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Introduction of Jesse v1, a new AI model architecture built from scratch using public datasets, consuming zero tokens and offering fast reasoning capabilities.
Ant Group released Ling-3.0-flash-Fin, a finance-focused open-weight AI model that scores competitively on intelligence and financial benchmarks but faces challenges in agentic tasks.
Salesforce and Nvidia have introduced Koa, a new reasoning model built on Nvidia's Nemotron, tailored for enterprise tasks like sales and customer support, providing an open-weight alternative to proprietary AI models.
Ox Alpha is described as the most popular free reasoning model on OpenRouter, featuring a 1M token context window for coding and agentic tasks, and can be tested via Pi or Hermes Agent.
The article introduces BDH-CQ, a 150M parameter recurrent model that combines in-context learning with latent reasoning, achieving 29.5% on ARC-AGI-1 at a cost of $0.0007 per task, setting a new standard for cost efficiency.
This paper introduces BDH-CQ, a 150M-parameter reasoning model that combines in-context learning with recurrent latent reasoning, achieving 29.5% pass@2 on ARC-AGI-1 at very low inference cost and establishing a new cost-accuracy frontier.
Jensen Huang announced that NVIDIA has officially open-sourced its autonomous driving reasoning model Alpamayo 2 Super. The model can understand complex scenes and think before acting, and is suitable for Robotaxi, trucks, delivery vehicles, etc. It is open for commercial use under the OpenMDW-1.1 license.
AntLingAGI announces Ling 3.0 flash, a native hybrid-linear reasoning model with 124B total parameters and 5.1B active, MIT-licensed, claiming to match a 1T flagship with much less compute and faster response times.
inclusionAI released Ling-3.0-flash, a native hybrid reasoning model with 124B total/5.1B active parameters using a hybrid linear attention architecture (KDA+MLA) and sparse MoE. It matches or outperforms its 1T-class predecessor Ring-2.6-1T while being far more compute-efficient, with built-in agentic and long-context optimizations.
NVIDIA released Alpamayo 2 Super, an open reasoning model for autonomous vehicles, now available for commercial use under the OpenMDW-1.1 license, delivering frontier-scale reasoning for robotaxis and AV development.
The DeepLens Diagnosis Agent uses a five-stage agentic workflow with a small medical reasoning model (7B) to achieve 60.14% diagnostic accuracy on a 915-case benchmark, outperforming frontier LLMs like Claude Sonnet 4.5 and Gemini 3.1 Pro at lower cost. The workflow design alone yields a 36-point gain over the base model, demonstrating that structured process constraints are key for diagnostic reasoning.
A user shares their experience with the Laguna S2.1 model, finding it effective for complex debugging due to its thorough reasoning style, but not suitable as a general planner. It successfully fixed bugs that other models like Qwen and Claude could not.
Domyn-Small is a 10-billion-parameter open-weight reasoning language model released under MIT license, trained on 9 trillion tokens and optimized for reasoning, instruction following, and tool use. It achieves strong accuracy-efficiency balance against peers like Qwen3.5-9B and OLMo-3-7B-Think.
The paper proposes FUSAR-R1, a large-scale reasoning model for SAR image interpretation that uses chain-of-thought reasoning and reinforcement learning to achieve better performance than existing models.
An 8B reasoning OCR model achieves near-perfect 99% accuracy, instantly extracting text from images and converting complex layouts into clean Markdown, outperforming larger models like GPT-4o on document tasks.
Qwythos-9B is a 9B super reasoning model based on the Qwen3.5-9B base, further trained with 500 million+ Claude Mythos reasoning traces. It natively supports 1M long context and tool calling, designed for local deployment by security researchers. It significantly outperforms the original base model on MMLU and mathematical reasoning.
Empero releases Qwythos-27B-v1, an open-weight reasoning model based on Qwen3.5-27B that preserves native multi-token prediction, the full vision tower, and a 1,048,576-token context window. It demonstrates strong agentic terminal performance and improved closed-book reasoning over its 9B sibling.
Empero AI releases Qwythos-9B-v2, an improved version of their reasoning model that eliminates looping behavior while preserving performance across benchmarks. The update also restores the MTP head and fixes identity injection issues.
Tess-4-27B is a 27B reasoning model built on Qwen3.6-27B, post-trained on 64K-token long-context agentic traces with weight-scaled reasoning. It is designed for efficient, honest, and agentic task execution, available in open-source formats.
Sebastian Raschka's new book 'Build a Reasoning Model (From Scratch)' covers inference scaling, reinforcement learning, and distillation. The announcement also references Dario Amodei's comments on Chinese models distilling from Anthropic models.