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Intelligent Agents Redefine Research, Personalized Assistance, and Health Navigation

Cutting-edge AI systems are demonstrating significant strides as sophisticated, proactive partners across diverse domains. Original work includes Google DeepMind's AlphaEvolve, an LLM-powered coding agent making verifiable breakthroughs in theoretical computer science, and Google's TTD-DR, a deep research agent that autonomously drafts and refines complex reports. In personalized assistance, the 'Sensible Agent' offers unobtrusive, context-aware help in augmented reality, while Google Research's Personal Health Agent (PHA) and Wayfinding AI are pioneering tailored health insights and guidance using multimodal data and proactive conversations. These innovations collectively showcase AI's evolving capacity to emulate human expertise and collaboration, delivering efficient, personalized, and user-centric solutions.

calendar_today 2025-09-30 attribution research.google/blog/

AI as a research partner: Advancing theoretical computer science with AlphaEvolve

Google DeepMind introduces AlphaEvolve, an LLM-based coding agent that is making significant strides in theoretical computer science. This novel AI system discovers complex combinatorial structures and 'gadgets,' traditionally painstaking to find manually, which are crucial for proving universal statements in complexity theory. AlphaEvolve successfully improved the inapproximability bounds for MAX-4-CUT and discovered extremal Ramanujan graphs, significantly advancing average-case hardness understanding. Crucially, its findings are automatically verifiable with 10,000x speedup, ensuring mathematical rigor. This marks a new era for AI as a precise, verifiable research partner in mathematics.
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calendar_today 2025-09-19 attribution research.google/blog/

Deep researcher with test-time diffusion

Google introduces TTD-DR, an innovative Deep Research agent that models complex report writing as a diffusion process, mimicking human iterative research. This powerful framework drafts and refines its own content by leveraging high-quality retrieved information, achieving new state-of-the-art results in long-form research reports and multi-hop reasoning. TTD-DR employs component-wise self-evolution and report-level refinement via retrieval, significantly outperforming existing deep research agents in accuracy and efficiency. Its 'draft-first' design ensures coherence, making it a robust companion for real-world research tasks.
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calendar_today 2025-09-18 attribution research.google/blog/

Sensible Agent: A framework for unobtrusive interaction with proactive AR agents

AR agents often demand explicit verbal commands, proving awkward or disruptive in social settings. Google's 'Sensible Agent' research prototype tackles this by enabling proactive, unobtrusive assistance in AR, leveraging real-time context like gaze and hand availability. This framework understands 'what' to assist using multimodal sensing and 'how' to interact based on social context, employing subtle gestures and minimal visual cues. A user study revealed significant reductions in cognitive load and strong user preference, fostering a more collaborative human-agent relationship crucial for seamlessly integrated AR systems in daily life.
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calendar_today 2025-09-30 attribution research.google/blog/

The anatomy of a personal health agent

Google Research unveils a groundbreaking LLM-powered Personal Health Agent (PHA) that synthesizes multimodal wellness data to provide personalized, evidence-based health insights and coaching. This innovative multi-agent system, featuring data science, domain expert, and health coach sub-agents, addresses the complex, individualized nature of health needs. Rigorous evaluation across 10 benchmark tasks demonstrated that the collaborative PHA significantly outperforms monolithic and parallel multi-agent baselines, establishing a robust blueprint for future personalized health AI by emulating human expert team structures.
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calendar_today 2025-09-25 attribution research.google/blog/

Towards better health conversations: Research insights on a “wayfinding” AI agent based on Gemini

Tired of generic health information online? Google Research introduces a novel "Wayfinding AI" agent, powered by Gemini, that transforms passive Q&A into proactive, tailored health conversations. This research demonstrates an AI that actively seeks context, clarifies user goals, and guides individuals through complex health information. Through mixed-method studies, participants overwhelmingly preferred this approach, finding it more helpful, relevant, and personalized than traditional chatbots. The Wayfinding AI's design, featuring targeted questions and a two-column interface, enables deeper understanding and empowers users to articulate concerns effectively. This marks a significant step towards human-centered AI for health navigation.
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