Weaviate Podcast

Alexis, Manya, and Niloofar from humans& join the Weaviate Podcast to introduce Persimmon, a user model built to simulate how humans actually behave in multi-turn, multi-party conversations. Persimmon is explicitly not an assistant, a companion, or a Character AI-style stand-in, it is a research preview aimed at faithfully capturing the distribution of human behavior. This includes the natural friction of frustration, excitement, and group dynamics that assistant chatbots trained to be helpful never exhibit. Alexis, Manya, and Niloofar bring a striking mix of backgrounds to the problem: AI tutoring and student modeling, programming languages for high-performance computing, and privacy and information-flow research at Carnegie Mellon. The conversation opens with whether the Turing test is solved. Humans& runs a distributionally grounded, multi-turn version where the judge sees many examples of human and AI behavior. Frontier models fool it less than 5% of the time, while Persimmon reaches roughly 20% against a 50% ceiling. From there, the discussion dives into training for non-verifiable tasks: why rubrics-as-rewards approaches invite reward hacking, why the team refuses to impose its own theory of human behavior, and how distribution matching, with the multi-turn Turing test as a North Star metric operating in an implicit feature space, rather than Earth mover's distance over hand-picked features anchors both training and evaluation. They walk through evaluating on real human interaction data like the TIDES meeting transcripts and the TutorMoments tutoring dataset, and why role-played or scripted dialogue doesn't count.The discussion then moves into theory of mind and world models as twin goals, with Persimmon enabling multi-agent environments where assistants get realistic human feedback at training time. The podcast further covers the choice of NVIDIA's Nemotron 3 Ultra and why starting from a base model matters: post-training causes mode collapse, you can't prompt-optimize your way out of it, and injected randomness drifts away over long rollouts. The conversation lands on what excites each guest next: personalized tutors, models that balance overlapping human goals, and training paradigms with long-term social pressures.

Chapters
0:00 Welcome Niloofar, Manya, and Alexis!2:22 An Overview of Persimmon6:08 Solving the Turing Test13:36 User Models and AGI16:00 RL with Non-Verifiable Rewards20:35 Distribution Matching28:00 Collecting Human Data32:32 Theory of Mind in AI37:40 NVIDIA Nemotron 3 Ultra43:49 Prompt Optimization46:00 Exciting Directions for AI

Alex Ledbetter is the founder of Delegance Brokerage, an AI-native commercial insurance brokerage he built entirely on his own. The conversation opens with his origin story: an internship on a political risk, credit, and bond underwriting team in New York, where he watched a three-trillion-dollar portfolio run off the back of Excel and realized commercial insurance brokers earning 15–35% annual commissions could be disintermediated the way Robinhood disintermediated retail stock brokers. After raising $200K and securing 190 state licenses in 45 days, he spent seven months teaching himself to build with AI coding tools. Alex compares this process to hammering sheet metal, walking through his own product flow 700 times before showing it to a client.From there, the discussion dives into the architecture. Clients drop in insurance binders approaching a thousand pages, and a document processing pipeline classifies up to 50 commercial insurance document types, vectorizes everything into Weaviate, builds a table of contents per document for multi-hop traversal search, and runs trailing async extractors that pull structured fields into Postgres, processing 80 pages per minute per concurrent upload by converting PDFs into images for vision APIs rather than relying on flat OCR. Three million carrier appetite rules then route each client to the right insurer. Since only 7 of his 27 carrier partners have APIs, browser agents log into carrier portals, handle one-time passwords, and fill out underwriting questionnaires by querying Weaviate and Supabase in real time and monthly compliance agents renew licenses across 50 states.The conversation moves into agent harnesses: multi-model developer pipelines using Codex, Claude, and BugBot that have opened over 900 PRs a month, an email operating system with a chief of staff over iMessage, and how Weaviate unifies memory across web, iOS, Slack, email, and phone. It lands on what's next: a fresh fundraise, SOC 2, and running lean with embedded partnerships instead of brokers.

Chapters:0:00 Welcome Alex!0:38 Founding Story of Delegance Brokerage
6:38 Insurance Brokers and AI
12:15 A Knowledge Base for Insurance
24:20 Vibe Coding
28:50 Browser Agents
34:14 Email Agents
37:30 What is an Agent Harness?
49:00 Weaviate and Postgres Database Design
55:15 The Future of Delegance Brokerage

