Lossless LLM compression for efficient GPU inference via dynamic-length float
DFloat11 achieves a 30% reduction in LLM size while ensuring outputs are bit-for-bit identical to the original, utilizing entropy coding for optimal compression.
New LLM jailbreak bypasses all major FMs
HiddenLayer introduces a novel universal bypass that enhances security for all major LLMs, effectively protecting against inference, bypass, extraction attacks, and model theft without complicating existing models or requiring access to raw data.
Show HN: Lemon Slice Live – Have a video call with a transformer model
Lemon Slice has developed a custom diffusion transformer (DiT) model that enables real-time video streaming at 25fps, allowing users to create talking avatars from a single image without prior training or rigging.
Paper2Code: Automating Code Generation from Scientific Papers
PaperCoder automates the transformation of machine learning papers into functional code repositories through a structured three-stage process: planning, analysis, and generation, utilizing specialized agents for collaboration.
[R][P] We compress any BF16 model to ~70% size during inference, while keeping the output LOSSLESS so that you can fit in more context or run larger models.
DF11 compresses BF16 models to ~70% size during inference while maintaining lossless output, allowing for larger context or model sizes without sacrificing accuracy, as detailed in the arXiv paper.
PyGraph: Robust Compiler Support for CUDA Graphs in PyTorch
PyGraph enhances CUDA Graphs in PyTorch2 by automating their deployment, addressing challenges like static graph structures and performance overhead from data copying, which can paradoxically degrade performance in some scenarios.
[R] Cross-Encoder Rediscovers a Semantic Variant of BM25
BERT-based cross-encoders not only outperform BM25 but may also reimplement it semantically, revealing how MiniLM learns components akin to BM25 through mechanistic interpretability.
[R][P] Byte-level LLaMA and Gemma via cross-tokenizer distillation (with open-source toolkit)
ALM enables cross-tokenizer distillation, allowing the transfer of knowledge between models with different tokenization methods, exemplified by the successful distillation of Gemma and LLaMA into byte-level models.
[R] Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
PaperCoder automates the transformation of machine learning papers into functional code repositories, utilizing a multi-agent LLM framework that enhances reproducibility and accelerates research progress.
[D] A Bourgain-Embedding approach for abstract-board games?
The project introduces a novel AI approach for the strategy game TRIUM, utilizing a Bourgain-Embedding method that transforms board states into unique strings, enabling effective distance calculations for game moves.
FRAG: Frame Selection Augmented Generation for Long Video and Long Document Understanding
FRAG introduces a novel approach to long input processing by selecting relevant frames before generating outputs, enhancing efficiency without the need for extensive model fine-tuning.
[Discussion] Is the future of coding agents self-learning LLMs using KGs to shape their reward functions?
Current coding agents like Copilot lack the ability to learn from specific codebases, functioning instead as basic context-fetchers; however, integrating Reinforcement Learning (RL) with Knowledge Graphs (KGs) could enable them to adapt and improve based on project-specific standards and feedback.
[D]Beyond Jailbreaks: Sentrie Protocol Shows Deeper Gemini 2.5 Control
The Sentrie Protocol enhances control over Gemini 2.5, revealing capabilities that extend beyond traditional jailbreaks by allowing for more nuanced interactions with AI systems.
NVIDIA Research at ICLR — Pioneering the Next Wave of Multimodal Generative AI
NVIDIA's ICLR presentation features over 70 papers that advance multimodal generative AI, addressing real-world applications in healthcare, robotics, and autonomous vehicles.
[Discussion] Continual learning for Retrieval augmented generation?
A continual learning (CL) RAG system aims to deliver the most current information while accommodating specific temporal contexts, highlighting the need for adaptability in information retrieval.