AI Coding Live News

The official Kimi account @Kimi_Moonshot announced the Kimi K3.1 Pro model weights at 16:00 UTC on November 4; the moonshotai/Kimi-K3.1-Pro repository is public and not gated. The model card describes a natively multimodal MoE with 2.8T total and 104B active parameters and a 1M-token context, shipped as MXFP4 weights from quantization-aware training, about 1.56TB in total; vLLM, SGLang and TokenSpeed are the recommended engines. The license allows free use and modification, but Model-as-a-Service businesses with over $20M in annual revenue need a separate agreement.

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On the evening of November 2, SenseTime released the official version of SenseNova 6.8 Flash, a multimodal agent model with two main upgrades. First, long-term memory and proactive interaction: it keeps user goals and constraints across tasks and sessions and flags affected items when information changes. Second, design generation across Web, SVG, Three.js, Blender, animated PPT and motion posters, with Figma integration. It is live in SenseNova Studio, the Raccoon assistant and the SenseNova Token Plan; the official X account says the Token Plan offers a free trial of 1,500 API requests every 5 hours. SenseTime says it beats some international frontier models on agent collaboration, data analysis and presentation generation, but the announcement lists no specific scores.

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Flower Labs, the company behind the Flower federated learning framework, released Endeavor 1.1 on November 3, its third model this year after the UK sovereign 7B model Lizzy in April and Endeavor 1.0 in September. The update adds image input, an optional EU-only GPU inference tier, and Flower-managed, private-cloud or on-premises deployment, and promises readable thinking traces plus ownership of outputs that customers may use to train their own models. Vendor-reported scores are GPQA 94.4, HumanEval 99.9, AIME 2026 99.9 and MMMU-Pro 81.1, but the comparison uses GPT-5.6 Sol and Claude Fable 5 rather than either lab's newest models, and access is request-only for now.

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On October 30, Microsoft's Mage team released Mage-VL-Cowork on Hugging Face under Apache-2.0: a 4B multimodal computer-use agent in which a single model decides at every step whether to act through screenshots and GUI clicks (computer_use) or through bash and Python in a terminal, with no external orchestrator. Microsoft reports a two-run average of 66.0% on OSWorld-Verified (361 tasks, 50-step budget), above Qwen3.6-27B at 63.9% but below the 9B SCALECUA-Qwen3.5-9B at 68.7%.

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On Nov 2 Meituan's LongCat team listed LongCat-2.5 in its API platform change log as the full release of September 25's LongCat-2.5-Preview. The official technical blog says it has 1.6 trillion total parameters with about 48 billion activated per token, builds on LongCat-2.0 with stronger native visual understanding and agentic coding, and was trained and served with W4A4 inference on domestic AI accelerators. Vendor-reported scores include 88.4 on Terminal Bench v2.1, 38.5 on Terminal Bench v4.0, 91.9 on BrowseComp and 83.1 on MCP Atlas. The API speaks both OpenAI and Anthropic formats with a 1M-token context and up to 128K output tokens, at a limited-time price of $0.30 per 1M uncached input tokens, $0.006 cached input and $1.20 output.

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On Oct 29 Mistral released an improved Mistral Large 4 (Le Chonk) with open weights on Hugging Face (plus an NVFP4 quantized checkpoint), served on its API as mistral-large-4. After about three more weeks of reinforcement learning since the early-October preview, Mistral reports 19.7% on FrontierCode 2.0, Terminal-Bench 4.0 up from 28% to 41%, and new low/medium/high reasoning modes. The license is a modified MIT that allows commercial use, but running a model-as-a-service business needs a separate commercial license.

