Feng Zhu

Very excited to share our work on "Science in the Age of Foundation Models", where we discuss applications of foundation models in time series forecasting and PDEs including weather forecasting and aerodynamics.

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The Quiet Shift Redefining AI Roadmaps NVIDIA’s latest research on SLM Agents reveals an underappreciated truth: the AI landscape is undergoing a fundamental transformation that most roadmaps haven’t accounted for yet. 𝗪𝗵𝗮𝘁’𝘀 𝗥𝗲𝗮𝗹𝗹𝘆 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 1️⃣ Small > Big - The race to ever-larger models is giving way to optimized ensembles of specialized SLMs (Small Language Models). Why? Because real-world applications demand: 10-100x lower inference costs Sub-second latency for customer-facing applications 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁 𝗼𝗻 𝗲𝗱𝗴𝗲 𝗱𝗲𝘃𝗶𝗰𝗲𝘀 2️⃣ Agents, Not Monoliths - Tomorrow’s AI won’t be a single LLM trying to do everything, but coordinated teams of models where: Each handles what it does best (reasoning, retrieval, validation) Components can be independently updated/swapped 𝗙𝗮𝗶𝗹𝘂𝗿𝗲𝘀 𝗮𝗿𝗲 𝗶𝘀𝗼𝗹𝗮𝘁𝗲𝗱 𝗮𝗻𝗱 𝗿𝗲𝗰𝗼𝘃𝗲𝗿𝗮𝗯𝗹𝗲 3️⃣ The End of the "One Model" Fantasy - The industry is quietly moving beyond the myth that a single LLM can optimally handle all use cases. Hybrid architectures (SLMs + symbolic systems + RL) are proving more robust for: Compliance-heavy industries Real-time operational systems Mission-critical applications Strategic Implications for Leaders 1. 𝘊𝘰𝘴𝘵 𝘌𝘧𝘧𝘪𝘤𝘪𝘦𝘯𝘤𝘺 𝘢𝘵 𝘚𝘤𝘢𝘭𝘦 SLMs reduce inference costs from dollars to pennies per query Enable profitable deployment in high-volume applications (e.g., customer support, transaction monitoring) Change cloud spend from capital drain to competitive advantage 2. 𝘜𝘯𝘮𝘢𝘵𝘤𝘩𝘦𝘥 𝘈𝘥𝘢𝘱𝘵𝘢𝘣𝘪𝘭𝘪𝘵𝘺 Swap compliance modules for new regulations in days, not months Fine-tune customer-facing models to brand voice without full retraining Incorporate live data (inventory, market feeds) through specialized agents 3. 𝘉𝘶𝘪𝘭𝘵-𝘪𝘯 𝘙𝘦𝘴𝘪𝘭𝘪𝘦𝘯𝘤𝘦 No single point of failure (outages affect components, not entire systems) Clear audit trails for regulated decision-making Avoid vendor lock-in through modular architecture The Bottom Line: This isn’t just another technical improvement - it’s a fundamental shift in how we architect AI solutions. Companies that recognize this early will gain: ✔️ Faster time-to-production ✔️ Better unit economics ✔️ Future-proof systems The question isn’t if this will impact your AI strategy - it’s when you’ll need to adapt. 🔗 Dive deeper: SLM Agents Research Are your AI plans accounting for this shift? Let’s discuss in the comments. #AIStrategy #TechLeadership #BusinessTransformation #ArtificialIntelligence #DigitalInnovation For those of you, who has read so far 😏 𝙸̲𝚗̲𝚜̲𝚒̲𝚐̲𝚑̲𝚝̲𝚏̲𝚞̲𝚕̲ ?? Now what if i told you this post was written by a bunch of agents 🤖 !!! who picked, curated, formated and critiqued countless time, before bringing this to me.. felt like editor in chief of Daily Planet.. just that 🦸🏻‍♂️clark is powered by Gemini than the Sun  #crewai #AgentsEveryWhere

