Research

This talk challenges the assumption that advanced frontier models are necessary for building effective voice agents. The presenters argue that simpler, more focused models can achieve comparable or even superior results for specific voice agent tasks, emphasizing practicality and efficiency over raw model power. They advocate for a pragmatic approach to agent development, prioritizing task-specific capabilities.

This talk argues that the core challenge in software engineering is shifting from writing code to managing complexity, designing systems, and ensuring correctness. While AI models now excel at generating code syntax, the future of software development lies in leveraging AI for higher-level tasks like architectural design, complex problem decomposition, and inductive reasoning. The economics of code production are changing, making code generation nearly free and emphasizing the need for new processes and evaluation methods.

Benoit Schillings, VP of Research at Google DeepMind, outlines the frontier of AI engineering research, emphasizing the shift from foundational model development to sophisticated engineering for product integration and real-world impact. The agenda focuses on building robust, scalable, and reliable AI systems that can be productized and deployed effectively. This involves a deep dive into the engineering challenges and opportunities at the forefront of AI research.

This talk explores how autoresearch is transforming the landscape of machine learning research. It delves into the methodologies and tools that enable automated research processes, aiming to accelerate discovery and innovation within the ML field. The core thesis is that by automating repetitive and time-consuming research tasks, scientists can focus on higher-level problem-solving and conceptualization.

This talk addresses the ambiguity of "done" in the context of AI agents, arguing that a simple boolean check is insufficient. It proposes that "done" is a bundle of claims requiring verification across multiple dimensions, including artifact production, evidence of completion, adherence to a rubric, clear ownership for next steps, and survival of real-world conditions. The core thesis is that agent systems need a liveness model to manage the tension between keeping work moving and ensuring its quality.

This talk addresses the challenge of transitioning frontier machine learning research into production-ready software. It proposes a three-pronged approach focusing on research legibility, code structure, and decomposition strategies to improve the velocity and efficiency of teams bridging the gap between ML research and software engineering. The core idea is to treat this transition as a systems and process problem.

This talk explores the integration of coding agents, specifically the OpenClaw agent, into products. It emphasizes that coding agents are becoming a fundamental building block for software systems and highlights the Pi framework as an excellent tool for experimentation and development. The presentation demonstrates how agents can be leveraged to automate complex workflows, such as processing sales proposals, by interacting with various tools and systems.

This talk introduces FLUX, an open-source visual AI model developed by Black Forest Labs, and discusses its evolution and the future of visual AI. The presentation highlights FLUX's capabilities in text-to-image generation, image editing, and its progression towards visual intelligence. It also delves into a novel training methodology called Self Flow, designed to improve multimodal generative models by integrating representation learning directly into the generation process, eliminating the need for external encoders.

This talk provides a deep dive into Gemma 4, Google DeepMind's latest family of open-source AI models. The presentation highlights significant architectural improvements and new capabilities, including enhanced multimodal support and optimized performance for both on-device and complex reasoning tasks. Gemma 4 aims to set a new standard for small, open-source models, making advanced AI more accessible to developers.

This workshop introduces a system for automating the creation of technical articles, moving beyond generic AI-generated content. It details the architecture and implementation of a deep research agent and a writing agent, designed to work together to produce high-quality, human-like content. The system aims to augment human writers by handling the research and initial drafting, allowing for more efficient and scalable content production.

This talk explores Google DeepMind's research into the next frontiers of AI, focusing on advancements beyond traditional language models. It highlights progress in creating more robust and versatile AI systems, including multimodal embedding models, advanced weather prediction systems, and interactive 3D world models. The core thesis is that significant breakthroughs lie in developing AI that can understand and interact with the world in more comprehensive and nuanced ways, moving towards more general intelligence.

Paperclip is an open-source human control plane for AI labor, designed to orchestrate AI agents for accomplishing real-world tasks. It allows users to set up an organizational chart of agents, manage their interactions, and invoke specific preferences to complete work. The system emphasizes user involvement from high-level design to execution, enabling the creation of businesses that can largely run themselves with human oversight.

This talk introduces Manus, an AI action engine designed to go beyond providing answers and instead execute tasks, automate workflows, and extend human capabilities. The presentation highlights the platform's evolution, including Manus 1.5, which offers increased speed and quality. Manus aims to be a general AI agent accessible through various interfaces like web applications, Slack, and a newly launched API, meeting users wherever they are.

This talk introduces enterprise deep research as a powerful application for AI agents, focusing on its ability to conduct in-depth, multi-step investigations within private enterprise data. It highlights the challenges of factual accuracy in generative AI and presents an agent operating system designed to mitigate hallucinations and ensure robust information retrieval for enterprise-grade deployments.

This talk reviews recent advancements in AI research, focusing on the DeepSeek models and the evolution of training methodologies. It introduces a new "Test of Time" paper club initiative aimed at systematically covering foundational AI concepts. The discussion highlights how reinforcement learning and extended inference time are enabling models to develop advanced reasoning capabilities, leading to significant performance improvements.

This talk demonstrates how to build AI agents using Vercel's AI SDK, progressing from fundamental concepts to a practical deep research project. It highlights the SDK's unified interface for model switching, tool-use capabilities for interacting with the external world, and structured output generation for creating type-safe data. The deep research example showcases how to break down complex tasks into agentic workflows, combining web searching, result analysis, and recursive query generation to produce comprehensive reports.

This talk details the development of Deep Research, a personal research agent integrated into Gemini Advanced. The core thesis is that by removing compute and latency constraints at inference time, AI models can perform extensive web browsing to generate comprehensive answers, addressing the limitations of traditional chatbots that often provide blueprints rather than direct solutions. The presentation highlights the product and technical challenges encountered in building such an asynchronous, long-form research tool within a synchronous chatbot interface.

This talk explores the potential of reinforcement learning (RL) to advance AI agents beyond current chatbot and reasoner capabilities. It posits that RL offers a path to developing more autonomous systems that can learn and improve through interaction with their environment, moving beyond static prompt engineering and tool-calling approaches. The discussion highlights emerging trends and open-source efforts in this domain, suggesting a future where RL is integral to agent engineering.

This talk discusses the system design challenges and future directions for running and training next-generation large language models. It highlights the significant computational and memory bandwidth requirements for inference, particularly for models with trillions of parameters. The presentation also touches upon the massive scale of infrastructure being built for training these models and the associated engineering hurdles.

This talk introduces the fundamental concepts of AI agents, detailing their components and use cases. It emphasizes that agents are best suited for complex, multi-step tasks requiring the integration of various capabilities, such as data aggregation, visualization, and reasoning, or when personalization and adaptive responses are necessary. The session also provides a hands-on guide to building a research agent from scratch, incorporating tools, memory, and reasoning patterns.

This talk explores the concept of latent spaces within AI models, particularly focusing on embeddings, as a means to gain more direct control and understanding of model behavior. Instead of relying solely on indirect prompting, the speaker advocates for interacting with the internal representations of models to achieve more precise manipulation and insight into their decision-making processes.