In this month’s episode of The Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques welcome Giota Antonakaki, a Cisco leader in machine learning engineering, for an illuminating deep dive into a recent academic research shaping AI agent technology. Together, they unpack “The Illusion of Diminishing Returns: Measuring Long Horizon Execution in LLMs”, a paper challenging the common belief that scaling large language models (LLMs) results in only marginal gains.

 

The conversation explores fresh concepts like “long horizon” and “self-conditioning,” revealing how even small improvements in LLM step accuracy can dramatically boost performance in complex, multi-step tasks. Giota breaks down the difference between planning, reasoning, and execution in LLMs, and why reasoning-focused models outperform even the largest standard LLMs for long-running AI agents.

We also extend a special thank you to Akshit Sinha and his team of researchers for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/abs/2509.09677.