TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture

CUGA Agent: From Benchmarks to Business Impact of IBM's Generalist Agent

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 foundations, achieving state-of-the-art performance on AppWorld and WebArena. Beyond benchmarks, it was evaluated in a pilot within the Business-Process-Outsourcing talent acquisition domain, addressing enterprise requirements for scalability, auditability, safety, and governance.  CUGA code: https://github.com/cuga-project/cuga-agent  Paper: https://arxiv.org/abs/2510.23856 Learn more about AI observability and evaluation [https://arize.com/llm-evaluation/], join the Arize AI Slack community [https://arize.com/community/] or get the latest on LinkedIn [https://www.linkedin.com/company/arizeai/] and X [https://twitter.com/arizeai].

TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture

We dive into the latest paper from Google and a team of academic researchers: "TUMIX: Multi-Agent Test-Time Scaling with Tool-Use Mixture [https://arxiv.org/abs/2510.01279]." 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 the question and previous answers. In experiments, TUMIX achieves significant gains over state-of-the-art tool-augmented and test-time scaling methods. Learn more about AI observability and evaluation [https://arize.com/llm-evaluation/], join the Arize AI Slack community [https://arize.com/community/] or get the latest on LinkedIn [https://www.linkedin.com/company/arizeai/] and X [https://twitter.com/arizeai].

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 [https://ai.meta.com/research/publications/are-scaling-up-agent-environments-and-evaluations/] titled “ARE: scaling up agent environments and evaluations" and the new ARE and Gaia2 [https://arize.com/blog/meta-ai-researcher-explains-are-and-gaia2/] frameworks. Learn more about AI observability and evaluation [https://arize.com/llm-evaluation/], join the Arize AI Slack community [https://arize.com/community/] or get the latest on LinkedIn [https://www.linkedin.com/company/arizeai/] and X [https://twitter.com/arizeai].

Georgia Tech's Santosh Vempala Explains Why Language Models Hallucinate, His Research With OpenAI

Santosh Vempala, Frederick Storey II Chair of Computing and Distinguished Professor in the School of Computer Science at Georgia Tech, explains his paper [https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4aaa5/why-language-models-hallucinate.pdf] co-authored by OpenAI's Adam Tauman Kalai, Ofir Nachum, and Edwin Zhang. Read the paper: Sign up for future AI research paper [https://arize.com/ai-research-papers/] readings and author office hours. See LLM hallucination examples here [https://arize.com/llm-hallucination-examples/] for context. Learn more about AI observability and evaluation [https://arize.com/llm-evaluation/], join the Arize AI Slack community [https://arize.com/community/] or get the latest on LinkedIn [https://www.linkedin.com/company/arizeai/] and X [https://twitter.com/arizeai].

Atropos Health’s Arjun Mukerji, PhD, Explains RWESummary: A Framework and Test for Choosing LLMs to Summarize Real-World Evidence (RWE) Studies

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 [https://arize.com/blog/atropos-healths-arjun-mukerji-phd-explains-rwesummary-a-framework-and-test-for-choosing-llms-to-summarize-real-world-evidence-rwe-studies/] from structured study output — an important but often under-tested scenario compared to the typical “summarize this PDF” task. Learn more about AI observability and evaluation [https://arize.com/llm-evaluation/], join the Arize AI Slack community [https://arize.com/community/] or get the latest on LinkedIn [https://www.linkedin.com/company/arizeai/] and X [https://twitter.com/arizeai].