Arslan Aziz

Among other things, my team has been focusing on AI research for agents. We just blogged about a recent project: the Test-Time Diffusion Deep Researcher (TTD-DR). This framework uses a Deep Research agent to draft and revise its own drafts using high-quality retrieved information. This approach achieves new state-of-the-art results in writing long-form research reports and completing complex reasoning tasks. The work achieves better results than any other deep research agent we compared to. It also shows that self-evaluation and self-improvement are must-have components of a successful agent. https://lnkd.in/gBZpVMQy

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The emergent multi-agent applications rely on sophisticated language models and often need to interact with other systems, built on other frameworks and crossing organizational boundaries. Two distinct protocols play critical roles in enabling these capabilities: Anthropic’s Model Context Protocol (MCP) and our recently announced Google Agent-to-Agent (A2A) interoperability protocol. Both relate to agentic AI, but, they address different levels and aspects of agentic system design. During our Google NEXT 2025 conference, after we annoucned this, as an author of this protocol, I was asked many questions by our customers and partners and academics. One of the most salient questions were around how these protocols compare and contrast. This article aims to clarify their specific functions, differences, and how they can complement each other. Let me know if you have any questions. https://lnkd.in/gKbQa8sM #A2A #AgenticAI #Agents #MultiAgentSystems #EnterpriseAI Google Cloud #Agent2Agent #Interoperability #GenAI

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Our Open Human Preferences paper is published in Nature Machine Intelligence 🔗 https://rdcu.be/er3Hp the paper explores the collection of preference datasets for aligning LLMs and proposes strategies for making it open.

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😤 Ever wonder where reinforcement learning actually boosts (or hurts) LLM’s reasoning capabilities? Meet 💥 SPARKLE 💥 —a new analysis framework that dissects gains from RL in planning, knowledge integration, and subproblem solving. SPARKLE is a fine-grained framework for analyzing models’ reasoning ability along three key axes: 1. Plan-following & execution 2. Knowledge utilization 3. Sub-problem decomposition These axes reveal where RL helps (or hurts) reasoning — insight that plain accuracy can’t show.   📜 Paper: https://lnkd.in/g3VekeTB 📽️ Project: https://lnkd.in/g3PFn3aE ⏲️ Code: https://lnkd.in/gCHUAgue X: https://lnkd.in/gEYdwhh8

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𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝗶𝘁 𝗺𝗲𝗮𝗻 𝘁𝗼 𝘀𝗰𝗮𝗹𝗲 𝗰𝗼𝗻𝘀𝘂𝗺𝗲𝗿 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝘀 𝗶𝗻 𝗮𝗻 𝗔𝗜-𝗳𝗶𝗿𝘀𝘁 𝘄𝗼𝗿𝗹𝗱? Dived deep into this at the roundtable at Bits n Atoms, hosted by The Product Folks last night — and the conversations stuck with me. Scaling consumer products in an AI world demands thinking across multiple dimensions, and these are just some of the lenses through which our group explored it.  • 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘀𝗮𝘁𝗶𝗼𝗻 — not just smart recommendations, but designing for one and actually impacting behaviour change  • 𝗧𝗿𝘂𝘀𝘁 — especially for this new generation of AI-native products, where UX becomes critical to adoption  • 𝗖𝗼𝘀𝘁-𝗲𝗳𝗳𝗲𝗰𝘁𝗶𝘃𝗲𝗻𝗲𝘀𝘀 — using AI to boost efficiency and also designing AI systems that can scale without exploding infra or model costs  • 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝘁𝗶𝗼𝗻 — whether through subscriptions or new models that make sense for AI-first experiences  • 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗶𝗼𝗻 & 𝗚𝗧𝗠 — this differentiation matters more than ever, in a world where building products is getting faster and cheaper everyday  • 𝗔𝗰𝗰𝗲𝘀𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 — across regions, languages, devices, and modalities. If it only works for the few, does it really scale? We’re in an interesting moment — where product thinking is expanding, not just because of what AI can do, but because of the questions it’s forcing us to ask. Loved the sharp, honest, and wide-ranging perspectives from everyone Shreyanshi C. Kushal Khandelwal Ashish Jain Madhav Bhartia Tanmay Saxena Pranav Agrawal Sachin Kamkar Shashank Raghavendra Nikhil Ankan Huge shout out to Abhay Jani Aditya Mohanty Suhas Motwani for driving these thoughtfully curated and highly impactful events!

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