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On 2026-10-23 (17:15 UTC) the official @Kimi_Moonshot account launched Kimi K3.1 Pro, alongside K3.1 Pro Highspeed (2-3x faster). Both are live on Kimi.ai, Kimi Work, Kimi Code and the Kimi API, and full weights are promised by 2026-11-04. The official tech blog describes it as a post-trained version of K3 with much larger multi-domain joint RL: a 2.8T-total / 104B-active MoE, 1M-token context, multimodal. Vendor-reported scores: 65.7% on Terminal-Bench 4.0 (GPT-6 Astra 58.2%, Claude Opus 5.5 66.4%) and 92.7% on BenchCAD with tools. The Kimi API pricing page lists kimi-k3.1-pro at $2 input, $10 output and $0.20 cached input per million tokens, down from K3 at $3/$15.

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Microsoft Research (MSRA) researchers published and open-sourced WildAWM (arXiv:2610.26828, GitHub: MSR-Orchard/WildAWM), a generalist agent world model designed for open-ended 'in the wild' digital environments. Traditional computer-use agents rely on slow, brittle, and expensive live web/OS sandboxes for training and evaluation. WildAWM establishes a scalable multimodal world model that accurately predicts visual screen transitions and DOM mutations conditioned on agent actions (click, type, scroll). By replacing live execution environments with a high-fidelity neural simulator, WildAWM unlocks safe, accelerated offline reinforcement learning for GUI agents.

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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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Researchers introduced MemPilot (arXiv:2610.06830), an on-demand multimodal memory curation framework for LLM agents. Resolving the chronic context explosion and cross-session forgetting that plague autonomous agents handling mixed text, screenshots, and action traces, MemPilot introduces an adaptive policy controlling memory retention, semantic clustering, and targeted retrieval. Across extensive long-horizon multi-modal agent benchmarks, MemPilot cuts active memory footprint by over 60% while substantially improving cross-session task completion.

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Adobe Research and Brown University released OmniXRef (arXiv:2610.22744), a unified framework for multimodal multi-reference video editing. Overcoming the limitations of prior systems constrained to text prompts or single visual exemplars, OmniXRef utilizes a single-stream Diffusion Transformer with Mix-Patchify and Reference-Relative RoPE (R2-RoPE) to jointly control camera motion, subject content, dynamic motion, style, and visual effects (VFX). In camera trajectory evaluations, OmniXRef cuts rotation and translation errors by more than 5x compared to leading commercial engines.

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Multimodal LLMs excel at holistic image captioning but struggle with pixel-level spatial grounding due to lack of spatial supervision. Xiaomi AI Lab unveiled Xiaomi-Lens-0 (arXiv:2610.23279), a 4B MLLM based on Qwen3.5 that natively predicts pixel masks. A lightweight decoder integrates patch embeddings, visual latents, and mask-token activations, driving RefCOCO comprehension (REC [email protected]: 94.6% / 92.1% / 92.5%) and segmentation (RES cIoU: 83.4% / 79.9% / 81.1%) past Florence-2 and LISA-7B while preserving a 69.7% MME-RW score.

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Three new frameworks advance web and multimodal agent post-training: Salesforce AI Research's CLIFT (arXiv:2610.06829) certifies URL-conditional self-verification questions for dense RL rewards and judge-free Conformal Trajectory Selection (CTS), propelling Gemma-4 31B to a 74.6% SOTA on WebArena Infinity (beating Gemini 3 Flash at 70.1%) and lifting GPT-5.5 to 53.7% on VisualWebArena. Meanwhile, MMSearch-OPD (arXiv:2610.22240) boosts 9B multimodal deep search by +15.3 points to 60.2%, and Northeastern University's SCOPED-8B (arXiv:2610.22217) uses skill-decoupled on-policy expert distillation to reach 73.13% across 8 benchmarks—surpassing Qwen3-VL-32B (72.01%) and Claude Opus 4.6 (72.86%).

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Google AI Studio launched Gemini 4 Argon access in AI Studio Build and Playground for Google AI Pro subscribers, while Hugging Face Transformers v5.20.0 and v5.20.1 added native support for Reflection AI's frontier coding model Beam, Meta's 816B-parameter (42B active, 1M context) multimodal agentic coding model Muse Spark Open, InclusionAI's Ling 3.0 Flash, and OpenBMB's MiniCPM-V 4.7.

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