Your shot at VC access, global visibility, TechCrunch coverage, and $100K equity-free funding is running out. Deadline to apply is May 27. Apply now.
We haven’t heard much about Warner Bros.’ forthcoming Supergirl, starring Milly Alcock in the title role, since the first teaser dropped back in December. But with its summer release approaching, the studio just released the first official full trailer, and it’s definitely a crowd-pleaser. As previously reported, we met Alcock’s Supergirl briefly at the end of Superman, when she showed up to collect her dog Krypto, still a bit hungover from partying on a red-sun planet. She is […]
arXiv:2602.09071v1 Announce Type: new Abstract: The ability to automatically classify source code repositories with ”topics” that reflect their content and purpose is very useful, especially when navigating or searching through large software collections. However, existing approaches often rely heavily on README files and other metadata, which are frequently missing, limiting their applicability in real-world large-scale settings. We present DRAGON, a repository classifier designed for very large and diverse software collections. It operates entirely on lightweight signals commonly stored […]
Livestock growth prediction is essential for optimising farm management and improving the efficiency and sustainability of livestock production, yet it remains underexplored due to limited large-scale datasets and privacy concerns surrounding farm-level data. Existing biophysical models rely on fixed formulations, while most machine learning approaches are trained on small, isolated datasets, limiting their robustness and generalisability. To address these challenges, we propose LivestockFL, the first federated learning framework specifically designed for livestock growth prediction. LivestockFL enables collaborative model […]
Fireside chat with Vincent Granville, CAIO & Co-Founder at BondingAI.io. Hosted by Benjamin Johnson, CEO & Co-Founder at Particle41. Dive into AI advancements with Vincent Granville, as he reveals breakthrough methods in hallucination-free AI models and synthetic data innovations. Join us in this episode of the Particle Accelerator Podcast as we welcome Vincent Granville, Co-Founder at BondingAI. With over 25 years of experience in statistics and AI, Vincent Granville shares valuable insights on building hallucination-free AI models and […]
arXiv:2510.14582v2 Announce Type: replace Abstract: Causal discovery methods can identify valid adjustment sets for causal effect estimation for a pair of target variables, even when the underlying causal graph is unknown. Global causal discovery methods focus on learning the whole causal graph and therefore enable the recovery of optimal adjustment sets, i.e., sets with the lowest asymptotic variance, but they quickly become computationally prohibitive as the number of variables grows. Local causal discovery methods offer a more scalable […]
arXiv:2604.05112v1 Announce Type: new Abstract: Recent progress in in-context reinforcement learning (ICRL) has demonstrated its potential for training generalist agents that can acquire new tasks directly at inference. Algorithm Distillation (AD) pioneered this paradigm and was subsequently scaled to multi-domain settings, although its ability to generalize to unseen tasks remained limited. The Decision Pre-Trained Transformer (DPT) was introduced as an alternative, showing stronger in-context reinforcement learning abilities in simplified domains, but its scalability had not been established. In […]
TimF left a comment on my guitar pick post saying the image was a “squircle-ish analog for an isosceles triangle.” That made me wonder what a more direct analog of the squircle might be for a triangle. A squircle is not exactly a square with rounded corners. The sides are continuously curved, but curved most at the corners. See, for example, this post. Suppose the sides of our triangle are given by L1(x, y) = 1 for i […]
The rapid advancement of artificial intelligence (AI) has created unprecedented demand for specialized models capable of complex reasoning tasks, particularly in competitive programming where models must generate functional code through algorithmic reasoning rather than pattern memorization. Reinforcement learning (RL) enables models to learn through trial and error by receiving rewards based on actual code execution, making it particularly well-suited for developing genuine problem-solving capabilities in algorithmic domains. However, implementing distributed RL training for code generation presents significant infrastructure […]
arXiv:2601.14277v1 Announce Type: new Abstract: Quantization is a practical technique for making large language models easier to deploy by reducing the precision used to store and operate on model weights. This can lower memory use and improve runtime feasibility on constrained hardware, which is especially relevant for users running models locally. Quantization in llama.cpp enables large language models to run on commodity hardware, but available formats are often evaluated inconsistently, making it hard to choose among schemes. We […]