Deep Papers

• Scalable Chain of Thoughts via Elastic Reasoning

  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 contributes to more concise and efficient reasoning, even in unconstrained settings, and its implications for deploying LRMs in resource-limited environments.Read the paper here: https://arxiv.org/pdf/2505.05315Sign up for the next discussion & see more AI research: arize.com/ai-research-papersLearn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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

  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 new benchmarks—Stateful GSM-Symbolic, Stateful AIME, and the multi-query extension of GSM—that show up to 5x lower compute at inference, 2.5x lower cost per query, and up to 18% higher accuracy when scaled.​You’ll also see how this method applies to realistic agent use cases and what makes it most effective.If you care about LLM efficiency, scalability, or cutting-edge research.Explore more AI research, or sign up to hear the next session live: arize.com/ai-research-papersLearn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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• LibreEval: The Largest Open Source Benchmark for RAG Hallucination Detection

  For this week's paper read, we actually 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 largest public dataset of hallucinations, as well as a series of fine-tuned evaluation models.We talk about what we built, the process we took, and the bottom line results.📃 Read the paper: https://arize.com/llm-hallucination-dataset/Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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

  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.Read it on the blog: https://arize.com/blog/ai-benchmark-deep-dive-gemini-humanitys-last-exam/Sign up to watch the next live recording: https://arize.com/resource/community-papers-reading/Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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• Model Context Protocol (MCP)

  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 interoperability for agentic workflows, and the development of more capable AI agents that can execute complex tasks in real-world environments.Learn more about AI observability and evaluation, join the Arize AI Slack community or get the latest on LinkedIn and X.

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