Search: “Tensor G6”

GPT-6 Astra, Looped Transformers, and Hidden Reasoning

OpenAI released GPT-6 Astra, its strongest model to date, with standout 3D rendering and computer-use performance and 99.9% on ARC-AGI-3.

Sebastian Raschka reviews OpenAI's GPT-6 Astra, calling it the best model he has used, with disproportionate gains in 3D rendering, animation, and computer use through the Codex/ChatGPT harness. The model scores 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol and leads the Artificial Analysis Coding Agent Index, though gains on independent aggregate indices are more incremental. The article also explains looped transformer/recurrent depth architecture rumors, speculation that Astra hides its chain-of-thought reasoning, and recent research insights on the topic.

Hacker News · AI·updated Sep 12, 13:10 UTC · 3d ago·first Sep 9, 14:37 UTC · 6d agoModel release 10 sourcesHN 84↑ · 12 comments2

The latest AI news we announced in August 2026

Google's August 2026 AI recap includes launches of Gemini 3.7 Flash, Gemini 3.5 Transcribe, and the Pixel 11 series, plus 1 billion Gemini users.

Google's monthly recap covers the Gemini 3.7 Flash workhorse model for coding and agents, released three weeks after 3.6 Flash at half its per-million-token cost, and the Gemini app surpassing 1 billion monthly users. The Pixel 11 series launched with the Tensor G6 chip running Gemini Nano, alongside Gemini 3.5 Transcribe for real-time speech-to-text and Gemini Omni 1.1 Flash for studio-quality video generation. Other announcements include a free year of Google AI for college students, Gemma's 1 billion downloads, and AI weather forecasts for aviation contrail reduction.

Google · AI·Sep 1, 20:45 UTC · 14d agoAI industry

ZDI-26-592: NVIDIA TensorRT ONNX File Parsing Improper Validation of Array Index Remote Code Execution Vulnerability

NVIDIA TensorRT improper array index validation in ONNX parsing (CVE-2026-24238, CVSS 7.8) enables remote code execution.

ZDI-26-592 addresses improper validation of array index in NVIDIA TensorRT's ONNX file parsing, tracked as CVE-2026-24238 with CVSS 7.8. Exploitation permits remote code execution on affected installations and requires user interaction. The advisory was published by the Zero Day Initiative on August 24, 2026.

ZDI Published Advisories·Aug 24, 05:00 UTC · 23d agoAdvisoryCVE-2026-24238

ZDI-26-593: NVIDIA TensorRT ONNX File Parsing Heap-based Buffer Overflow Remote Code Execution Vulnerability

ZDI disclosed a second TensorRT heap-based buffer overflow RCE (CVE-2026-24268, CVSS 7.8) in ONNX file parsing, requiring user interaction.

The Zero Day Initiative published advisory ZDI-26-593 covering another heap-based buffer overflow in NVIDIA TensorRT's ONNX file parsing. A remote attacker can execute arbitrary code if the target opens a malicious file or visits a crafted page. ZDI rated the vulnerability CVSS 7.8 and assigned CVE-2026-24268.

ZDI Published Advisories·Aug 24, 05:00 UTC · 23d agoAdvisoryCVE-2026-24268

GPT-6 built this earth exploration site in 5 prompts

Hacker News post showcases an interactive earth exploration website that GPT-6 generated from only five prompts.

The item is a Hacker News submission (23 points, 32 comments) linking to earth.ethanplus.ai, an earth exploration website the author says was built by GPT-6 in five prompts. It functions as a community demo of the model's web development and generative capabilities rather than a research or security disclosure.

Hacker News · security·Sep 11, 21:39 UTC · 4d agoAI industryHN 23↑ · 32 comments

ZDI-26-591: NVIDIA TensorRT ONNX File Parsing Heap-based Buffer Overflow Remote Code Execution Vulnerability

ZDI disclosed a heap-based buffer overflow RCE (CVE-2026-24272, CVSS 7.8) in NVIDIA TensorRT ONNX parsing, requiring user interaction to exploit.

The Zero Day Initiative published advisory ZDI-26-591 covering a heap-based buffer overflow in NVIDIA TensorRT's ONNX file parsing. Successful exploitation allows remote code execution when a user opens a malicious ONNX file or visits a crafted page. ZDI rated the vulnerability CVSS 7.8 and assigned CVE-2026-24272.

