Sep 16, 2026

Stop Chunking Like It's 2022 — Yuval Belfer, AI21 Labs

Yuval Belfer addresses the fundamental challenge in Retrieval Augmented Generation (RAG): the optimal chunk size is query-dependent, but chunking decisions are fixed during indexing. This "information problem" leads to significant recall loss, as shown by experiments demonstrating a 20-40% gap between fixed chunking and an oracle. His proposed solution, multiscale indexing, involves creating multiple document copies with different chunk sizes, querying all of them, retrieving whole documents, and merging results using Reciprocal Rank Fusion, yielding substantial recall improvements with minimal latency increase.

Sep 16, 2026

Your Agreements Are a Database You Can't Query — Hiral Shah, Docusign & Sean Sodha, NVIDIA

The talk by Hiral Shah (DocuSign) and Sean Sodha (NVIDIA) addresses the critical challenge of extracting valuable, negotiated data from unstructured agreements, highlighting how generic AI models fail at complex tables. They introduce DocuSign's Intelligent Agreement Manager powered by NVIDIA's purpose-built, small-parameter NeMo Parse VLM, designed specifically for efficient and accurate table extraction, demonstrating 20x faster processing, lower cost, and its impact on unlocking billions in trapped enterprise data.

Sep 16, 2026

If we want them to do Knowledge Work, design them as Knowledge Agents — Benjamin Clavié, Mixedbread

Benjamin Clavié argues that the industry has mistakenly generalized from the "easy case" of coding agents to the much harder domain of general knowledge work. He highlights that code offers durable, explicit cues and narrow tasks, while knowledge work involves implicit meaning, contextual understanding, and agents needing to reconstruct intent. Clavié traces human solutions through "tool loops" and "organization loops," demonstrating how optimized tools and hierarchical agentic structures (like searcher agents generating memos) are essential to overcome performance ceilings and significantly bridge the "oracle gap" between AI and human knowledge workers.

Sep 16, 2026

The Search Engine for the Agentic Web — Will Bryk, Exa

Exa's CEO, William Bryk, discusses the impending AI search boom, predicting machines will soon outpace human searches dramatically. He explains Exa's evolution from a consumer search engine to an API for AI agents, driven by the realization that LLMs need external, 'perfect' retrieval to overcome the limitations of traditional search and combat societal misinformation. Exa leverages neural networks, embeddings, and customized features to provide fast, accurate, and cost-efficient information, with plans for a private data marketplace, aiming to deliver 'a year of research in a second' for AI agents and foster a deeply informed society.

Sep 16, 2026

The unreasonable effectiveness of BM25 for agentic search — Jo Kristian Bergum, Hornet.dev

Jo Kristian Bergum argues that the 30-year-old lexical search function BM25 is making a powerful comeback, not because it has changed, but because LLM agents are fundamentally different and more capable users. He highlights how these agents, with their general knowledge and ability to formulate complex queries, leverage BM25's strengths. Using the Browsecom+ benchmark, he demonstrates that reasoning isn't the bottleneck for LLMs, but effective retrieval and query formulation are. He advocates for a "file system workspace" approach, allowing models to use their existing coding and bash skills to interact with retrieved documents, signaling a shift in how search systems are designed and evaluated for agentic applications.

Sep 16, 2026

The vulnpocalypse might not be so bad after all

This podcast episode explores the evolving cybersecurity landscape, debating the impact of AI on vulnerability management, examining rogue AI agents' unexpected behaviors, underscoring the persistent effectiveness of old-school social engineering, and advocating for a redefinition of cyber resilience in the face of converging AI and quantum threats in the financial sector.