Extracting books from production language models (2026)
Substantial amounts of copyrighted text can be extracted from production LLMs, with findings indicating that models like Claude 3.7 Sonnet can output entire books near-verbatim, achieving an nv-recall of 95.8% in some cases.
AI solves Erdos problem #728 (Terence Tao mathstodon post)
Terence Tao discusses the impact of AI tools on solving problems related to Erdős, highlighting their potential to enhance mathematical research and discovery.
Show HN: EuConform – Offline-first EU AI Act compliance tool (open source)
EuConform is an open-source tool designed to ensure compliance with the EU AI Act, enabling users to classify risk levels, detect algorithmic bias, and generate compliance reports entirely offline, adhering to GDPR and WCAG 2.2 AA standards.
GPU memory snapshots: sub-second startup (2025)
GPU memory snapshots enhance cold start performance by capturing the entire GPU state, enabling workloads to start up up to 10x faster than traditional methods, significantly improving user experience in serverless environments.
[D] deepseek published a new training method for scaling llms. anyone read the mhc paper?
Deepseek's new method, manifold constrained hyper connections (MHC), aims to enhance performance in large language models (LLMs) by allowing internal information sharing while maintaining stability, as detailed in their arXiv paper.
How we made v0 an effective coding agent
The v0 Composite Model Family enhances coding efficiency through a multi-step agentic pipeline, integrating a dynamic system prompt, LLM Suspense, and deterministic autofixers to improve code generation reliability significantly.
[P] LLM Jigsaw: Benchmarking Spatial Reasoning in VLMs - frontier models hit a wall at 5×5 puzzles
LLM performance declines sharply as puzzle complexity increases, with solve rates plummeting from 95% on 3×3 grids to nearly 0% on 5×5 grids, highlighting a significant challenge for frontier multimodal LLMs.
Warp Specialization in Triton: Design and Roadmap
Warp specialization in Triton enhances GPU kernel performance by creating tailored code paths for each warp, effectively minimizing control flow divergence and optimizing hardware utilization, which is crucial as kernel complexity increases.
Supercharging LLMs: Scalable RL with torchforge and Weaver
torchforge is a PyTorch-native RL library that simplifies large-scale reinforcement learning for LLMs, enabling seamless scaling from single-node to multi-node clusters without infrastructure complexity.