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Deep Papers

Arize AI

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Deep Papers is a podcast series featuring deep dives on today’s most important AI papers and research. Hosted by Arize AI founders and engineers, each episode profiles the people and techniques behind cutting-edge breakthroughs in machine learning.
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How AI products come to life—straight from the builders themselves. In each episode, we dive deep into how teams spotted a customer problem, experimented with AI, prototyped solutions, and shipped real features. We dig into everything from workflows and agents to RAG and evaluation strategies, and explore how their products keep evolving. If you’re building with AI, these are the stories for you.
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Guests** Jack Taylor, Product Engineer, Gradient Labs Ibrahim Faruqi, AI Engineer, Gradient Labs In this episode The iceberg metaphor: why frontline support is only the tip of automation potential How three agent types (inbound, back office, outbound) coordinate on complex tasks like fraud disputes Natural language procedures that let subject matte…
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Guests Chris O'Connor – CEO, Mowie Jessica Valenzuela – Co-Founder, Mowie What we cover in this episode How Mowie evolved from a concierge marketing service to an AI-powered platform The "document hierarchy" architecture: how Mowie builds and maintains context about each business Why they moved from structured schemas to loosely structured markdown…
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Guests Steven Payne, Product Manager, Perk Gabriel Stock, Senior Engineering Manager, Perk Philipe Steiff, Senior Software Engineer, Perk What we cover in this episode How Perk's team identified an AI use case by connecting prior experimentation with a real operational problem Why they chose Make.com for prototyping—and shipped to production withou…
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Former controller of the currency Eugene Ludwig has written a book called The Mismeasurement of America that lays out the shortcomings of the standard economic data that U.S. government and businesses use to make decisions, and how this data obscures the truth about how low-income Americans are actually faring.…
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We dive into the latest paper from Google and a team of academic researchers: "TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture." Hear from one of the paper's authors — Yongchao Chen, Research Scientist — walks through the research and its implications. The paper proposes Tool-Use Mixture (TUMIX), an ensemble framework that runs multiple …
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Guests Martin Siniawski, CEO and co-founder, Rest Ignacio, CTO, Rest You'll hear how they: Discovered the sleep use case from podcast app user behavior (10% of users, but high willingness to pay) Used jobs-to-be-done research to identify "DIY sleep hackers" as an underserved segment Chose CBTI (Cognitive Behavioral Therapy for Insomnia) as their fo…
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Guests Claire Smid — AI Engineer, Xelix Emilija Gransaull — Back-End Tech Lead, Xelix Talal A. — Product Manager, Xelix Key Takeaways Start narrow to win: pick high-volume, high-cost requests (invoice status & reminders). Enrichment > magic: accurate replies come from great retrieval/matching, not just a bigger LLM. Design for adoption: familiar in…
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In our latest paper reading, we had the pleasure of hosting Grégoire Mialon — Research Scientist at Meta Superintelligence Labs — to walk us through Meta AI’s groundbreaking paper titled “ARE: scaling up agent environments and evaluations" and the new ARE and Gaia2 frameworks. Learn more about AI observability and evaluation, join the Arize AI Slac…
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Guests Lawrence Jones, Founding Engineer at Incident.io Ed Dean Product Lead for AI at Incident.io Key Takeaways AI’s biggest impact comes from compressing time—identifying causes minutes instead of hours. Retrieval-augmented reasoning still benefits from simplicity: deterministic tagging and re-ranking often beat complex vector setups. Post-incide…
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Guests David Eason, Principal Product Manager at Trainline Billie Bradley, Product Manager, Travel Assistant at Trainline Matt Farrelly, Head of AI and Machine Learning at Trainline Key Takeaways AI assistants need both scalable reasoning and deep domain context to be useful. Tool design and guardrails are as critical as prompt design in agent syst…
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Guests Noa Reikhav, Head of Product, Zencity Andrew Therriault, VP of Data Science, Zencity Shota Papiashvili, SVP of R&D, Zencity In this episode How Zencity helps local governments reach, understand, and act on community voices Turning thousands of survey responses, social posts, 311 calls, and news items into usable insight Building a data model…
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Guests: Seyna Diop, Chief Product Officer, Neople Job Nijenhuis, CTO & Co-founder, Neople Christos Constantinou, Lead Design Engineer, Neople Chapters: 00:00 Meet the Team: Introducing Neople's Key Players01:16 Understanding Neople's Product: Digital Coworkers03:25 The Origin Story: How Neople Came to Be06:24 Customer Success: Real-World Applicatio…
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Santosh Vempala, Frederick Storey II Chair of Computing and Distinguished Professor in the School of Computer Science at Georgia Tech, explains his paper co-authored by OpenAI's Adam Tauman Kalai, Ofir Nachum, and Edwin Zhang. Read the paper: Sign up for future AI research paper readings and author office hours. See LLM hallucination examples here …
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Guests: SallyAnn DeLucia, Director of Product, Arize Jack Zhou, Staff Engineer, Arize In this episode, we cover: What tracing, observability, and evals really mean in GenAI applications How Arize used its own platform to build Alyx, its AI agent The role of customer success engineers in surfacing repeatable workflows Why early prototyping looked li…
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Guest: Hamel Husain AI products and problems discussed: GitHub Copilot Forecasting AirBnB Guest Growth- NurtureBoss Resources & Links Hamel’s blog on AI evals AI Evals for Engineers and PMs course on Maven (Get 35% off with this affiliate link) Chapters: 00:00 Introduction to Hamel Hussein 00:34 Challenges in AI Consulting 02:00 Machine Learning Fu…
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Guests: Thom van der Doef, Principal Product Designer at eSpark Mary [last name], Director of Learning Design & Product Manager at eSpark Ray Lyons, VP of Product & Engineering at eSpark Topics covered: The origin story of Teacher Assistant: connecting administrator mandates with teacher needs Why the team abandoned a chatbot interface in favor of …
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Large language models are increasingly used to turn complex study output into plain-English summaries. But how do we know which models are safest and most reliable for healthcare? In this most recent community AI research paper reading, Arjun Mukerji, PhD – Staff Data Scientist at Atropos Health – walks us through RWESummary, a new benchmark design…
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Guest: Ellen Brandenburger – Product leader and coach; former head of product at Chegg Skills and Stack Overflow’s data licensing team. What we cover in this episode: How Ellen joined Stack Overflow just two weeks before ChatGPT launched, reshaping the company’s future overnight The creation of Overflow AI: a team tasked with exploring “what’s just…
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How AI products come to life—straight from the builders themselves. In each episode, we dive deep into how teams spotted a customer problem, experimented with AI, prototyped solutions, and shipped real features. We dig into everything from workflows and agents to RAG and evaluation strategies, and explore how their products keep evolving. If you’re…
  continue reading
 
