Open-Source AI
Open models — Llama, Mistral, DeepSeek, Qwen and others — are catching up to closed labs and let you run AI on your own hardware, vendor-free. That shifts the balance of power, the safety debate and the economics of the field. Here is everything on open-source AI: open-weight releases, licenses, local deployment and the race against closed models.
Cognition releases agent plugin to fix PHP and Node.js holes, including a CVSS 9.2 flaw
After the critical PHP2Shell flaws and a Node.js release fixing 10 CVEs, Cognition open-sourced a plugin that has Devin and other coding agents find and patch…
Echo Teaches VLA Robots to Recognize Success and Failure Inside Their Own Actions
Researchers embedded stop and failure signals in every action chunk of a VLA model and denoise them faster than the motions. No per-frame progress labels are needed.
PaperFormalizer Turns LaTeX Papers into Verified Lean Code, with the Author in the Loop
Stanford researchers released PaperFormalizer, an agentic pipeline for Claude Code or Codex that finds gaps in proofs and formalizes a paper in Lean.
DetectorScope Turns the Black Box of AI-Text Detectors into a Transparent One
Mistral Introduces Voxtral 3: 50-Language Recognition and Voice Agents for the Enterprise
MemT 1.0 builds memory into the model and aims at AI self-improvement
Xiaohongshu speeds up its sparse-attention indexer: DoSA halves TTFT
Dream Labs, from the NVIDIA DreamZero team, raises $90M and opens an API for robots
The DreamZero team, formerly at NVIDIA, has emerged from stealth with a $90M seed round and a public Dream API that controls a robot from a text command.
Stratified Sparse Attention speeds up decoding 2.96x at 1M context
The SSA sparse attention method combines deterministic top-k with importance-weighted sampling of the remainder. At 50x sparsity on 128K, it retains 85% of…
Qwen-Audio-3.1-ASR: 30 Languages, 16 Dialects, and Diarization in a Single Model
Of 1 598 RL Papers, 37 Made It Into Frameworks: An Audit of Seven Open-Source Stacks
Researchers teach LLMs to adapt to dialect: the CAP method and 10 model families
Agent4Sci: Claude Opus Beats Records on 20 Scientific Benchmarks by 10.4%
Omni-Woodpecker Cuts Hallucinations in Omni-LLMs Without Retraining: +12.9 on CMM
A Nanjing University team proposes OWP, a method that corrects an omni model's hidden states before decoding and adds 12.9 points on CMM for Qwen2.5-Omni-7B.
Researchers Explain How JEPA Selects Features: Relationships Matter, Not Lone Features
Mosh Levy, Aly Lidayan and Yoav Goldberg introduce "predictive closure": JEPA retains not individually predictable features, but sets of features that predict each…