LLM plus visual understanding, but yeah. For context, you could do this before, but models tended to be very off with edge detection and sizes.
RESEARCH
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Nine Months of Building: Muse Spark Model Launch Success
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Fun nine months! My first week i remember we had a long dinner in the cafeteria daydreaming about the cool research directions to pursue, then going to back to our desks to write a basic script to inference llama. Now we have a pretty complete stack and our first model is out 🥑 Alexandr Wang (@alexandr_wang) 1/ today we're releasing muse spark, the first model from MSL. nine months ago we rebuilt our ai stack from scratch. new infrastructure, new architecture, new data pipelines. muse spark is the result of that work, and now it powers meta ai. 🧵 — https://nitter.net/alexandr_wang/status/2041909376508985381#m
→ View original post on X — @_jasonwei, 2026-04-08 17:25 UTC
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Hamiltonian Monte Carlo: Physics-Based Probabilistic Sampling
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Hamiltonian Monte Carlo: probability as physics.
— Mathematica (@mathemetica) 8 avril 2026
Endow particles with momentum, then let Hamilton’s equations (dq/dt = ∂H/∂p, dp/dt = −∂H/∂q) carve reversible, volume-preserving trajectories through phase space.pic.twitter.com/Uf7MD4lqnqHamiltonian Monte Carlo: probability as physics. Endow particles with momentum, then let Hamilton’s equations (dq/dt = ∂H/∂p, dp/dt = −∂H/∂q) carve reversible, volume-preserving trajectories through phase space.
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Neurosymbolic AI: Symbol Placement in Transformer Systems
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please read my 2001 book that laid out what neurosymbolic AI is. you are just wrong. and the python script doing the work is a transformer NOT doing the work. it’s just a question of where you put the symbols. if you don’t have them somewhere in the system (a la what hinton
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AGI Discussion: Accelerating Science and Medicine Through AI
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Great to chat with fellow Londoner @HarryStebbings about the path to AGI and how we’re using AI today to accelerate science & medicine. Appreciated our discussion on the incredible talent & potential for deep tech here in the UK. Thanks for the kind words and for having me on!
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Self-Improving Agents: Systems Engineering and Evaluation Infrastructure
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Self-improving agents isn’t a single algorithm – it’s a systems engineering problem involving: – eval data curation + maintenance – experiment design to battle overfitting – an update algorithm – human review during the process & especially before prod we share practical learnings + a local research scaffold to autonomously hill-climb harness centered around evals our goal is to give everyone the tooling and infra to measure and iteratively their improve agents. Evals are training data for agents which fuels this loop let's build the future of well-designed, self-improving systems 🚀 Viv (@Vtrivedy10) x.com/i/article/204172946391… — https://nitter.net/Vtrivedy10/status/2041927488918413589#m
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Project Glasswing: Limited Distribution AI Initiative Unveiled
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correct: not publicly released, but see Project Glasswing for limited distribution
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AI hasn’t produced a single paperclip yet in 2026
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it's 2026 and ai has not even made a single paperclip
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PDR Framework: Parallel Reasoning Agents for Complex Scientific Queries
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Reasoning doesn’t have to mean longer chains of thought:
— Anirudh Goyal (@anirudhg9119) 8 avril 2026
PDR = draft in parallel → distill into a compact workspace → refine, and shift the Pareto frontier.https://t.co/4Sca6dFu4Q https://t.co/kk0fYpc8Y1 pic.twitter.com/PvevaX9ngYReasoning doesn’t have to mean longer chains of thought: PDR = draft in parallel → distill into a compact workspace → refine, and shift the Pareto frontier. arxiv.org/abs/2510.01123 Alexandr Wang (@alexandr_wang) 3/ we’re also releasing contemplating mode, which orchestrates multiple agents that reason in parallel designed to handle complex scientific & reasoning queries. in our testing we found it competitive w/ other extreme reasoning models such as Gemini Deep Think & GPT Pro. — https://nitter.net/alexandr_wang/status/2041909381667958855#m