We dive into the latest paper from a team of researchers at IBM: "From Benchmarks to Business Impact: Deploying IBM Generalist Agent in Enterprise Production." We're excited to host several of the paper's authors, who walk us through the research and its implications. The paper reports IBM’s experience developing and piloting the Computer Using Generalist Agent (CUGA), which has been open-sourced for the community. CUGA adopts a hierarchical planner–executor architecture with strong analytical f...
Feb 11, 2026•23 min
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 agents in parallel, each employing distinct tool-use strategies and answer paths. Agents in TUMIX iteratively share and refine responses based on th...
Nov 24, 2025•24 min
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 Slack community or get the latest on LinkedIn and X ....
Nov 10, 2025•23 min
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 for context. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X ....
Oct 14, 2025•31 min
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 designed to evaluate LLMs on summarizing real-world evidence from structured study output — an important but often under-tested scenario compared to the typ...
Sep 22, 2025•26 min
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 author, Stan Miasnikov , Distinguished Engineer, AI/ML Architecture, Consumer Experience at Verizon, to walk us through the research and its implications...
Sep 06, 2025•48 min
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, inherently more suitable, and necessarily more economical for many invocations in agentic systems, and are therefore the future of agentic AI. The authors...
Sep 05, 2025•31 min
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-generated content distinguishable from human writing, without sacrificing text quality or requiring access to the model’s internals. Learn more about ...
Jul 30, 2025•43 min
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 the Self-Adapting Language Models paper . Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on Link...
Jul 08, 2025•31 min
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 tasks and a decline in reasoning effort as problems get harder. Dylan and Parth dive into the paper's findings as well as the debate around it, includ...
Jun 20, 2025•31 min
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 better balance between efficiency and performance. Read the paper Access the slides Read the blog Join us for Arize Observe Learn more about AI observa...
Jun 04, 2025•25 min
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 related to uncontrolled output lengths in real-world deployments with strict resource constraints. Our discussion explores how Elastic Reasoning contribu...
May 16, 2025•29 min
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 precomputing key reasoning steps, sleep-time compute dramatically reduces test-time latency and cost—without sacrificing performance. We explore n...
May 02, 2025•30 min
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 SLMs that perform just as well as their base LLM counterparts, but at 1/10 the cost. So, over the past few weeks, the Arize team generated the larges...
Apr 18, 2025•27 min
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 benchmarks like HLE and ARC AGI 2 help us understand the current state and future direction of AI. Join us for the next live recording , or check out the late...
Apr 04, 2025•26 min
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 transforming them into capable, context-aware agents. We explore the key benefits of MCP, including enhanced context retention across interactions, improved int...
Mar 25, 2025•15 min
This week, we're mixing things up a little bit. Instead of diving deep into a single research paper, we cover the biggest AI developments from the past few weeks. We break down key announcements, including: DeepSeek’s Big Launch Week: A look at FlashMLA (DeepSeek’s new approach to efficient inference) and DeepEP (their enhanced pretraining method). Claude 3.7 & Claude Code: What’s new with Anthropic’s latest model, and what Claude Code brings to the AI coding assistant space. Stay ahead of t...
Mar 01, 2025•30 min
This week, we dive into DeepSeek. SallyAnn DeLucia, Product Manager at Arize, and Nick Luzio, a Solutions Engineer, break down key insights on a model that have dominating headlines for its significant breakthrough in inference speed over other models. What’s next for AI (and open source)? From training strategies to real-world performance, here’s what you need to know. Read our analysis of DeepSeek , or dive into the latest AI research . Learn more about AI observability and evaluation , join t...
Feb 21, 2025•30 min
We talk to Google DeepMind Senior Research Scientist (and incoming Assistant Professor at Harvard), Yilun Du, about his latest paper, "Multiagent Finetuning: Self Improvement with Diverse Reasoning Chains." This paper introduces a multiagent finetuning framework that enhances the performance and diversity of language models by employing a society of agents with distinct roles, improving feedback mechanisms and overall output quality. The method enables autonomous self-improvement through iterati...
Feb 04, 2025•30 min
LLMs have typically been restricted to reason in the "language space," where chain-of-thought (CoT) is used to solve complex reasoning problems. But a new paper argues that language space may not always be the best for reasoning. In this paper read, we cover an exciting new technique from a team at Meta called Chain of Continuous Thought—also known as "Coconut." In the paper, "Training Large Language Models to Reason in a Continuous Latent Space" explores the potential of allowing LLMs to reason...
