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Sep 7

Sep 7Mon
  1. OpenBMB (MiniCPM) · new models on Hugging FaceAI score45

    openbmb/JustRL-II-base-model: RL starting checkpoint for long-CoT math reasoning

    AIOpenBMB released JustRL-II-base-model, the pre-RL starting checkpoint for the JustRL II math-reasoning case study, scoring about 61% on AIME 2025 before reinforcement learning. The full JustRL II recipe reaches 81% on AIME 2025 in about 300 RL steps from this checkpoint, versus about 74% for a standard GRPO baseline. The Llama-architecture weights are available on Hugging Face and are intended for reproducing the recipe and research on long-CoT RL, not general assistant use.

Sep 6

Sep 6Sun
  1. OpenBMB (MiniCPM) · new models on Hugging FaceAI score32

    MiniCPM5-2B-DSpark draft model released for speculative decoding with MiniCPM5-2B

    AIOpenBMB released MiniCPM5-2B-DSpark, a 323,776,001-parameter DSpark draft checkpoint with five layers that proposes seven draft tokens per forward pass for the MiniCPM5-2B target model. The model, trained on 7,054,154,509 tokens with an average acceptance length of 5.5174 at T=0 and 4.0514 at T=1.0, is served through SGLang with DSPARK speculative decoding. It is released in BF16 under the Apache-2.0 License.

  2. OpenBMB (MiniCPM) · new models on Hugging FaceAI score62

    OpenBMB releases MiniCPM5-2B, a 2B open-source model with open training data

    AIOpenBMB has released MiniCPM5-2B, a dense 2B Transformer built for on-device and resource-constrained deployment, with an average score of 53.9 in its comparison set. The release also opens the UltraData datasets behind it, including UltraX, UltraData-Code, UltraData-SFT-Agent-2609 and UltraData-RL-2609, and includes GGUF, MLX, GPTQ and DSpark variants for common runtimes.

    Why it matters: The release pairs a 2B model with open training datasets and reports per-benchmark comparisons against named same-size and larger models, letting readers check the claims directly.

Sep 4

Sep 4Fri
  1. BAAI · new models on Hugging FaceAI score26

    ConsiSpace: BAAI and Peking University release geometry-consistent video spatial reasoning model

    AIBAAI and Peking University researchers released official weights for ConsiSpace, a geometry-consistent multimodal framework for spatial reasoning in long-form visual observations. The model is described in the paper "ConsiSpace: Learning Geometric Consistency Matters for Video Spatial Reasoning" (arXiv:2607.17599).

  2. Tencent · new models on Hugging FaceAI score36

    Tencent Releases EVIE-8B Open-Source Visual Document Retrieval Model

    AITencent has open-sourced EVIE-8B, an 8.4B-parameter visual document retriever that scores 66.75 nDCG@10 on ViDoRe V3 and ranks first on that leaderboard's mean task score of 66.24. The model uses 4096D per-token multi-vector embeddings with MaxSim late-interaction scoring and bidirectional attention, and it serves as the teacher for the lightweight EVIE-4.5B model. Model weights, inference pipelines, and evaluation suites are available, while the formal research paper is promised for a future release.

  3. Tencent · new models on Hugging FaceAI score36

    Tencent Open-Sources EVIE-4.5B Visual Document Retrieval Model With Elastic Embeddings

    AITencent released EVIE-4.5B, a 4.5B-parameter visual document retrieval model, with weights, training pipelines, HAC token compression, and evaluation suites open-sourced on Hugging Face. It scores 66.02 on ViDoRe V3 and ranks second on that leaderboard behind the 8.4B EVIE-8B, which scores 66.24. Its Prefix-MRL head lets a single 2048D projection be truncated to 64–2048 dimensions at runtime without separate models.

Sep 3

Sep 3Thu
  1. Mark ChenAI score80

    Mark Chen announces GPT-6 Astra with computer use and agent oversight

    AIOpenAI researcher Mark Chen announced GPT-6 Astra, which he described as the company's most capable and aligned model yet. He said it can build and test software, work across apps on a computer, and help with open scientific problems. The post also highlights improved computer use compared with Operator and stronger monitoring that can stop potentially unauthorized agent actions.

    Why it matters: The post links a named model release to specific capabilities like computer use and aligned agent behavior, giving readers concrete claims to check against the model.

  2. Google DeepMind · The KeywordAI score72

    Google DeepMind releases WeatherNext 3, a global weather model with hourly satellite-based forecasts

    AIGoogle DeepMind and Google Research introduced WeatherNext 3, which generates hourly global forecasts at up to 5-kilometer resolution using live geostationary satellite data. The company reports that precipitation forecasts improved by up to 60% against IMERG in medium-range evaluations, and that longer-range precipitation forecasts are up to 50% more accurate. The model is now available across Search, Gemini, Google Maps, Google Maps Platform Weather API, Google Earth Engine, BigQuery, and Google Cloud Storage.

