AI Coding Live News

arXiv paper 2611.03233 by Yuqi Jia, Yunjie Tian and five co-authors introduces Axion 1.0, an agent foundation model that is open beyond its weights. It uses just two lightweight SFT stages plus reinforcement learning, with no dedicated mid-training, DPO or on-policy distillation, and about 18K SFT trajectories in total. Training code is hosted under the bytedance GitHub organization, and the training data and an Axion 1.0-35B reproduction checkpoint (Qwen3.6-35B-A3B base, 35B total / 3B active, Apache 2.0) are on Hugging Face; weights for the paper's 122B model are listed as coming soon.

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On Oct 27 Liquid AI released LFM3, a new generation of foundation models built on a tri-hybrid architecture of gated short convolutions, sliding-window attention and global attention, pre-trained on 22T tokens. The first six models all branch from LFM3-1.7B-Base at around 2B parameters, with weights on Hugging Face under the LFM Open License v1.0. Liquid says the text model LFM3-1.7B needs about 1 GB at 4-bit, supports 256K context and beats models several times its size on several agent benchmarks.

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On Oct 22 Canva released Canva Craft, the first image-generation foundation model trained from scratch by its in-house lab, Canva Research. It works in two stages: a planning model writes out the composition (elements, spatial relationships, hierarchy and any text), then a rendering model produces the image at up to 2K across more than 30 aspect ratios. Training leaned heavily on millions of human-made templates from Canva's library, such as posters, social posts, slides and infographics. Canva says it debuted 4th overall on the crowdsourced DesignArena leaderboard and 1st in the People & Portraits and Landscapes & Nature categories. It is available now to all Canva users in Dream Lab and to developers through the Connect API as craft_v1_2k, with Canva MCP access coming soon.

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During the GTC Berlin keynote, NVIDIA unleashed a sweeping array of open-weights domain-specific foundation models. The release highlights include the multimodal Nemotron 3.5 Super VL, the lightweight Nemotron 3.5 Bolt series, and the end-to-end Nemotron Retriever suite for embedding, reranking, and document OCR. For physical AI, NVIDIA debuted the Cosmos world models and Alpamayo 2 Edge autonomous driving models. In life sciences, NVIDIA and the Baker Lab released BioNeMo RFD4-Proteina, which establishes state-of-the-art accuracy across more than 10 protein design benchmarks while synthesizing 200-amino-acid binders roughly 13x faster than RFdiffusion3.

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The Shanghai Artificial Intelligence Laboratory published the technical report for Sage-A1.5 on arXiv:2610.28578, introducing an open-weights 35B Mixture-of-Experts (MoE) foundation model purpose-built for agentic tasks. Building on the Agents-A1 lineage, Sage-A1.5 focuses on scaling the agent execution horizon rather than model parameters through adaptive capability composition. Tailored for complex repository-level software engineering and multi-step scientific hypothesis exploration, Sage-A1.5 consistently outperforms dense 70B+ baselines on long-horizon reasoning benchmarks while maintaining lightweight deployment footprints.

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Researchers introduced MemT-1.0 (arXiv:2610.26825), a groundbreaking architecture internalizing memory as an intrinsic component of foundation model computation. Moving beyond exploding KV caches and lossy Vector RAG pipelines, MemT-1.0 employs Gated DeltaNet-2 with stable within-turn token-level updates alongside a novel Mixture-of-Storage mechanism. Trained across a two-stage 34B-token pipeline, the released MemT-1.0-Nano model achieves a 1.86x end-to-end inference speedup and a staggering 66x reduction in persistent storage at 128K tokens relative to full-context KV caching, dramatically elevating procedural task execution in autonomous agents.

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On Oct 19 (UTC) fal released FLUX 3 Max, a video model built by fal Research with Black Forest Labs through post-training, reinforcement learning and inference optimization on top of the FLUX 3 foundation model. It handles text-to-video, image-to-video and keyframes, up to 20 seconds, at 720p or 1080p, with audio on by default. On Artificial Analysis's preliminary image-to-video arena it scores 1,139 Elo, level with Seedance 2.5 and within the confidence interval of leader MiniMax H3 (1,141), and fal says a 10-second clip takes about 12.45 seconds. A 75% launch discount runs until Nov 7, putting 720p at $0.0425 per second.

