeper insights, AI Odyssey offers something for everyone. From AI ethics to machine learning intricacies, each episode is crafted to inspire curiosity and spark discussion on how artificial intelligence is shaping our future.

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28 episodes
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English

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May 3 · 17 min
AI Agents Are Becoming Companies
What if the next leap in AI agents is not a smarter worker, but a better organisation? This paper introduces OneManCompany, a framework that turns scattered agents, tools, skills, and runtime configurations into managed “Talents” that can be hired, reviewed, replaced, and improved over time. Its Explore-Execute-Review loop decomposes work, assigns accountability, checks outputs, and learns from failures. The result is striking: 84.67% success on PRDBench, beating reported baselines by 15.48 percentage points. But the catch is equally important: this organisational intelligence costs more and is still mostly validated on software tasks. Inspired by the work of Zhengxu Yu, Yu Fu, Zhiyuan He, Yuxuan Huang, Lee Ka Yiu, Meng Fang, Weilin Luo, and Jun Wang, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/abs/2604.22446v1
April 27 · 18 min
AI Agents Just Learned to Remember
What if the real bottleneck for AI agents is not reasoning,but memory? StructMem argues that long-term agents should not storeconversations as isolated facts or expensive knowledge graphs. Instead, they should remember temporally grounded events: what happened, who was involved, and how one event connects to another. On the LoCoMo benchmark, thisstructure-enriched memory reaches the best overall score while cutting construction costs dramatically compared with graph-heavy approaches. For anyone building autonomous agents, the message is clear:memory is becoming an architecture problem, not just a retrieval problem. Inspired by the work of Buqiang Xu, Yijun Chen, Jizhan Fang,Ruobin Zhong, Yunzhi Yao, Yuqi Zhu, Lun Du, and Shumin Deng, this episode was created using Google's NotebookLM. Read the original paper here: https://arxiv.org/pdf/2604.21748v1
April 18 · 20 min
The Protocol That Lets Agents Rewrite Themselves
What if the missing layer in agent design isn't communication, but version control? In this episode, we unpack Autogenesis, a two-layer protocol that treats prompts, tools, and memory as first-class resources with explicit lifecycle, versioning, and rollback. The core insight is striking: connectivity standards like MCP and A2A tell agents how to reach tools, but stay silent on what happens when agents start rewriting those tools on their own. Autogenesis fills that gap, and the numbers speak loudly, including a 33% jump on the hardest GAIA benchmark tasks. Inspired by the work of Wentao Zhang, this episode was created using Google's NotebookLM. Read the original paper here: https://arxiv.org/abs/2604.15034
April 12 · 25 min
When Agents Learn to Forget: The Memory Revolution in AI Research
What if the biggest bottleneck in AI agents wasn't reasoning power, but memory management? In this episode, we explore a fascinating new framework called MIA, the Memory Intelligence Agent, which reimagines how AI research agents store, compress, and reuse their past experiences. Instead of hoarding every search trace into an ever-growing context window, MIA separates memory into a Manager, a Planner, and an Executor, each with a distinct role. The result: a 7-billion parameter model that outperforms GPT-4o on complex research tasks, and even boosts GPT-5.4 performance by up to 9%. We unpack why "keeping everything" is a trap, and how forgetting strategically might be the real key to smarter AI. Inspired by the work of Jingyang Qiao, Weicheng Meng, Yu Cheng, and colleagues at East China Normal University, this episode was created using Google's NotebookLM. Read the original paper here: https://arxiv.org/pdf/2604.04503
April 6 · 23 min
The Web is a Minefield: How AI Agents Get Trapped
What if the biggest threat to AI agents isn't a flaw in the model, but the internet itself? A new paper from Google DeepMind introduces the first systematic framework for "AI Agent Traps": adversarial content hidden in websites, documents, and digital resources, engineered to manipulate autonomous agents. From invisible HTML instructions that hijack summaries, to poisoned memory stores that corrupt decisions across sessions, to systemic traps that could trigger flash crashes across agent economies. The researchers identify six categories of attack targeting every layer of an agent's architecture: perception, reasoning, memory, action, multi-agent dynamics, and the human overseer. As enterprises deploy agents at scale, this paper is a wake-up call: the web was built for human eyes, and rebuilding it for machine readers demands a fundamentally new security playbook. Inspired by the work of Matija Franklin, Nenad Tomašev, Julian Jacobs, Joel Z. Leibo, and Simon Osindero, this episode was created using Google's NotebookLM. Read the original paper here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=6372438
March 29 · 24 min
🎧 AI That Rewrites Its Own Brain: Meet the HyperAgent
What happens when you give an AI system the ability to modify not just its answers, but the very process it uses to improve itself? In this episode, we explore HyperAgents, a new framework from Meta and UBC that enables AI systems to recursively improve their own learning mechanisms. Unlike previous approaches where the improvement strategy was fixed by human engineers, HyperAgents can rewrite their own self-improvement code, creating a loop where getting better at a task also means getting better at getting better. The results are striking: improvements discovered in one domain, like reviewing research papers, transfer to completely unrelated tasks like grading Olympic math solutions. Inspired by the work of Jenny Zhang, Bingchen Zhao, Wannan Yang, Jakob Foerster, Jeff Clune, Minqi Jiang, Sam Devlin, and Tatiana Shavrina, this episode was created using Google's NotebookLM. Read the original paper here: https://arxiv.org/abs/2603.19461
March 22 · 21 min
When Agents Remember Their Mistakes: The End of AI Amnesia
What if an AI agent could learn from every single failure, every clumsy workaround, every brilliant recovery, and feed that experience back into its own future performance? Today’s LLM-powered agents suffer from a fundamental flaw: amnesia. They repeat the same mistakes, miss the same shortcuts, and rediscover the same solutions over and over. A new framework from IBM Research changes that by mining agent execution trajectories for three types of actionable knowledge: strategy tips from clean successes, recovery tips from failure-and-fix sequences, and optimization tips from tasks completed inefficiently. On the AppWorld benchmark, agents equipped with this learned memory improved scenario goal completion by up to 14.3 percentage points on unseen tasks, and by a staggering 28.5 points on complex multi-step challenges. That is a 149% relative increase, with zero model changes. Inspired by the work of Gaodan Fang, Vatche Isahagian, K. R. Jayaram, Ritesh Kumar, Vinod Muthusamy, Punleuk Oum, and Gegi Thomas, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/abs/2603.10600
March 14 · 13 min
Agents That Teach Themselves
What if AI agents could diagnose their own mistakes and build the exact skills they need to fix them, with no human intervention? In this episode, we explore EvoSkill, a self-evolving framework where coding agents automatically discover and refine reusable skills through iterative failure analysis. Instead of optimizing prompts or fine-tuning models, EvoSkill lets agents build structured skill libraries that accumulate over time, improving performance by up to 12% on challenging benchmarks. Even more striking: skills learned on one task transfer to completely different tasks without modification. Inspired by the work of Salaheddin Alzubi, Noah Provenzano, Jaydon Bingham, Weiyuan Chen, and Tu Vu, this episode was created using Google’s NotebookLM. Read the original paper here: https://arxiv.org/pdf/2603.02766

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