AI Dynamics

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AI Dynamics

  • Model Improvements Enable IMO Gold Quality with Smaller Inference

    Also a less obvious but noteworthy point is that we are able to serve IMO gold quality now to more users because model improvements lead to requiring a smaller inference time topology than the IMO competition itself. 😀

    → View original post on X — @yitayml, 2026-02-12 23:35 UTC

  • Decoupled Diffusion Inverse Solver: Solving PDEs with Minimal Data

    I started in physics with numerical simulations, then accidentally found my way into ML. This Caltech SURF project let me bring those ideas back to physics beyond textbooks. The journey was nonlinear, full of surprising empirical and theoretical discoveries. Grateful for the guidance and opportunity. A formative experience at the frontier of computational physics × generative models. Prof. Anima Anandkumar (@AnimaAnandkumar) Solving Inverse PDEs with 1% Paired Data: Introducing Decoupled Diffusion Inverse Solver We propose a data-efficient and physics-aware diffusion framework for solving inverse problems on function spaces. In scientific machine learning, solving inverse problems requires costly and limited data acquisition from physical systems. Existing joint-embedding diffusion models require massive paired training data, as they represent the underlying physics implicitly through statistical correlations. In this work, we identify that under data scarcity, the observation-induced guidance signal vanishes during posterior sampling, making reconstruction impossible. Our Solution: We propose a decoupled design against joint-embedding: an unconditional diffusion learns the coefficient prior, while a neural operator explicitly models the forward PDE for guidance. This enables (1) superior data efficiency (2) effective physics-informed learning and sampling. Performance: Achieves state-of-the-art results on Navier-Stokes, Helmholtz, and Poisson benchmarks, improving spectral error by 54% on average. Data Efficiency: DDIS maintains high accuracy even when limited to just 1% of paired training data, outperforming joint models by 40% in L2 error. Robustness: Theoretical guarantees that avoid the guidance attenuation identified in joint-embedding methods. Check out the paper for the full theoretical analysis and experiments! arxiv.org/abs/2601.23280 Thomas Lin , @jiacheny7, Alex Chiang, Julius Berner, #MachineLearning #DiffusionModels #InverseProblems #PDE #NeuralOperators @Caltech #AI4Science — https://nitter.net/AnimaAnandkumar/status/2019510545242890370#m

    → View original post on X — @animaanandkumar, 2026-02-06 03:52 UTC

  • Automate Your Enterprise with Abacus AI

    Use Abacus AI to automate your enterprise

    → View original post on X — @abacusai

  • Kimi K2.5 Thinking Launches on ChatLLM Platform

    Kimi K2.5 Thinking is now live on ChatLLM by Abacus AI. Start using it now inside ChatLLM (and via our CLI), alongside 20+ LLMs already integrated. Kimi K2.5 Thinking is one of the strongest APIs, 10x cheaper than opus 4.5! China is leading the open-sourced AI race.

    → View original post on X — @abacusai

  • Using the Most Expensive Model Available

    Whatt do you mean? we literally use the MOST EXPENSIVE model

    → View original post on X — @abacusai

  • PlaNet: Learning Latent Dynamics for Planning from Pixels

    #PaperADay 12
    2019: Learning Latent Dynamics for Planning from Pixels (PlaNet) https://
    arxiv.org/pdf/1811.04551 This was the forerunner to the Dreamer 1/2/3/4 series of RL agents / papers, which I am going to read in sequence. Planning is common in tasks with fully specified transition

    → View original post on X — @id_aa_carmack

  • State-of-the-art reinforcement learning algorithms replacing traditional methods

    #PaperADay 11
    Discovering state-of-the-art reinforcement learning algorithms https://nature.com/articles/s41586-025-09761-x
    … The paper is about replacing the traditional RL target generation algorithms (policy gradient, GAE, TD-lambda, etc) that look at a sequential block of agent frame predictions

    → View original post on X — @id_aa_carmack

  • New Gemini CLI Course: Open-Source Agent for Coding Workflows

    New course: Gemini CLI: Code & Create with an Open-Source Agent, built with @googlecloudtech/@geminicli and taught by @JackWoth98. Agentic coding assistants like Gemini CLI are transforming how developers work. This short course teaches you to use Google's open-source agent to coordinate local tools and cloud services for coding and non-coding workflows. Gemini CLI works from your terminal, so it works with your local files and development tools. You can also connect it to services through MCP. Then provide high-level instructions, and it autonomously plans and executes complex workflows. Skills you'll gain: – Build website features and automate code reviews with GitHub ActionsCreate data dashboards that combine local files with cloud data sources – Use MCP servers and extensions to orchestrate workflows across GitHub, Canva, and Google Workspace – Generate social media content from multimedia files like conference recordings I particularly appreciate that Gemini CLI is open-source. You can see exactly how it works, read the prompts it uses, and understand its architecture. The community has contributed thousands of pull requests. Since Gemini 3’s release I've found Gemini CLI highly capable – this is a tool worth having in your toolbox! Whether you're prototyping applications, automating workflows, or working with multimedia content, join to learn to delegate complex tasks and build faster: deeplearning.ai/short-course…

    → View original post on X — @andrewyng, 2026-01-22 17:36 UTC

  • RouteLLM Routes Prompts to Optimal AI Models on ChatLLM

    RouteLLM is available on ChatLLM and routes to the best model based on your prompt We use the following models – GPT 5.2
    – Sonnet 4.5
    – Opus 4.5
    – Gemini 3.0 Flash
    – Grok 4.1 We combine them all to give you the best experience

    → View original post on X — @abacusai