ZDI Published Advisories·Aug 24, 05:00 UTC · 23d agoAdvisoryCVE-2026-24272

Unsolved Problem by Fields Medalist Breached by Two High School Students

Two high school students used Claude Opus 5 and GPT-5.6 Sol to help solve an open Lorentzian polynomials problem, posting a 75-page arXiv proof.

Aayush Bathija and Prince Rohatgi of Oak Park High School, mentored by UCLA postdoc Daniel Soskin, published the 75-page paper 'Bounded Ratios for Lorentzian Polynomials' (arXiv 2609.05341), solving an open problem in Fields Medalist June Huh's Lorentzian polynomial theory. The main structural theorem extends bounded coefficient-ratio characterization from quadratic to arbitrary-degree polynomials via discrete convexity conditions. The students used Claude Opus 5 and GPT-5.6 Sol for exploration and proof ideas but independently verified all arguments; the result follows an open letter from 25 Fields Medalists voicing concerns about AI's impact on mathematical rigor.

Hacker News · security·Sep 14, 12:23 UTC · 1d agoAI researchHN 22↑ · 19 comments

Benign Loss Landscapes Can Coexist with Worst-Case Hardness

Theory paper shows tree tensor networks contain worst-case hard targets yet benign loss landscapes, with difficulty arising from degenerate saddles.

The paper studies tree tensor networks (TTNs), which generalize deep linear networks and Tucker decompositions and embed arbitrary read-once Boolean formulas. It proves that every local minimum that is minimum-norm is global for every realizable target, so bad local minima do not distinguish typical from worst-case problems. Instead, learning difficulty arises from high-order degenerate saddle points caused by rank-deficiency, illustrated via a parity function case study, linking landscape geometry to computational hardness.

arXiv cs.AI / cs.LG / cs.CL·Sep 11, 16:57 UTC · 4d agoAI research

Graph Machine: Towards Better Pretraining via Edges

Researchers propose Graph Machine, an O(n)-state sparse architecture that replaces 75% of Qwen3-0.6B dense layers with only slight loss change.

The paper introduces the Graph Machine (GM), an architecture that maintains an O(n)-sized state accessed through sparse, dynamic routing via pointer-like edges updated differentiably by a referral mechanism resembling pointer chasing. The authors replaced 75% of dense Transformer layers in Qwen3-0.6B with GM sparse layers and pretrained from scratch on 15.7B tokens. Retrieving 2 of 4,096 tokens per KV head in each sparse layer degrades loss only slightly, while retrieving 4 marginally improves loss over the dense baseline.

Hugging Face daily papers·Sep 1, 20:00 UTC · 14d agoAI research

ISTA-DASLab/Qwen3.8-27B-GSQ-RCO-GGUF — new model trending #3 on Hugging Face

ISTA-DASLab releases GSQ-RCO non-uniform GGUF quantizations of Qwen3.8-27B down to 2.5 bpw, with task-lossless IQ3_S matching BF16 benchmark scores.

ISTA-DASLab released GGUF quantizations of Qwen3.8-27B produced with GSQ (Gumbel-Softmax Quantization) and RCO (Riemannian Constrained Optimization), non-uniform methods that allocate per-tensor precision via gradient-based search under a total size budget. Four checkpoints range from 2.50 bpw (8.4 GB) to 3.50 bpw (11.8 GB), plus a BF16 vision projector (mmproj) enabling multimodal use. The recommended IQ3_S build is task-lossless, matching the BF16 base exactly on AIME25 (100.00) and LiveCodeBench v6 (85.71) at roughly one fifth of the BF16 size. Optional -mtp variants add a Multi-Token Prediction head for speculative decoding in llama.cpp.

Hugging Face trending models·Aug 28, 18:38 UTC · 18d agoModel release1

Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics

Latent-MoE adds domain-aware mixture-of-experts routing to PINNs, improving accuracy over an order of magnitude on multi-regime physics PDEs.

The paper shows standard coordinate networks used in physics-informed neural networks have translation-variant NTKs causing long-range gradient conflicts on PDEs with spatially varying physics. MoE architectures with centered compact-support routers produce a uniformly banded NTK that localizes learning. Latent-MoE interleaves domain-aware MoE blocks in a shared backbone, outperforming FB-PINNs and X-PINNs by over an order of magnitude on multi-stage time-variable physics benchmarks.

arXiv cs.AI / cs.LG / cs.CL·Sep 7, 17:52 UTC · 8d agoAI research

Semigroup-JEPA: Latent Dynamics Consistency for Zero-Shot Physics Generalization

SG-JEPA world model conditions latent prediction on physical parameters, halving open-loop prediction error versus DINO-WM and boosting robotic control success.

