Deep Papers

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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 To…

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 abou…

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 a…

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 –…

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 meanin…

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 langua…

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 …

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 trai…

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 mode…