In the latest episode of the Cisco AI Insights podcast, hosts Rafael Herrera and Sonia Marques are joined by Dr. Catarina Carvalho, a Cisco leader in machine learning engineering. Together, they unpack the complex academic paper " Multi-Crit: Benchmarking Multimodal Judges on Pluralistic Criteria-Following," developed by researchers from the University of Maryland and the University of Waterloo.

As the industry moves toward more reliable multimodal models, traditional pass-or-fail evaluation is no longer sufficient. This paper introduces a hierarchical framework that uses "LLM-as-a-judge" to evaluate outputs across five distinct criteria: visual grounding, logical coherence, factuality, reflection, and conciseness. Dr. Carvalho guides the discussion through the nuances of this "judge of judges" approach, exploring why human alignment remains the gold standard even as we automate evaluation processes.

A special thank you to the teams at both The University of Waterloo and The University of Maryland, College Park, for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: https://arxiv.org/pdf/2511.21662. 

In the latest episode of the Cisco AI Insights Podcast, hosts Rafael Herrera and Sónia Marques welcome Cisco AI operations engineer James Tidd for a discussion on the world of small language models (SLMs) and the evolution of efficient AI inference. Together, they unravel the complexities behind “Fast Inference from Transformers via Speculative Decoding,” a groundbreaking paper from Google that explores how smaller draft models can speed up large language model predictions while maintaining accuracy.

James shares his hands-on experience experimenting with the technique, leveraging knowledge distillation and speculative execution. The trio also discusses the potential of this approach to optimize AI, reduce power consumption and costs, and help businesses of all sizes get more out of existing hardware.

A special thank you to Google’s AI team for developing this month's paper. If you are interested in reading the paper yourself, please visit this link: research.google/blog/looking-back…ulative-decoding/.

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.