SG-JEPA extends the LeWorldModel JEPA framework by supplying the governing physics parameter to the temporal model via action-conditioning and jointly training an encoder and predictor through autoregressive latent rollout. On out-of-distribution gravitational-field tasks it reduces open-loop prediction error by up to 2x versus DINO-WM on 2D datasets and increases 3D robotic control success rate up to 2.5x using independently trained diffusion policies. A linear feature analysis attributes most of the gain to the encoder learning features that the predictor can carry forward through rollout.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:08 UTC · 6d agoAI research

The Pelican comparison grid for Astra is pretty interesting

Simon Willison's pelican SVG comparison shows GPT-6 Astra producing markedly better images than GPT-5.6 Sol, Terra, and Luna across reasoning levels.

Willison generated pelicans-riding-bicycles SVGs with newly accessed GPT-6 Astra at low through max reasoning levels and rendered them in a comparison grid against GPT-5.6 Sol, Terra, and Luna. Astra's outputs were markedly more coherent, while even the best GPT-5.6-Sol images remained largely abstract shapes. Astra does not support a reasoning=none setting, so all comparisons involved reasoning-enabled runs.

Simon Willison·Sep 4, 23:59 UTC · 11d agoAI research

NCP-ArchPreview Technical Report: Moving towards Latent Space Language Models through Next Concept Prediction

An 8.9B-parameter latent-space language model using next-concept prediction matches OLMo-3-7B pretraining loss with only 51.3% of the training tokens.

NCP-ArchPreview augments next-token prediction with Next Concept Prediction over a product-quantized concept vocabulary built from hidden states, trained jointly end-to-end. The 8.9B model was trained on 5.73T tokens from the Dolma-3 dataset, the largest latent-space language model demonstration to date. It consumes 51.3% of the tokens to reach OLMo-3-7B's final pretraining loss and outperforms it by 2.45 points on the downstream macro-average, including a 5.99-point GSM8K gain. The learned latent space also enables lightweight domain adaptation via a 17M-parameter VQ module and improves speculative drafting accepted length by 4.17%.

Hugging Face daily papers·Sep 8, 20:00 UTC · 7d agoAI research1

Embedded Graph Flows for Categorical Graph Generation

Researchers propose Embedded Graph Flows, a generative model with learned categorical embeddings that beats DiGress and GruM on molecular graph benchmarks.

Embedded Graph Flows (EGF) learns continuous embeddings for node and unordered-edge categories and transports Gaussian noise toward these endpoints using a permutation-equivariant graph transformer. On QM9 it achieves the best result on all four reported metrics, with a Fréchet ChemNet Distance of 0.150 versus 0.717 for DiGress and 0.812 for GruM. On ZINC250k it retains the lowest NSPDK MMD, indicating close agreement with local substructures of reference molecules. Code is released on GitHub.

arXiv cs.AI / cs.LG / cs.CL·Sep 4, 16:26 UTC · 11d agoAI research1

GPT-6 Astra: A new generation of intelligence

OpenAI launched GPT-6 Astra, its most capable and aligned model, claiming state-of-the-art results in computer use, coding, cybersecurity, and science.

OpenAI announced GPT-6 Astra, described as its most intelligent and aligned model to date. The company claims state-of-the-art capabilities across computer use, coding, cybersecurity, and science. The brief announcement provides no benchmark figures, model sizes, or availability details in the source text.

OpenAI News·Sep 3, 11:00 UTC · 12d agoModel release

DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NEO-CODER-MAX-MTP-GGUF — new model trending #8 on Hugging Face

A new Qwen3.8-27B GGUF fine-tune claims ARC-C 735 at 8-bit with thinking tokens cut 2x-10x versus the base model.

Independent creator DavidAU released a GGUF fine-tune of Qwen3.8-27B built with Unsloth, claiming ARC-C of 735 at 8-bit and 719 at 4-bit, trending #8 on Hugging Face. The 'TURBO' variant cuts thinking tokens by one half to as much as one tenth while retaining output quality and detail. The repo ships both regular and MTP quants and claims gains over the base model across seven benchmarks, using 'Cold Fusion (GAIN + Unsloth)' and 'Fable Fusion 711' training methods.

