Discovering new solutions to century-old problems in fluid dynamics
New AI methods have enabled the discovery of unstable singularities in fluid dynamics equations, potentially transforming our understanding of complex physical phenomena.
Nvidia buys $5B in Intel
Nvidia and Intel have announced a partnership to develop Intel x86 RTX SOCs, integrating Intel CPUs with Nvidia RTX graphics, aimed at enhancing the gaming PC market and custom data center CPUs for AI applications.
Learn Your Way: Reimagining Textbooks with Generative AI
Generative AI (GenAI) enhances textbooks by creating personalized, multimodal learning experiences, leading to a 9% improvement in immediate assessments and an 11% increase in retention compared to traditional methods.
[R] Is Chain-of-Thought Reasoning of LLMs a Mirage? A Data Distribution Lens
Chain-of-Thought (CoT) reasoning in LLMs may be a superficial phenomenon, as it often relies on learned patterns from training data rather than genuine inferential processes, suggesting a need for critical evaluation of its effectiveness.
Launch HN: Cactus (YC S25) – AI inference on smartphones
Cactus is a cross-platform framework enabling local deployment of LLM, VLM, and TTS models in Flutter and React-Native applications, supporting a wide range of models from Huggingface.
Llama-Factory: Unified, Efficient Fine-Tuning for 100 Open LLMs
LLaMA Factory enables zero-code fine-tuning of over 100 large language models through a user-friendly CLI and web UI, streamlining the process for developers and researchers alike.
[P] Open dataset: 40M GitHub repositories (2015 → mid-2025) — rich metadata for ML
A new open dataset of 40M GitHub repositories offers extensive metadata, surpassing existing public snapshots like BigQuery’s ~3M repositories, and includes a 1M-repo sample for rapid experimentation.
[P] We built mmore: an open-source multi-GPU/multi-node library for large-scale document parsing
mmore is an open-source library designed for multi-GPU/multi-node document parsing, achieving significant speed and accuracy improvements over existing tools like Docling, particularly in handling diverse formats such as PDFs, DOCX, and multimedia files.
Overcoming accuracy limitations of Analog In-Memory Computing hardware
Analog in-memory computing (AIMC) enhances neural network inference speed and power efficiency but faces challenges like noisy computations and input/output quantization constraints, limiting conventional LLM performance on AIMC hardware.
NVIDIA Blackwell: Born for Extreme-Scale AI Inference
NVIDIA's Blackwell architecture is engineered specifically for extreme-scale AI inference, promising enhanced performance and efficiency in processing large datasets.
[R] Uni-CoT: A Unified CoT Framework that Integrates Text+Image reasoning!
Fast and Fluent Diffusion Language Models via Convolutional Decoding and Rejective Fine-tuning
Convolutional decoding (Conv) and Rejecting Rule-based Fine-Tuning (R2FT) enhance diffusion language models by addressing the long decoding-window problem, allowing for faster and more fluent text generation without sacrificing bidirectionality.
Internalizing Self-Consistency in Language Models: Multi-Agent Consensus Alignment
Multi-Agent Consensus Alignment (MACA) enhances self-consistency in language models by utilizing a reinforcement learning framework that promotes reasoning aligned with internal consensus through multi-agent debates, leading to improved decision-making.
Enhancing Retrieval Augmentation via Adversarial Collaboration
The Adversarial Collaboration RAG (AC-RAG) framework enhances Retrieval-augmented Generation (RAG) by utilizing a generalist Detector and a domain-specialized Resolver to combat Retrieval Hallucinations effectively.
RationAnomaly: Log Anomaly Detection with Rationality via Chain-of-Thought and Reinforcement Learning
RationAnomaly introduces a novel framework for log anomaly detection that combines Chain-of-Thought (CoT) fine-tuning with reinforcement learning, enhancing both interpretability and accuracy in software systems.