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On October 15 Mistral released Timestral, its first family of time-series foundation models, in 5M, 28M, 123M and 209M sizes. Weights are on Hugging Face under a Modified MIT license and the inference code is Apache 2.0. The models produce multivariate probabilistic forecasts with 21 quantiles per step, accept past-only and known-future covariates, and take up to 32,768 steps of context. Mistral says the 209M model ranks first among pretrained zero-shot models on GIFT-Eval, TIME and fev-bench with about two-thirds the parameters of the previous best; on an 8-thread CPU, forecasting one day from three weeks of 15-minute data takes 10 ms with the 5M model and 140 ms with the 209M. These are Mistral's own figures; the technical report is still to come.

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On October 19 Dream Labs came out of stealth with a $90M seed round and opened a public API for Dream-1. Dream-1 is a World Action Model (WAM) that learns to predict video and actions together. The company says it can zero-shot control bimanual parallel-gripper robots without any demonstrations from that robot, and it ran 27 unseen robots in simulation, including the 18-year-old PR2. Pricing is $3/hour for Dream-1 and $2/hour for Dream-1-flash. In an independent Robocurve evaluation, Dream-1 scored 59 on RoboDojo-RC Tier 1 versus 48 for the previous best. The company stresses it is a research beta not validated for safety-critical use.

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Viggle released JST-2 on October 19. Instead of learning in pixel or video space, it represents the world as persistent "atoms" in 3D space (Physics-Native Tokens) and uses one architecture to generate scenes, characters and motion. Viggle says a 3D scene takes about one second on an NVIDIA B200 at under $0.01 of inference, with up to 64 input views kept as spatial context, and characters animate without a separate rigging step. It is free to try in the PINOC browser app and is also available via API and MCP.

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Modern 3D world models typically use view-dependent features from geometry foundation models (GFMs) as predictive states, entangling camera-induced variations with physical scene dynamics. Tongji University and Tencent introduced GeoState (arXiv:2610.22343). By aggregating multi-view GFM features into a view-decoupled 3D scene state, GeoState models physical scene evolution independently of camera viewpoints, maintaining a single context-derived scale throughout rollouts without per-frame scale realignment.

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On 2026-10-15, MBZUAI’s Institute of Foundation Models (IFM) and Inception42 released Jais 3—the first language adaptation of the open K2 Horizon fleet. Six open-weight sizes run from a 0.9B dense phone model to a 375B-A23B sparse MoE (≈23B active), plus Jais Speech for Arabic ASR. Models share one training recipe and agent stack, with up to 512K context (128K on 0.9B), Apache-2.0 weights and stage checkpoints on Hugging Face, and day-0 vLLM support.

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Robotics research typically separates world models (predicting scene transitions) from control policies (generating actions), fragmented by proprietary robot-specific kinematic formats. Sber AI researchers introduced GreenWAM, a foundational world model for robotics. GreenWAM unifies 20 robot embodiment families (from bimanual arms to dexterous humanoids) under a common joint coordinate space, co-training across visual, action-conditioned, and world-action objectives with specialized action-expert heads. GreenWAM achieves top-tier 3D trajectory and depth accuracy on WorldArena 1.0, establishes state-of-the-art policy benchmark scores on RoboTwin 2.0 and LIBERO-plus, and successfully drives real-world humanoid robots outperforming Cosmos 3 Edge and pi_0.5.

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On 2026-10-14 NEURA Robotics launched Neuron AI, a Physical AI foundation model built on a Perception-Language-Action (PLA) architecture that aligns 3D vision, touch and force, audio, robot state and language in one space, adds memory, and runs on the robot itself across five NEURA embodiments. No quantitative benchmarks were published. Customers fine-tune skills and roll them out to fleets via Neuraverse; a fully autonomous 11-minute laundry task will be demoed at NVIDIA GTC Berlin on October 20–22.

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