    Why it matters: The post explains how training on live satellite data and station observations changes resolution and update frequency, with precipitation accuracy gains reported against named baselines.

  3. Google DeepMind · YouTubeAI score72

    Google DeepMind's WeatherNext 3 offers hourly, 5km-resolution weather forecasts

    AIGoogle DeepMind introduced WeatherNext 3, a weather forecasting model that learns directly from satellite feeds and ground-level weather station data. It produces a fresh forecast every hour, compared with the six-hour refresh typical of traditional models, with native 5km resolution for temperature and humidity. It is available through Google Search, Gemini, Google Maps and more.

    Why it matters: The source shows a shift from six-hourly to hourly refresh and 5km local resolution, which matters for energy planning and local forecasting.

  4. BAAI · new models on Hugging FaceAI score25

    BAAI Releases Recon2Reason-Reasoning-4B, a Spatial Reasoning Vision-Language Model

    AIBAAI released Recon2Reason-Reasoning-4B, a 4,437,815,808-parameter vision-language model fine-tuned from Qwen3-VL-4B-Instruct for indoor spatial reasoning. The model handles metric distance, relative position, and object-relation questions from single or multiple images, and loads with the standard Qwen3VLForConditionalGeneration interface without trust_remote_code. The checkpoint is released under Apache-2.0 with BF16 Safetensors weights, and the retrieval-augmented scene-reconstruction extension ships separately.

Sep 2

Sep 2Wed
  1. NVIDIA · new models on Hugging FaceAI score36

    NVIDIA Releases EgoHand-1.0 Model for Single-Image 3D Hand Pose Estimation

    AINVIDIA released EgoHand-1.0, a 883.5M-parameter DINOv3-based transformer that predicts SOMA hand pose, MHR shape coefficients, and camera translation from a single 256×256 hand crop. The model is evaluated on the HOT3D egocentric benchmark and is intended for research and demonstration rather than production use. Its outputs can supply hand trajectories for training robotic manipulation policies, and it runs on NVIDIA Ampere GPUs under Linux with PyTorch.

  2. NVIDIA · new models on Hugging FaceAI score67

    NVIDIA releases Nemotron-3-Labs-Ultra-Math-RL for mathematical proof reasoning

    AINVIDIA has published Nemotron-3-Labs-Ultra-Math-RL on Hugging Face, a 550B total, 55B active parameter model for solving difficult math problems and identifying proof mistakes. The model is part of an ensemble that reached gold-medal level at the International Mathematical Olympiad 2026, and it is available for commercial and non-commercial use under the OpenMDW-1.1 license. Deployment is designed for NVIDIA Blackwell or Hopper GPUs, with a recommended minimum of 8× B200 on a single node and a context length of up to 1M tokens.

    Why it matters: The release details the model's math-proof role, its 550B total and 55B active parameters, and its vLLM deployment requirements for teams weighing adoption.

  3. Google AI StudioAI score78

    Google releases Gemini 3.8 Flash and restricted 3.8 Flash Cyber model

    AIGoogle introduces Gemini 3.8 Flash for coding, agentic tasks, and multi-step reasoning, priced at $0.75 per million input tokens and $3.75 per million output tokens during the introductory period. Gemini 3.8 Flash Cyber targets vulnerability detection and automated patching and is available only to trusted defenders through the new Fairwind Program. The introductory price expires December 31, 2026, after which $1.50 and $7.50 per million tokens apply.

    Why it matters: The post separates a general coding and agent model from a restricted cyber variant, showing how one shared core is deployed under different access and safety tiers.

  4. Sundar PichaiAI score62

    Google introduces Gemini 3.8 Flash Cyber, a cybersecurity model for vulnerability work

    AIGoogle introduces Gemini 3.8 Flash Cyber, which it describes as its most capable cybersecurity model. The company reports 86.2% on CyberGym, 47.2% on CWE-Bench for patching, and a 70%+ success rate in discovering vulnerabilities across 20 programming languages on its internal benchmark. Google says the model offers frontier-level performance at Flash-level speed and pricing.

    Image from @sundarpichai's post
  5. Varun MohanAI score57

    Gemini 3.8 Flash released with gains in agentic coding and knowledge work

    AIGoogle's Gemini 3.8 Flash is out, and Varun Mohan says it substantially improves on 3.7 Flash for agentic coding and general knowledge work. It is now available to everyone on Antigravity. The attached benchmark table lists Gemini 3.8 Flash at $0.75 per 1M input tokens and $3.75 per 1M output tokens, with introductory pricing through December 31, 2026.