Sam O'Nuallain joins the Weaviate Podcast to discuss AutoIndex, research from UMass Amherst and Databricks on learning representation programs for retrieval. Instead of tuning the retriever or re-ranker, AutoIndex asks how the data itself should be represented: a two-agent system, an analysis agent and a code agent, writes and refines Python programs that chunk, enrich, and reorganize a corpus to optimize downstream metrics like Recall and nDCG.The conversation opens with why indexing is such a natural target for code optimization. Frontier LLMs are exceptional at writing code, a representation program applies cheaply across an entire corpus without passing every document through an LLM, and code is verifiable. Every hypothesis the code agent proposes is gated against a validation set before it is accepted. From there, the discussion dives into the optimization signal. The analysis agent uses tools to read documents, query the retriever, and inspect where gold documents rank, turning a bare score like "recall went up" into rich natural language feedback about why a representation is failing, echoing ideas like GEPA's reflective metrics and the value of small-margin positives for training re-rankers.That leads into why BM25 pairs so well with agents: its lexical transparency makes failures easy to diagnose, illustrated by case studies from the CRUMB benchmark. This includes LaTeX formatting errors sinking Stack Overflow retrieval and Tip-of-the-Tongue movie search, where AutoIndex learned to repeat plots to up-weight terms and expand documents with synonym dictionaries. The conversation moves through connections to document enrichment methods like Anthropic's contextual retrieval, doc2query, and EnrichIndex, generalizing representation programs to text-to-SQL schemas and data lakehouses. The podcast concludes by discussing Sam's lessons transitioning from research to production AI engineering: loop engineering, QA, and evals. It lands on the directions that excite Sam most: harness design, continual learning, and memory as a retrieval problem, squeezing more out of the models we already have without touching the weights.

Chapters
0:24 An Overview of AutoIndex
4:04 Retrieval Indexing as Code Optimization
8:47 Optimizing Chunking and Database Schemas
15:57 Feedback for Search Optimization
23:57 Future Directions for AutoIndex
27:34 Document Enrichment for RAG
36:32 Web Search vs. Databases
40:40 AI Engineering
49:30 Exciting Directions for AI

Alex Zhang, a PhD student at MIT, joins the Weaviate Podcast to discuss Recursive Language Models (RLMs), a new abstraction for designing agent harnesses. Instead of the standard ReAct-style loop that stuffs every tool observation into an ever-growing prompt, an RLM treats the prompt as a variable in a program. The model writes code that manipulates its own context and spawns recursive LLM calls over pieces of it. The published headline was long-context performance, but the deeper intention, inspired by how DSPy programmers decompose tasks, is letting the model do that decomposition itself, relieving context pressure so each call only sees a small, local, in-distribution problem. Stacking tool outputs into one giant sequence is terribly out of distribution for how models were trained. Frontier labs spend enormous resources making those long trajectories in-distribution, but RLMs aim to get more from the model without that.From there the conversation turns to PrimeAgent, Prime Intellect's production harness centered on an RLM, where the only tool is a persistent IPython REPL: search, compaction, sub-agents, and skills are all callable inside code, yielding strong results on ARC-AGI-3 and long-horizon benchmarks with big token savings. Alex shares why they moved from prompt tuning to RL on the weights of Qwen3, and why Claude Code's dynamic workflows are RLM-like but constrained by legacy tool-calling defaults.Alex then previews an unreleased library for speculative programmatic tool calling. Borrowing from speculative decoding and CPU speculative execution, a shadow executor queues up sub-agent calls while the main model is still generating code, delivering roughly 2x speedups and better GPU utilization for locally served agents. The discussion moves into running RLMs in the cloud with sandbox providers like Modal and Daytona, security concerns around code execution and prompt injection, and why serving engines should be redesigned around agents. It closes with RLMs and search, combining ColBERT-style retrievers as callable tools for problems like agent log analysis, and a theory that everything "long" is naturally decomposable. The podcast concludes with exciting ideas about where AI goes next from principled harness design to whether the transformer itself gets replaced with a newer neural architecture design.

Mathew Jacob, lead author of "Drowning in Documents: Consequences of Scaling Reranker Inference" and now a PhD student in ML systems at the University of Washington, joins the Weaviate Podcast to unpack one of the most surprising results in modern search: cross-encoder rerankers get worse as you give them more documents. The paper began during his Databricks internship, where scaling reranking past roughly 100 documents sent recall@10 plummeting, a result so counterintuitive he assumed it was a bug.The conversation digs into why this happens, reframing rerankers through the lens of boosting, rather than being strictly stronger than first-stage retrievers. Cross-encoders are very good at correcting retriever errors within the distribution they were trained on. Full-scoring experiments over 10,000 randomly sampled documents drive the point home, with BM25 beating state-of-the-art cross-encoders. From there, the discussion moves into phantom hits, cases where wildly irrelevant documents scored highly. For example, a dishwasher document surfacing for a query about disease in Gabonese children. We also discuss whether ensembling rerankers can patch these false positives.The second half explores what comes next for reranking: prompt-based listwise reranking with sliding windows, which proved far more robust than pointwise scoring; RankZephyr-style fine-tuning versus encoding learning signal in prompts with GEPA and DSPy, reasoning rerankers like Rank1 and their latency trade-offs, hard negative mining behind ZeroEntropy's zELO, and pairwise and setwise designs that sit between cross-encoders and full listwise ranking. Adaptive retrieval comes into focus through Natural Language Query to Configuration for Retrieval Agents, predicting per query whether to run simple retrieval, multi-hop, or full agentic search to push the cost-quality frontier.The conversation lands on TraceLab, from Mathew's lab at UW: 40,000 real traces harvested from Claude Code and Codex usage, revealing how coding agents actually behave, prefix cache patterns, long-tailed tool calls, and how understanding these workloads unlocks the next generation of serving optimizations.