HuggingFace

Flash-dLLM is a training-free inference acceleration framework for diffusion LLMs that uses IO-aware KV caching and parallel decoding to achieve significant speedups and memory efficiency improvements.

This paper introduces AIDE^2, a system that enables AI research agents to autonomously improve their own code through recursive self-improvement, leading to performance gains across various AI research tasks.

UK AISI and EvalEval are collaborating to openly share AI evaluation results using a standardized schema and platform, enhancing reproducibility and transparency in benchmarking for AI models.

Hugging Face's transformers library now supports GGUF models from llama.cpp, enabling efficient local inference on consumer hardware through familiar APIs.

Jun Kim, creator of oMLX, joins Hugging Face to support the MLX community, enhancing stability and development for local AI on Apple Silicon.

The paper presents a physics-inspired approach to pruning LLM blocks by modeling block removal as a constrained binary optimization problem mapped to an Ising glass, achieving significant compression gains without benchmarking each configuration.

RULER introduces instance-aware rubric rewards for SVG generation, using a vision-language judge to optimize reinforcement learning and significantly improve performance over previous methods.

The paper introduces Ovis-Embedding, a state-of-the-art omni-modal embedding model that uses a shared backbone to encode text, image, video, and audio in a common representation space, achieving top performance on benchmarks like MMEB-v3 and MVEB.

This survey paper reviews the application of large language models in mental health, tracing their evolution from basic pattern recognition tools to sophisticated personalized companions.

The paper introduces GAE, a geometry-native autoencoder that creates a compact latent space for generating 3D-consistent scenes, enhancing visual quality and coherence over existing methods.

HyperQ introduces token-conditioned quantum residual branches into frozen masked-diffusion language models, using a circuit hypernetwork to dynamically generate quantum circuit parameters. This approach improves performance on benchmarks and is computationally efficient, requiring fewer fine-tuning examples than classical baselines.

This paper introduces Segment-Snap, a method that combines geometric and semantic cues to improve interaction understanding in 3D scenes, achieving significant gains in motion-gated AP and handle detection metrics.

The paper presents ACLArena, a framework for evaluating Agent Continual Learning in multi-stage post-training, analyzing forgetting and generalization mechanisms, and proposing an improved ACL recipe using offline replay and LoRA experts.

This paper introduces Complex KDA, an enhanced version of Kimi Delta Attention that combines a delta-rule transformation with a reflection to achieve greater expressivity, outperforming Transformers in some tasks while maintaining efficiency, with open-source code and models available.

EdgeGen is a synthetic task generation framework that creates database-grounded edge-case tasks to improve tool-calling agents through fine-tuning and harness optimization, demonstrating consistent performance improvements.

This paper introduces SVEET, a framework for high-quality streaming video editing that leverages a pretrained video diffusion model to enable auto-regressive editing with real-time performance on a single GPU.

The paper introduces THAW-VLA, a method that distills world-model representations into Vision-Language-Action models for robotics, enhancing robustness and performance on simulation and real hardware without additional inference overhead.

The paper presents VideoGen-Agent, a reinforcement learning-based multimodal agent that coordinates tools for video generation, significantly improving performance on the new VABench benchmark.

Jev-Mem introduces an agentic memory architecture inspired by System-One/System-Two cognition, enhancing efficiency and effectiveness for long-horizon AI agents with improved scores and faster operations.

The paper introduces GameHorizon Suite, a unified data and evaluation framework for assessing AI models' capabilities in gameplay across multiple temporal horizons, featuring an annotation pipeline, large-scale dataset, and reproducible benchmark.