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OpenAI Releases GPT-6 Astra, a Coding Model for Legacy Languages Such as COBOL

OpenAI has released GPT-6 Astra, a coding model for legacy programming languages such as COBOL, Fortran, and Visual Basic. OpenAI built two evaluations for this purpose, MultiLangBench and COBOL migration. GPT-6 Astra scored 87.4% on the 20 languages in MultiLangBench, compared with 26.8% for GPT-5.6 Sol; it scored 86.8% on COBOL migration, or 97.9% at high reasoning effort, while GPT-5.6 Sol scored only 5.4%. The model is now available to developers through the OpenAI API for coding tasks in COBOL, Fortran, OCam, and Visual Basic.

AMD: Real-time control and reliability are the key to physical AI

AMD executive Salil Raje proposed that performance evaluation for physical AI should shift from raw compute power to real-time control and system-level reliability. AMD builds heterogeneous architectures with Kria AI SOM, Versal, Zynq, and Spartan FPGAs; in third-party tests, Kria AI SOM achieved inference times under 0.1 seconds and supported 8,000 control decisions per second; in open navigation benchmarks, the Ryzen AI Embedded configuration delivered control-timing reliability up to 3.4 times that of Nvidia Jetson T5000, with 1.6 times more available compute headroom.

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fal and BFL Release Video Model FLUX 3 Max

fal and BFL have partnered to release the post-trained video model FLUX 3 Max, with the team optimizing inference to improve output quality. The model scores 1,139 on the Artificial Analysis image-to-video index and generates up to 22 times faster than Seedance 2.5; fal says it can generate a 10-second 720/768p video in about 12 seconds, with a 75% price cut over the next three weeks.

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Viggle Releases JST-2, a 3D-Native World Model

Viggle has released JST-2, building its entire model stack in 3D space and representing the world with Physics-Native Tokens that carry xyz coordinates, attribute vectors, and dynamics vectors, rather than implicitly inferring physical laws from video pixels. The architecture consists of three parts: the JST encoder, JST generator, and JST decoder. A single forward inference generates a complete 3D scene in about 1 second, at a cost below $0.01 (B200), and supports up to 64 input views as a spatial context window. Blind human preference tests published by Viggle show that character generation wins 58.4% against Meshy 7.1 and 73.2% against Tripo H3.1, while video-generated motion wins 89.1% against DeepMotion. JST-3 plans to extend the learned dynamics to object interaction.

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Dream Labs Releases Robot Foundation Model Dream-1

Dream Labs exits stealth mode and releases the robot foundation model Dream-1, which can control dual-arm robots it has never seen before, perform tasks it has never been trained on, and enter locations it has never been to, starting at $3 per hour. The Dream API opens to everyone the same day, with its inference powered by fal Serverless.

fal Research and Black Forest Labs Launch the FLUX 3 Max Video Model

fal Research, in partnership with Black Forest Labs, has launched the video model FLUX 3 Max. Through dedicated post-training and inference optimization, it is tied for first place in image-to-video quality on independent third-party benchmarks, generating a 10-second 720/768p video in about 12 seconds. fal says its latency is only a fraction of that of competing frontier models, and the model will be 75% off for the next three weeks.

Ant Ling Thanks SGLang for Day-One Support of Ming-3.0

Ant Ling thanks the SGLang project for providing day-one SGLang-Omni support for Ming-3.0, and has released a practical cookbook to help developers get started. According to the original SGLang post, SGLang-Omni supports three versions of Ming-3.0 on day one: Ming-3.0-asr-tiny supports Chinese, English, and dialect ASR; Ming-3.0-tts-tiny supports voice cloning in 7 languages; and Ming-3.0-audio-flash is a 126B-A6.5B MoE model for audio understanding and reasoning.

Alibaba Tongyi MAI-UI team open-sources general computer-use agent tech stack OpenGCUA

Alibaba Tongyi MAI-UI team open-sources the complete general computer-use agent tech stack OpenGCUA, including harness, cross-device SDK, native macOS app, evaluation trajectories, and reference agent implementation. Its core design is that code execution and decision-making shift from the harness to the model; the model takes screenshots on demand, writes Python programs with loops and conditional judgments to orchestrate GUI operations, API calls, and shell commands, and maintains its own todo list. In evaluations of 8 frontier models on OSWorld-v2, the average partial score rose from 45.3 to 64.9, and the average number of steps per task decreased by 53%; in real-machine macOS tests, under the same harness, Qwen3.8-Max's inference token consumption was 19.4 times that of GPT-6 Astra, completing 3 of 4 tasks.

MiniMax releases the M3.1 Flash model, at one-third the cost of M3 and 3x faster

MiniMax has released M3.1 Flash, which reaches frontier-level performance on tasks such as Cowork (Automation-Bench 62%, ALE 30%), coding (DeepSWE 74%), and visual reasoning (BabyVision 91%), at one-third the cost of M3 and 3x the speed. The unified API price is $0.15/$0.60 per 1M input/output tokens, with cache reads at $0.015 per 1M tokens. The company says Space Bunny, which previously topped global leaderboards and processed 200T+ tokens, is an early version of M3.1 Flash, and the model weights will be released on October 23.

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SenseTime Releases Empirical Study on GPT-6-Astra Embodied Intelligence and the SenseNova-RoboRSI Framework

SenseTime has released an empirical study on GPT-6-Astra's embodied intelligence, covering 12 simulation benchmarks and 8 real robot platforms, and has simultaneously open-sourced the SenseNova-RoboRSI framework and a 27B model whose operating cost is about 1/50 that of GPT-6-Astra. Under a unified protocol (ICL plus up to 10x interaction budget), GPT-6-Astra achieved SOTA on all 12 benchmarks: RoboDojo rose from 27.6% at 1x budget to 81.9% at 10x, DexJoCo reached 87.3% versus the previous SOTA of 40.3%, and the LIBERO benchmark was pushed to saturation. The study also notes that the inference cost for simple tasks such as putting a pen into a pen holder is about $55, and that real robots overheat and wait times are too long; the conclusion is therefore that GPT-6-Astra has largely crossed the threshold of embodied capability, but practical-grade embodied intelligence has not yet arrived.