    Image from @_mohansolo's post
  6. koray kavukcuogluAI score62

    Gemini 3.8 Flash claims stronger engineering results at lower cost than larger models

    AIGoogle's Koray Kavukcuoglu says Gemini 3.8 Flash is a major step up from Gemini 3.7 Flash and outperforms most larger frontier models on complex engineering problems at a fraction of the cost. The attached DeepSWE V1.1 chart, sourced to Datacurve AI, plots average cost per task against score for Gemini 3.8 Flash and other models. A link to Google's blog post with more details is included.

    Image from @koraykv's post
  7. Logan KilpatrickAI score62

    Google releases Gemini 3.8 Flash with gains in agentic and coding tasks

    AIGoogle announced Gemini 3.8 Flash, its third updated Flash model in six weeks, citing improvements in agentic and coding capabilities. The benchmark table lists input at $0.75 and output at $3.75 per 1M tokens, with introductory pricing of $1.50 and $7.50 expiring December 31, 2026. Terminal-bench 2.1 shows 89.4% for Gemini 3.8 Flash against 85.8% for Gemini 3.7 Flash.

    Image from @OfficialLoganK's post
  8. Google AI StudioAI score62

    Google releases Gemini 3.8 Flash with improved coding, agent, and reasoning

    AIGoogle AI Studio announced Gemini 3.8 Flash, which it calls its most intelligent workhorse model. The company says it brings significant improvements over 3.7 Flash in software engineering, agentic tasks, and multi-step reasoning in specialized domains. It is available at the same introductory price as 3.7 Flash, $0.75 per million input tokens and $3.75 per million output tokens, through the Gemini API and AI Studio.

    Image from @GoogleAIStudio's post
  9. Cohere · new models on Hugging FaceAI score44

    Cohere Releases Tiny Aya En-Thinker, a 3.35B Multilingual Reasoning Model

    AICohere Labs released Tiny Aya En-Thinker, an open-weights 3.35 billion parameter multilingual reasoning model with a 32K context length. It is trained on English reasoning traces for 44 languages plus English, with coverage extending to 20+ more languages through non-reasoning instruction data. The model is available under a CC-BY-NC license that also requires adherence to Cohere Labs' Acceptable Use Policy.

  10. Cohere · new models on Hugging FaceAI score44

    Cohere Releases Tiny Aya L2-Thinker Multilingual Reasoning Model on Hugging Face

    AICohere Labs released Tiny Aya L2-Thinker, an open-weights 3.35 billion parameter multilingual reasoning model that thinks in the same language as the user's prompt before answering. The model supports in-language reasoning for 44 languages plus English, with coverage extended to 20+ more languages through additional non-reasoning instruction data, and has a 32K context length. It is licensed under CC-BY-NC and is available on Hugging Face.

Sep 1

Sep 1Tue
  1. Anthropic · YouTubeAI score78

    Anthropic releases Claude Fable 5.1, an upgrade to its most capable model class

    AIAnthropic has released Claude Fable 5.1, the latest upgrade to its most capable class of models, and it is available everywhere today. The company says it handles complex, long-running, multi-step work and avoids shortcuts when fixing root causes of software issues. At lower effort levels, Fable 5.1 can match or beat Fable 5 at a much lower cost, according to Anthropic's benchmarks.

    Why it matters: The source names the upgraded model class and its cost tradeoff at lower effort levels, which helps readers weigh it against the earlier version for their own workloads.

  2. Anthropic · YouTubeAI score72

    Anthropic releases Claude Fable 5.1 for complex, long-running tasks

    AIAnthropic has released Claude Fable 5.1, an upgrade to its most capable model class, and says it is available everywhere today. The company reports that at lower effort levels, Fable 5.1 can match or beat Fable 5 at a much lower cost. It is described as strong at complex multi-step work, such as long proofs and contracts with hundreds of cross-references, and at fixing root causes in software issues.

    Why it matters: The source reports cost and effort-level tradeoffs for long-running tasks, helping readers judge whether the upgrade changes their workloads or budgets.

  3. Ai2 · new models on Hugging FaceAI score22

    Ai2 Releases Supplemental ACE2S-SHiELD+ Ablation Checkpoints on Hugging Face

    AIAi2 has published supplemental checkpoints for its ACE2S-SHiELD+ climate model on Hugging Face, covering four ablation configurations that test random CO2 data and energy conservation. Each configuration includes two random-seed models, and the repository recommends the main ACE2S-SHiELD+ checkpoint for most uses. The checkpoints are licensed under Apache 2.0 for research and educational use.