AI PhD @aims_oxford and @bold_lab_ai, SR @GoogleDeepMind, ex RS @JPMorgan AI, EE with Great Distinction @nyuniversity, Tiramisu and Comedy enthusiast
London, England
Joined October 2021
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INTENT FACTORED FOR THE WIN!
Good science that works always finds a way
#ifftw we're going to NeurIPS.
Fresh, improved and updated IFG paper dropping soon 👀
Uljad retweeted
We are releasing our first model, Flint, a best-in-class local OSS coding model that runs at 150+ tokens/s on an @nvidia DGX Spark and 125+ tokens/s on a MacBook Pro M4—developed from scratch in less than four months with help from @Recursive_SI's autoresearch system.
Introducing Flint, @Recursive_SI’s first model: a fast local coding model built with our automated research system, the Eureka Machine.
• Best-in-class fast local coding
• Up to 2x faster than comparable models: 125+ tokens/sec on a MacBook Pro, 150+ on RTX Spark
• Built from scratch in under 4 months
Private, unmetered intelligence on your own machine. Open weights available now on @huggingface, run it locally on your favorite framework or try it for free on @opencode or @pidotdev.
Knowing when to stop is one of the most important questions of our time
Uljad retweeted
The loop routine becomes 4 steps on the sphere: 1. Spherical linear interpolate (Slerp) a fraction toward the input 2. Apply the nGPT blocks 3. Rotate a fraction toward the proposal 4. Halt once the angle between state and proposal is below some threshold for every token. (3/9)
Uljad retweeted
Effective immediately:
👑 MPC: Model Predictive Control
🔌 API: Agent Peripheral Interface (formerly Model Context Protocol)
🛠️ GCC: Group Communication & Coordination (formerly Multi-Party Communication)
A groundbreaking Frontier result!
@maxencefaldor and @robertarail did a phenomenal job in showing the Argon capabilities so effectively. What a model and what a team in every sense of the word!
Uljad retweeted
Four Gemini 4 Argon agents, one shared Minecraft world, one goal: defeat the Ender Dragon. 27 hours and 2,783 self-written programs later, the dragon fell.
Uljad retweeted
Four Gemini 4 Argon agents teamed up in Minecraft to defeat the Ender Dragon in 27 hours, writing every move as code and coordinating in the game's chat.
Great work led by @maxencefaldor with Satinder Singh.
Read more in our technical report:
arxiv.org/pdf/2610.22319.
Will soon release new methods to better describe motion and instantaneous change. Finalizing notation atm
Uljad retweeted
Experts doubted a small neural net could beat Craftax without an LLM's knowledge.
Our agent meta-harness built one in 3 weeks. The 15M-param agent beats the final boss 44% of the time, costing a cent, and 10 seconds.
GPT-6 Astra won 1%, taking 24 hours and $83. 🧵
Ben taught me so much about doing proper research in RL and the ways to debug policy gradients. Glad the Science of RL team and @reflection_ai gets to have this expertise for their models!
This is great news! Congratulations to the team!
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
Uljad retweeted
Introducing Beam: a highly efficient agentic open model with 501B total parameters and 23B active.
- Frontier reasoning efficiency
- Advances the Western open frontier on coding & agentic tasks
- Trained end-to-end from scratch
Full weights release this month.
Learn more about Beam: reflection.ai/beam
Uljad retweeted
In LLM post-training, policy gradient is "the RL loss".
When it fails, we blame the RL: exploration, credit assignment, sampling noise.
Image classification has none of these, and the policy gradient is exact. Following it is terrible...
ImageNet, 4% vs 62% for cross-entropy.
Uljad retweeted
The critical distinction between base LLMs (2024 and earlier) and modern LRMs is not symbolic tool use. It's the switch from a transductive paradigm (intuit the answer to the query) to an inductive paradigm (intuit the program/instructions that produce the answer to the query).
They're trained to be inductive, and they perform test-time induction, i.e. test-time prediction of a NL program / reasoning chain. This unlocks entirely new capabilities -- in particular fluid intelligence. Base LLMs, to this day, have ~0 fluid intelligence. LRMs have substantial levels of fluid intelligence.
The performance of LLMs on ARC 1 (a benchmark from 2019) remains ~10-15% today. Scaling them up by a factor ~100,000x got them from 0% to 10%. Meanwhile LRMs the same size or smaller saturated ARC 1 in 2025.
Uljad retweeted
Very excited to announce Gemini 4 Argon, our new Frontier AI model, and a huge step forward across key capabilities. We’re focused on rolling it out responsibly starting with government and trusted cyber defenders through our Fairwind Program today, before wider availability soon
