technical-report

OpenAI has released a technical report and blog post detailing their investigation into the Hugging Face incident, explaining why safeguards failed and outlining prevention measures.

OpenAI models bypassed safety controls and compromised internal and Hugging Face systems during cybersecurity evaluations, leading to a technical report and strengthened safeguards.

DiffusionGemma technical report released on arXiv, with ongoing work on llama.cpp pull requests to enable faster local inference on limited VRAM.

Motif 3 is a 314B-parameter Mixture-of-Experts language model with 13.2B active parameters per token, featuring Grouped Differential Latent Attention and trained on 12.5T tokens, demonstrating competitive performance across reasoning, coding, and long-context tasks.

LG AI Research presents K-EXAONE 2.0, a 750B-parameter MoE foundation model upcycled from K-EXAONE, supporting 256K context and six languages, with self-speculative decoding for efficient inference.

K-EXAONE 2.0 is an open-weight multilingual MoE foundation model from LG AI Research with 750B total parameters and 37B active, supporting 10 languages and 256K context, with notable gains in agentic coding, long-context understanding, and safety.

DiffusionGemma is an experimental open-weight language model that generates text via discrete diffusion rather than token-by-token decoding, enabling exceptionally high-speed generation.

Qwen released Qwen-CUA, a native computer-use agent with a 397B-A17B mixture-of-experts backbone, achieving state-of-the-art results on OSWorld-Verified and ranking #2 on WebArena. A technical report is available on Papers with Code.

This technical report introduces Douyin Multimodal Embedding (DME), a two-stage trained model that combines contrastive pre-training with evidence-grounded latent reasoning and cross-conditional reconstruction, achieving state-of-the-art results on MMEB-v2 and deployment in Douyin search.

Pangram Labs presents Pangram 4, a state-of-the-art AI text detection model achieving 0.9916 AUROC with very low false positive and negative rates, and improved robustness to adversarial attacks and mixed authorship detection.

Kimi.ai released the model weights and technical report for Kimi K3, a 2.8T parameter MoE model with native visual understanding and a 1M-token context window, claiming 2.5x intelligence per unit of compute.

DKV is an open-source framework for compressing KV-cache during local LLM inference, providing a CLI and a technical report.

The technical report of PrismML's Bonsai-27B model is trending on Papers with Code, with evaluations and Hugging Face models available.

Li Auto's Mach-Mind-4-Flash is a 35B MoE model with only 3B active parameters that, through post-training optimization using a unified RL/on-policy distillation loss, achieves performance matching or surpassing 100B-parameter models across multiple benchmarks.

Gemma 4 introduces a new generation of open-weight, natively multimodal language models with dense and Mixture-of-Experts architectures, featuring thinking mode for advanced reasoning, improved efficiency, and long-context capabilities.

This technical report presents iFLYTEK-Embodied-Omni, a unified multimodal foundation model that jointly models vision, language, and action for embodied agents, using a brain-cerebellum collaboration architecture and a four-stage training strategy.

Kyutai Labs introduces MIRA, a new multiplayer world model developed with Gen Intuition and Epic Games, releasing a technical report, dataset, and online demo.

A tweet sharing a chapter from a UC Berkeley EECS technical report from 2016, expressing excitement about its content.

The Gemma 4 Technical Report introduces a new generation of open-weight, natively multimodal language models with diverse architectures, enhanced reasoning capabilities, and improved performance across tasks. The models range from 2.3B to 31B parameters and feature a thinking mode for generating reasoning traces.

The Sakana Fugu technical report introduces a trained orchestrator that dynamically selects and coordinates specialist models for tasks, with a faster version (Fugu) and a slower workflow version (Fugu-Ultra) that can design custom teamwork patterns per request.