Jan 14, 2025•25 min
We discuss a major survey of work and research on LLM-as-Judge from the last few years. "LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation Methods" systematically examines the LLMs-as-Judge framework across five dimensions: functionality, methodology, applications, meta-evaluation, and limitations. This survey gives us a birds eye view of the advantages, limitations and methods for evaluating its effectiveness. Read a breakdown on our blog: https://arize.com/blog/llm-as-judge-survey...
Dec 23, 2024•29 min
LLMs have revolutionized natural language processing, showcasing remarkable versatility and capabilities. But individual LLMs often exhibit distinct strengths and weaknesses, influenced by differences in their training corpora. This diversity poses a challenge: how can we maximize the efficiency and utility of LLMs? A new paper, "Merge, Ensemble, and Cooperate: A Survey on Collaborative Strategies in the Era of Large Language Models," highlights collaborative strategies to address this challenge...
Dec 10, 2024•29 min
This week, we break down the “Agent-as-a-Judge” framework—a new agent evaluation paradigm that’s kind of like getting robots to grade each other’s homework. Where typical evaluation methods focus solely on outcomes or demand extensive manual work, this approach uses agent systems to evaluate agent systems, offering intermediate feedback throughout the task-solving process. With the power to unlock scalable self-improvement, Agent-as-a-Judge could redefine how we measure and enhance agent perform...
Nov 23, 2024•25 min
We break down OpenAI’s realtime API. Learn how to seamlessly integrate powerful language models into your applications for instant, context-aware responses that drive user engagement. Whether you’re building chatbots, dynamic content tools, or enhancing real-time collaboration, we walk through the API’s capabilities, potential use cases, and best practices for implementation. Learn more about AI observability and evaluation , join the Arize AI Slack community or get the latest on LinkedIn and X ...
Nov 12, 2024•30 min
As multi-agent systems grow in importance for fields ranging from customer support to autonomous decision-making, OpenAI has introduced Swarm, an experimental framework that simplifies the process of building and managing these systems. Swarm, a lightweight Python library, is designed for educational purposes, stripping away complex abstractions to reveal the foundational concepts of multi-agent architectures. In this podcast, we explore Swarm’s design, its practical applications, and how it sta...
Oct 29, 2024•47 min
In this episode, we dive into the intriguing mechanics behind why chat experiences with models like GPT often start slow but then rapidly pick up speed. The key? The KV cache. This essential but under-discussed component enables the seamless and snappy interactions we expect from modern AI systems. Harrison Chu breaks down how the KV cache works, how it relates to the transformer architecture, and why it's crucial for efficient AI responses. By the end of the episode, you'll have a clearer under...
Oct 24, 2024•4 min
In this byte-sized podcast, Harrison Chu, Director of Engineering at Arize, breaks down the Shrek Sampler. This innovative Entropy-Based Sampling technique--nicknamed the 'Shrek Sampler--is transforming LLMs. Harrison talks about how this method improves upon traditional sampling strategies by leveraging entropy and varentropy to produce more dynamic and intelligent responses. Explore its potential to enhance open-source AI models and enable human-like reasoning in smaller language models. Learn...
Oct 16, 2024•4 min
This week, Aman Khan and Harrison Chu explore NotebookLM’s unique features, including its ability to generate realistic-sounding podcast episodes from text (but this podcast is very real!). They dive into some technical underpinnings of the product, specifically the SoundStorm model used for generating high-quality audio, and how it leverages a hierarchical vector quantization approach (RVQ) to maintain consistency in speaker voice and tone throughout long audio durations. The discussion also to...
Oct 15, 2024•43 min
OpenAI recently released its o1-preview, which they claim outperforms GPT-4o on a number of benchmarks. These models are designed to think more before answering and handle complex tasks better than their other models, especially science and math questions. We take a closer look at their latest crop of o1 models, and we also highlight some research our team did to see how they stack up against Claude Sonnet 3.5--using a real world use case. Read it on our blog: https://arize.com/blog/exploring-op...
Sep 27, 2024•42 min
A recent announcement on X boasted a tuned model with pretty outstanding performance, and claimed these results were achieved through Reflection Tuning. However, people were unable to reproduce the results. We dive into some recent drama in the AI community as a jumping off point for a discussion about Reflection 70B. In 2023, there was a paper written about Reflection Tuning that this new model (Reflection 70B) draws concepts from. Reflection tuning is an optimization technique where models lea...
Sep 19, 2024•27 min
