Anthropic is an AI safety and research company that's working to build reliable, interpretable, and steerable AI systems. Read more ›
Sphere neural networks have achieved symbolic level syllogistic reasoning without training data, raising the question of where the limit of the scaling law for logical reasoning lies, i.e., whether data-driven machine learning systems can achieve the same level by increasing training data and training time. We show two methodological limitations that prevent supervised deep learning from reaching the symbolic-level syllogistic reasoning: (1) tra... Read more ›
HackMyClaw is over. No one was able to crack Fiu, and the challenge became too expensive to keep running. Thanks to everyone who participated. Read more ›
2 days left to lock in your spot at TechCrunch Founder Summit 2026 and save up to $190 before Early Bird rates expire on June 26 at 11:59 p.m. PT. Register here. Read more ›
Contribute to infiniteregrets/kv-psi development by creating an account on GitHub. Read more ›
“Murakkab” is a new automated system that streamlines the design of agentic workloads for AI applications and optimizes their deployment for customers, reducing computation and cost while boosting energy efficiency. Read more ›
Nature - On the robustness of topological gap detection via transport Read more ›
\"One AI agent across your browser, tools, and messages \" Discussion \| Link Read more ›
Meet the new and improved Facebook Creator Studio—now with AI—to manage content, track insights and grow your audience. Learn what's new. Read more ›
New models are launching in Asia that promise Mythos-like capabilities without fear of an export ban. U.S. AI labs may never recover this enormous market. Read more ›
Electroencephalography (EEG) is the dominant non-invasive modality for brain-computer interfaces (BCIs), yet reliable decoding of motor imagery is hampered by inter- and intra-individual variability. A recurring claim is that one decoding pipeline, most often a spatial or Riemannian method, is broadly preferable. We test the weakest version of that claim under the most favourable conditions. Using the Mother of All BCI Benchmarks (MOABB) frame... Read more ›
AI models have progressed to the point where their capabilities have real political consequences. Dealing with those consequences will require collective action. Read more ›
We present CyberChainBench, a benchmark for evaluating LLM-based agents on smart contract security across three complementary tasks: vulnerability detection, exploit generation, and patch synthesis. Built from 541 real-world exploit incidents from DeFiHackLabs spanning 9 EVM chains, the benchmark provides end-to-end on-chain evaluation where agents interact with historical blockchain state through isolated evaluation environments orchestrated ... Read more ›
A transpiler from stateful imperative workflows to declarative DSPy programs - theramkm/dspyer Read more ›
Learn how to secure, monitor, and remediate Gemini and Google API keys. Read more ›
A machine-learning model trained on thousands of electrocardiogram recordings identifies a previously unrecognized group of at-risk people. A machine-learning model trained on thousands of electrocardiogram recordings identifies a previously unrecognized group of at-risk people. Read more ›
A multi-agent protocol pairing a tool-using Scientist with a question-only advisor — no tools, no answers, no directives — improves Kaggle test performance on 4 of 5 MLE-bench tasks - hexo-ai/socrates Read more ›
Standard chain-of-thought on moral dilemmas exhibits two failure modes: stakeholder collapse (the trace names at most one party with a stake in the outcome) and uncertainty suppression (no explicit unknowns or hedges before committing to an action). We introduce narration-of-thought (NoT), a system prompt that structures chain-of-thought into five sections: protagonist, stakeholders, two-step consequences, uncertainty, then commitment. NoT adds ... Read more ›
This morning I saw , describing a small but effective inpainting model - a model where you can mark regions of an image to remove and the model imagines what should fill the space. The released model , but since it described itself as 0.2B I decided to try and get it running using WebGPU in a browser. TL;DR: I got it working, and you can try the demo at The finished tool Here's a video demo of the finished tool: You can open any image in it (non-square images get letterboxed), highlight areas... Read more ›