This episode dives into "Category-Theoretic Analysis of Inter-Agent Communication and Mutual Understanding Metric in Recursive Consciousness." The paper presents an extension of the Recursive Consciousness framework to analyze communication between agents and the inevitable loss of meaning in translation. We're thrilled to feature the paper's autho…
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We had the privilege of hosting Peter Belcak – an AI Researcher working on the reliability and efficiency of agentic systems at NVIDIA – who walked us through his new paper making the rounds in AI circles titled “Small Language Models are the Future of Agentic AI.” The paper posits that small language models (SLMs) are sufficiently powerful, inhere…
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In this AI research paper reading, we dive into "A Watermark for Large Language Models" with the paper's author John Kirchenbauer. This paper is a timely exploration of techniques for embedding invisible but detectable signals in AI-generated text. These watermarking strategies aim to help mitigate misuse of large language models by making machine-…
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The authors of the new paper *Self-Adapting Language Models (SEAL)* shared a behind-the-scenes look at their work, motivations, results, and future directions. The paper introduces a novel method for enabling large language models (LLMs) to adapt their own weights using self-generated data and training directives — “self-edits.” Learn more about th…
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Staking activities and stablecoins are two of the possible ways banks could have a role in decentralized finance, said Margaret Butler, head of the financial services practice at the law firm Baker Hostetler and Kristiane Koontz, director of Treasury Services and Payments at Zions Bank, in interviews recorded at the Digital Banking Conference in Ju…
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This week we discuss The Illusion of Thinking, a new paper from researchers at Apple that challenges today’s evaluation methods and introduces a new benchmark: synthetic puzzles with controllable complexity and clean logic. Their findings? Large Reasoning Models (LRMs) show surprising failure modes, including a complete collapse on high-complexity …
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We discuss Accurate KV Cache Quantization with Outlier Tokens Tracing, a deep dive into improving the efficiency of LLM inference. The authors enhance KV Cache quantization, a technique for reducing memory and compute costs during inference, by introducing a method to identify and exclude outlier tokens that hurt quantization accuracy, striking a b…
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In this week's episode, we talk about Elastic Reasoning, a novel framework designed to enhance the efficiency and scalability of large reasoning models by explicitly separating the reasoning process into two distinct phases: thinking and solution. This separation allows for independent allocation of computational budgets, addressing challenges rela…
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What if your LLM could think ahead—preparing answers before questions are even asked? In this week's paper read, we dive into a groundbreaking new paper from researchers at Letta, introducing sleep-time compute: a novel technique that lets models do their heavy lifting offline, well before the user query arrives. By predicting likely questions and …
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For this week's paper read, we dive into our own research. We wanted to create a replicable, evolving dataset that can keep pace with model training so that you always know you're testing with data your model has never seen before. We also saw the prohibitively high cost of running LLM evals at scale, and have used our data to fine-tune a series of…
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This week we talk about modern AI benchmarks, taking a close look at Google's recent Gemini 2.5 release and its performance on key evaluations, notably Humanity's Last Exam (HLE). In the session we covered Gemini 2.5's architecture, its advancements in reasoning and multimodality, and its impressive context window. We also talked about how benchmar…
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We cover Anthropic’s groundbreaking Model Context Protocol (MCP). Though it was released in November 2024, we've been seeing a lot of hype around it lately, and thought it was well worth digging into. Learn how this open standard is revolutionizing AI by enabling seamless integration between LLMs and external data sources, fundamentally transformin…
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