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We developed an open-source AI tool to design concrete mixes that are stronger, more sustainable, and ready to build with faster—speeding up construction while reducing environmental impact. https://lnkd.in/gPCk8tCM But the impact of this AI tool is not just hypothetical! Amrize used Meta’s AI-based technologies to design a new low-carbon mix, and successfully deployed it in an at-scale slab-on-grade application at Meta's new data center in Rosemont, MN. Compared to the legacy mix, this new AI-designed mix is: 🦁 Stronger ⏱ Faster 🍃 Lower carbon ⏱ ️The ideal set time All this was achieved without needing any new materials, nor special equipment. Best of all, the AI is open-sourced. https://lnkd.in/g2KA7KZW This work was featured in a Meta engineering blog article published today! https://lnkd.in/gBU9HY8H

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Fun fact: Some of the hardest math at Amazon isn’t about AI, it’s about trucks. Curious how we solve it? We built a solver, and today, it’s open-source. 🚀 👉 Check it out & drop us a ⭐: https://lnkd.in/epTCYN8a Meet 𝗝𝘂𝗟𝗦 (Julia Local Search), Amazon Science’s new open-source solver: Developed by scientists, for scientists, JuLS is fast, extensible, and open to community contributions. It combines Constraint-Based Local Search and Constraint Programming, and it’s already deployed in Amazon’s production environment to tackle complex middle-mile logistics and scheduling. JuLS performs on par with Google OR-Tools on standard tasks. Its real strength lies in handling black-box objectives and constraints, making it a powerful solution for real-world optimization scenarios that often require interfacing with proprietary or external systems. More than a solver, JuLS is a playground for researchers and practitioners tackling combinatorial optimization problems. Our team : Axel Navarro, Arthur Dupuis, Ilan Coulon, Maxime Mulamba Ke Tchomba, El Mehdi OUDAOUD

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My colleagues Amila Weerasinghe, Utkrisht Rajkumar and I are organizing "AI co-scientist: Accelerating scientific discovery through intelligent AI collaboration" workshop as part of Amazon Research Day 2026 event on April 30th. We will have invited talks by Peter Clark (Senior Research Director, Allen Institute for AI), Chenhao Tan (Associate Professor, University of Chicago), Wei Wang (Professor, UCLA), James Zou (Associate Professor, Stanford).   We will present our team's work on AtoZ AI Co-Scientist and our research projects in collaboration with UCSD ("AI Co-Scientist: Designing Autonomous Agents for ML", Prof. Rose Yu and Prof. Taylor Berg-Kirkpatrick) and UCLA ("Self-evolving Agents with Dynamic Temporal Knowledge Graphs: A Neuro-Symbolic Architecture for Continual Scientific Discovery" Prof. Wei Wang and Prof. Yizhou Sun).   We will also have a poster session. If you would like to present your work on AI Co-Scientists in the poster session please let me know.  

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Third piece in the AAD series is up. This one is about acceptance criteria (AC) and why treating them as a formal contract changes what you can diagnose when autonomous development breaks.  AC serve as the bridge between the spec and the environment in which the spec is compiled into a product. This is implicit in the first article, but after marinating on this for a few weeks, I felt like I should make it explicit.                                                                                                    The short version: most teams write AC the way you'd write them for a human. "Handle edge cases gracefully." "Performance should be acceptable under normal load." In AAD, those are  specification failures. An agent will do something when it reads that. It might even do something reasonable. But you've handed control over what "reasonable" looks like to the LLM, and you'll only find out whether you agree after you've burned the iteration cycle. In my experience, the first pass is ok, but it can quickly diverge and it takes a lot of time to get back to where you intended to go in the first place. One thing I keep coming back to is the diagnostic value of keeping the spec and the AC separate. My currently line of thinking leads me to this framing: The spec describes intent. The AC defines done. You need both. The Spec is the what and the why. The AC are the falsifiable tests that the intention was met. Keeping them separate creates a framework so they each get the attention they deserve as part of this new world. Building on the notion of falsifiability, there's also a section on the right mindset for writing AC. Just like with the best scientist/experimentalist, you should be writing AC from a lens that lets you confirm intention was not met, not that intention was met. It's a subtle but meaningful nuance. If you assume the agent is going to get it correct, you're biased to see the final product and assume it's correct. When you train yourself to focus on the ways the agent might get it wrong, you open up a whole new world on writing great AC. I think this applies to traditional development as well as AAD. Claude made a fancy doc for this one! But also, the full read is in the comments.

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