Hugging Face trending models·Sep 1, 07:01 UTC · 15d agoModel release

Implementation of Machine Learning Workflows with NVIDIA cuML, RAPIDS, GPU Benchmarking, Explainability, Clustering, and Model Inference

Hands-on tutorial implements NVIDIA cuML and RAPIDS to GPU-accelerate scikit-learn-style ML workflows with benchmarking, clustering, and inference.

The tutorial demonstrates NVIDIA cuML as a GPU-accelerated machine learning framework, using cuml.accel to speed up unmodified scikit-learn scripts with zero code changes and the native cuML API for CuPy/cuDF interoperability. It benchmarks CPU versus GPU implementations of PCA, K-Means, nearest-neighbor search, logistic regression, random forests, and DBSCAN on datasets up to 200,000 samples with 64 features. It also builds GPU pipelines with UMAP, t-SNE, and HDBSCAN, validates GPU-generated SHAP explanations, uses the FIL library for forest inference, and covers model serialization and GPU/CPU portability.

MarkTechPost·Sep 13, 01:42 UTC · 3d agoAI tools & infra

Introducing GPT-6 Astra for developers

OpenAI introduces GPT-6 Astra for developers, with improved attention to detail, prompt understanding, and notably strong 3D model generation.

Simon Willison shares early hands-on impressions of OpenAI's GPT-6 Astra developer release, noting across-the-board improvements in attention to detail and prompt understanding. The model reportedly excels at building 3D models, producing renderings of gardens, shipyards, animals, cityscapes, and Dyson spheres. The write-up is third-party testing rather than an official announcement.

Simon Willison·Sep 5, 23:27 UTC · 10d agoModel release1

XHToken/Spark-X2.5-4B-GGUF — new model trending #30 on Hugging Face

XHToken released GGUF weights of Spark-X2.5-4B, a compact model with 1M-token context and 200+ language support, under Apache 2.0.

The Hugging Face repository provides BF16 GGUF conversions of Spark-X2.5-4B, a compact general-purpose language model for conversation, writing, translation, reasoning, coding, tool use, and agentic workflows. The model uses a hybrid attention architecture, supports a native context length up to 1M tokens, and covers more than 200 languages. Local inference is supported through Ollama and LM Studio via an XHToken llama.cpp fork, with a --think=false flag to disable thinking mode for faster responses. Released under Apache License 2.0; it was trending #30 on Hugging Face at publication.

Hugging Face trending models·Aug 28, 11:26 UTC · 18d agoModel release

GPT-6 Astra appears to show a "step change" in spatial reasoning based on early benchmarks

Early StationeryBench robotics results show OpenAI's GPT-6 Astra far ahead of Ai2's MolmoAct2 at dual-arm manipulation, completing 7 of 100 tasks versus zero.

A new robotics benchmark called StationeryBench tested OpenAI's GPT-6 Astra against Ai2's MolmoAct2 on five desk-object tasks using identical dual-arm YAM robots over 200 trials. Astra fully completed 7 of 100 tasks with a median progress score of 46 out of 100, while MolmoAct2 completed zero with a median score of 12. Cornell and Google DeepMind researcher Yoav Artzi called the result a 'step change in spatial reasoning' and noted Astra approaches human-level accuracy on the unpublished REMAP benchmark. He speculated OpenAI trained the model on large amounts of 3D data such as Blender scenes, and OpenAI reportedly plans consumer robots.

The Decoder·Sep 12, 14:26 UTC · 3d agoAI industry1

ZGCM-1: A Fully Open and Extremely Efficient Foundation Model for Math and Agentic Search

ZGCM-1 is a fully open 7B foundation model with 256K context that stays competitive with frontier models on math reasoning and agentic search.

ZGCM-1 is a fully open 7B dense foundation model trained from scratch using an efficiency-focused recipe: interleaved gated sliding-window and full attention, a stable FP8 Muon optimizer, and MDP-based mid-training with context scaling across 16K, 64K, and 256K. On mathematical reasoning and agentic search suites it remains competitive with much larger frontier models such as Qwen3-235B-A22B and GLM-5.1. The recipe yields a ~4.2x improvement in 16K pre-training time-to-loss, and all weights, checkpoints, training code, data recipes, and W&B logs are open-sourced.

Hugging Face daily papers·Sep 10, 20:00 UTC · 5d agoModel release

Inside NVIDIA’s cuDNN Graph API: Fusion, Autotuning, and Plan Reuse with cuDNN Frontend

MarkTechPost tutorial walks through NVIDIA's cuDNN Frontend graph API, covering kernel fusion, autotuning, plan reuse, and CUDA graph capture on Colab GPUs.

