Unpack the latest breakthroughs in AI with our podcast. We delve into trillion-parameter language models like Alibaba's Qwen3-Max-Preview, which marks a significant advancement for Chinese AI technology in ultra-large-scale models, and the open-source Kimi K2, as well as Qwen3 models. Discover their cutting-edge agentic capabilities, with Kimi K2 specifically designed for agentic intelligence and excelling in tool-use tasks, extensive multilingual support for over 100 languages in Qwen3-Max-Preview and 119 languages and dialects in Qwen3, and unparalleled performance on authoritative benchmarks. Join us for insightful discussions on AI software development, advanced model architectures like Qwen3's hybrid thinking modes and Kimi K2's MuonClip optimizer, and the market impact of these innovations, including competitive pricing and open-source strategies

Tune into "REFRAG: Rethinking RAG Decoding" to discover a cutting-edge framework revolutionizing Retrieval-Augmented Generation (RAG) in Large Language Models (LLMs). Learn how REFRAG tackles the challenges of long-context inputs, which typically cause high latency and memory demands.

This podcast explores REFRAG's innovative "compress, sense, and expand context" approach, leveraging attention sparsity in RAG contexts. We'll discuss its use of pre-computed chunk embeddings and a lightweight reinforcement learning (RL) policy to selectively determine necessary token input, reducing computationally intensive processes.

Discover how REFRAG achieves up to 30.85× time-to-first-token (TTFT) acceleration (3.75× over previous methods) and extends LLM context size by 16× without losing accuracy. Join us to understand how REFRAG offers a practical and scalable solution for latency-sensitive, knowledge-intensive LLM applications

Why do AI chatbots confidently make up facts?

This podcast explores the surprising reasons language models 'hallucinate'. We'll uncover how these plausible falsehoods originate from statistical errors during pretraining and persist because evaluations reward guessing over acknowledging uncertainty. Learn why models are optimized to be good test-takers, much like students guessing on an exam, and what it takes to build more trustworthy AI systems.