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Research

Our in-house research and engineering insights on emerging methodologies, tools, and techniques for optimizing AI performance

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MCP vs. CLI Skills for agents: what our eval found (and which you should use)

Twitter said pick a side. The eval said the question was wrong. Six months ago, MCP (model context protocol) was the hot new thing: tool usage with a built-in discovery mechanism. Everyone was building MCP servers. Then Anthropic, who invented MCP, launched Claude Skills, and the discourse turned. Simon Willison, the co-creator of the Django…

9 minutes read
By Laurie Voss | 9 minutes read
Evaluations Large Language Models Research

CLAUDE.md: Best Practices Learned from Optimizing Claude Code with Prompt Learning

In our last post on Prompt Learning (our prompt optimization feature), we optimized Cline, a powerful coding agent, through its system prompt. This time, we used it on one that more of you are probably familiar with – Claude Code – and saw some awesome results. Claude Code is considered one of the best coding…

8 minutes read
By Priyan Jindal | 8 minutes read
Prompt Learning Research Use-Case

Meta AI Researcher Explains ARE and Gaia2: Scaling Up Agent Environments and Evaluations

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.” Context on the Paper Meta’s new ARE (Agents Research Environments) is a platform for building time-driven worlds where…

3 minutes read
By David Burch | 3 minutes read
Research

ServiceNow’s Tara Bogavelli on AgentArch: Benchmarking AI Agents for Enterprise Workflows

In our latest AI research paper reading, we hosted Tara Bogavelli, Machine Learning Engineer at ServiceNow, to discuss her team’s recent work on AgentArch, a new benchmark designed to evaluate and compare AI agent architectures across real-world enterprise workflows.  As Tara described, the motivation behind AgentArch is to “move benchmarking closer to reality.” Instead of…

3 minutes read
By Julian Reeves | 3 minutes read
LLM Evals Paper Readings Research

OpenAI’s Santosh Vempala Explains Why Language Models Hallucinate

In our latest AI research paper reading, we hosted Santosh Vempala, Professor at Georgia Tech and co-author of OpenAI’s paper, “Why Language Models Hallucinate.” This paper offers one of the clearest theoretical explanations to date of why AI models make up facts — connecting hallucinations to classical misclassification theory and statistical calibration. The conversation explored…

4 minutes read
By Julian Reeves | 4 minutes read
Large Language Models Paper Readings Research

Sleep-time Compute: Beyond Inference Scaling at Test-time

We recently discussed “Sleep Time Compute: Beyond Inference Scaling at Test Time,” new research from the team at Letta. The paper addresses a key challenge in using powerful AI models: the trade-off between gaining better performance by having models “think longer” or “reason deeper” at test time, and the resulting increased latency for users and…

4 minutes read
By Sarah Welsh | 4 minutes read
Paper Readings Research
Libre Eval Blog Image

LibreEval: A Smarter Way to Detect LLM Hallucinations

Over the past few weeks, the Arize team has generated the largest public dataset of hallucinations, as well as a series of fine-tuned evaluation models. 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….

3 minutes read
By Sarah Welsh | 3 minutes read
Paper Readings Research
Graph from Humanity's Last Exam (Reasoning & Knowledge), including how popular LLMs scored

AI Benchmark Deep Dive: Gemini 2.5 and Humanity’s Last Exam

Our latest paper reading provided a comprehensive overview of modern AI benchmarks, taking a close look at Google’s recent Gemini 2.5 release and its performance on key evaluations, notably the challenging Humanity’s Last Exam (HLE). For those who missed the live session, we’ve compiled the essential highlights and key takeaways. This recap covers Gemini 2.5’s…

5 minutes read
By Sarah Welsh | 5 minutes read
Paper Readings Research
o1 time series evals

o1-preview Time Series Evaluations

Time series anomaly detection is one of the most challenging tasks we tackle at Arize. Using large language models (LLMs) for time series analysis, especially in our AI co-pilot assistant, has proven invaluable for uncovering intricate patterns, correlations, and potential issues in complex datasets. In this post, we’ll dive into the results of our recent…

4 minutes read
By Aparna Dhinakaran | 4 minutes read
LLM Evals Research

How to Make Your AI App Feel Magical: Prompt Caching

Credit to Harrison Chu for the research behind this post A key ingredient to making your AI app feel “magical” is speed—snappy feedback enhances user experience significantly. Companies like Cursor achieve this by “pre-warming” their cache, adding relevant information as soon as users start interacting. For those looking to boost AI app performance, prompt caching…

1 minute read
By John Gilhuly | 1 minute read
Research

Zero to a Million: Instrumenting LLMs with OTEL

Thanks to Roger Yang, Xander Song, and John Gilhuly for their contributions to this piece. A few months ago, we hit a significant milestone: our OTEL LLM instrumentation surpassed one million monthly downloads. This journey has been challenging but rewarding. We, along with other key players in the industry, are paving the way for observability…

3 minutes read
By Aparna Dhinakaran | 3 minutes read
Research

Comparing OpenAI Swarm with other Multi Agent Frameworks

Last week, OpenAI introduced Swarm, the latest addition to the rapidly evolving multi-agent framework space. Swarm joins the ranks of frameworks like CrewAI and Autogen, pushing the boundaries of how agents work collaboratively in complex AI systems. Although Swarm is still in its early stages, comparing it to established players like CrewAI and Autogen highlights…

4 minutes read
By John Gilhuly | 4 minutes read
Research
Blog image Sally-Ann Delucia and Kyle O'Brien

Composable Interventions for Language Models

Introduction We’re excited to be joined by Kyle O’Brien, Applied Scientist at Microsoft, to discuss his most recent paper, Composable Interventions for Language Models. Kyle and his team present a new framework, composable interventions, that allows for the study of multiple interventions applied sequentially to the same language model. The discussion will cover their key…

33 minutes read
By Sarah Welsh | 33 minutes read
Paper Readings Podcasts Research
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