The tutorial explains how to express GPU computations as operation graphs via the cuDNN Frontend graph API, running the five-step build pipeline of validate, build operation graph, create execution plans, check support, and build plans. It progresses from a single fused convolution with bias and ReLU to autotuning across engine configs, FP8-style epilogues, attention, plan serialization, dynamic shapes, and CUDA graph capture. Each kernel is benchmarked against a PyTorch reference on a single Colab GPU to verify correctness and measure cost. The piece also covers practical setup issues like making libcudnn.so visible to the frontend's dynamic loader.

MarkTechPost·Sep 15, 21:37 UTC · 13h agoAI tools & infra

Safety overview: GPT-6 Astra

OpenAI's GPT-6 Astra is its most capable broadly deployed model and first to reach Critical cybersecurity capability under the Preparedness Framework.

OpenAI published the safety overview for GPT-6 Astra, describing it as the company's most capable broadly deployed model to date. Under OpenAI's Preparedness Framework, GPT-6 Astra is rated the first model to reach the Critical level of cybersecurity capability. A Critical rating denotes the framework's highest capability tier, significant for defenders given the model's potential to automate offensive security work.

OpenAI News·Sep 3, 00:00 UTC · 13d agoModel release

Model ML completes finance work more efficiently with GPT-5.6 Sol

OpenAI customer Model ML uses GPT-5.6 Sol to turn finance research into editable, traceable decks and workbooks.

OpenAI published a customer story describing how Model ML uses GPT-5.6 Sol for finance work. The model carries tasks from research and analysis through to editable, traceable PowerPoint decks and Excel workbooks. This is a product adoption case rather than a new model release.

OpenAI News·Aug 10, 12:00 UTC · Aug 10, 2026AI industry

Advancing price-performance for developers with GPT‑5.6 in Kiro

OpenAI's GPT-5.6 is now available in the Kiro developer platform, offering developers improved price-performance for planning, building, reviewing, and testing software.

OpenAI announced that GPT-5.6 is now available in Kiro, a developer platform for planning, building, reviewing, and testing software. The announcement emphasizes better price-performance for developer workloads rather than new model capabilities. This expands the commercial availability of OpenAI models in third-party coding tools.

OpenAI News·Aug 24, 12:00 UTC · 22d agoAI industry

GPT-5.6-Cyber refuses security researchers’ requests far less often

Help Net Security·Aug 11, 00:00 UTC · Aug 11, 2026Exploit / PoCCVE-2026-15903

Learning with Covariance Matrices: Principal Component Analysis Meets Learning with Graphs

Theoretical tutorial establishes equivalence between covariance neural networks and PCA, with stability and transferability bounds and brain-age applications.

The paper reviews the theory of coVariance neural networks (VNNs), graph neural networks that operate on covariance matrices as graphs. It derives a conceptual equivalence between VNNs and PCA-based information processing, refined stability bounds under finite-sample covariance perturbations, and transferability characterizations across multiscale datasets. Demonstrated applications include brain age gap estimation for neurodegenerative conditions from neuroimaging data.

arXiv cs.AI / cs.LG / cs.CL·Sep 9, 17:28 UTC · 6d agoAI research1

llm 0.35

llm CLI tool version 0.35 adds support for OpenAI's new GPT-6 Astra model exposed as gpt-6-astra.

Simon Willison released llm 0.35, which adds an OpenAI model definition for GPT-6 Astra under the model ID gpt-6-astra. No other release details were provided in the post.

Simon Willison·Sep 7, 23:54 UTC · 8d agoAI tools & infra

OpenAI’s GPT-5.6 Sol runs up to 14× faster with Ultrafast mode

OpenAI launched GPT-5.6 Sol Ultrafast mode in limited preview, running up to 14x faster at 750 tokens per second via Cerebras inference.

OpenAI's GPT-5.6 Sol Ultrafast mode is available in limited preview through the OpenAI API, delivering up to 14x faster processing and up to 750 output tokens per second, powered by Cerebras under the companies' ultra-low-latency inference partnership. Preview customers are testing it in production for coding, commerce, financial research, and support applications. OpenAI is also using Ultrafast internally for incident response tasks such as log analysis and trace review, and for research workflows with multiple same-day experiment iterations.

Help Net Security·Aug 14, 00:00 UTC · Aug 14, 2026AI industry