AI Coding Live News

At BoxWorks 2026 on November 5, Box announced an expanded NVIDIA collaboration: Box Context Retrieval, a new engine built on NVIDIA Nemotron Retriever embedding models, combines keyword and semantic search to return the most relevant passages to AI agents while honoring existing file permissions, with general availability for Enterprise Advanced customers planned for early next year. Box's press release claims 20% better search accuracy (Recall@5), 90% fewer tokens and 4x faster answers. Open-weight Nemotron models, starting with Nemotron 3 Ultra, are available in Box AI now. Box also unveiled Box Architect, which designs workflows from a stated goal; Box Mount, which mounts Box folders inside agent sandboxes and works with NVIDIA OpenShell; and agent security controls including MCP Guardrails.

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Ricursive Intelligence released its first model, Ripple-65B Preview, on November 4 (18:24 UTC), open-sourcing the instruction-tuned model (BF16 and NVFP4) and a base model on Hugging Face under Apache 2.0. The official model card describes a 65B dense Transformer pretrained on 12T tokens with up to 1M context, and self-reports TerminalBench 2.1 80.5, SWE-bench Pro v2.0 Hard 79.1 and DeepSWE 1.1 44.3, ahead of much larger US open-weight models on agentic coding. It fits on local hardware such as the DGX Spark. CEO Anna Goldie says it cost about a tenth of the prior comparable state of the art.

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Cognition released SWE-2 Flash on November 4 (19:55 UTC). Its official blog says the model is built on Thinking Machines' Inkling-Small (276B total, 12B active parameters) and, for the first time, adds a large mid-training stage before reinforcement learning, lifting FrontierCode 2.0 from 3.2% to 24.0% and Terminal-Bench 4.0 from 0.9% to 20.0%. Cognition says it beats every American open-weight model. It generates about 200 tokens per second at one-fifth the price and 3x the speed of SWE-2, and is free for Pro, Max and Teams users in Devin Desktop and CLI until the end of November. Weights are not public; a secure sharing program for partners and researchers is planned.

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Axiom released a Lean formalization of the entire Stacks Project on November 4 (official X post at 17:13 UTC, with over 2 million views). The roughly 7,500-page open-source algebraic geometry reference, started by Johan de Jong in 2005, becomes about 16 million lines of code, which Axiom calls the largest integrated, reusable formal math library ever built. Hierarchically coordinated AI agent swarms did the work and surfaced dozens of statements in the original that needed local fixes, with counterexamples. The library is open source and browsable side by side with the original text at stacks.axiommath.ai.

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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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CellType, a YC W26 startup, released BioSimBench 1.0 on November 4. It uses real lab experiments to test whether language models can predict which genes go up or down after a cell is hit by a drug, a gene knockdown or an immune signal. Among 12 frontier models, Gemini 4 Argon ranks first with an overall score of 0.184, ahead of Claude Sonnet 5.5 (0.160) and GPT-6 Astra (0.159), while two lab replicates agree at 0.462. A 27B model CellType trained in its BioSimGym RL environments beats the best frontier model on all four prediction tracks.

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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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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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Independent evaluator Artificial Analysis announced on X on November 4 that its six Capability Indexes (Finance & Accounting, Strategy & Ops, Legal, Healthcare & Medical, Engineering, Economics) are now v2.0, aligned with Intelligence Index v5.0 evaluations and weighting Non-Hallucination and Agentic Tool Use more heavily. Across the indexes, HLE becomes HLE-Diamond, AA-Omniscience moves to v1.1 and GDPval-AA is removed; Engineering gains a new Science capability. Per Artificial Analysis, Claude Opus 5.5 (max) leads all six, with GPT-6 Astra (max) second in Finance & Accounting, Legal and Engineering. Among open-weights models, Kimi K3.1 Pro (weights not yet released) leads all six at #5 to #8.

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On November 3, model-compression company Pruna AI open-sourced P-H3-Image, which turns MiniMax-H3, the open-weights video model MiniMax released this summer, into a text-to-image and image-editing model. Per Pruna: sampling drops from 50 to 8 steps, a 4 MP image on one B200 goes from 12.4 s to 2.6 s, the 4-bit set is a 20 GB download (vs 143 GB for H3), and peak VRAM at 8 MP is about 21 GB. It renders 2 to 16 MP and edits with multiple reference images. Weights are on Hugging Face with native ComfyUI support. It keeps the MiniMax H3 Community License, which by default excludes the EU, UK, South Korea and the US.

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On November 3, Menlo Ventures released its fourth annual enterprise AI report. It estimates enterprise spending on using AI reached $176.8B by September 2026, up 5.4x from $33B in 2025. In enterprise LLM API spend, Anthropic rose from 40% to 55%, OpenAI from 27% to 31%, and Google fell from 21% to 9%. Coding is the largest app category at $34.0B. Figures come from a survey of about 400 US enterprise AI decision-makers plus third-party data, so they are estimates; Menlo is an Anthropic investor.

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Indian AI company Sarvam released Sarvam Prism on November 3, a "System One" decision model for enterprise workflows: given a context and questions with fixed answer options, it returns a probability for every option in a single forward pass instead of generating text. Sarvam says that across the 25 decision models it tested, Prism averages 71.8 over three indices with a median response time of 44 ms on one B200, the highest score under 100 ms, and scores 82.5 on its Indian-language benchmark. Weights are on Hugging Face under Apache 2.0, with a hosted API as well. All scores are Sarvam's own measurements.

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