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.

Zoom

NVIDIA AI announced Nemotron 3.5 Transcribe on Oct 28, with weights on Hugging Face and ModelScope. It is an offline, instruction-driven speech model whose single checkpoint covers transcription in 40 languages, translation between 31 languages and English, speaker attribution in 13 languages, and audio question answering, on recordings up to an hour long with up to eight labeled speakers. NVIDIA says it ranks first among open-weight models on the Hugging Face Open ASR English leaderboard at 5.33% average WER, and Artificial Analysis measured a 2.8% AA-WER, tied for first among open models with Voxtral Small, which uses 24B active parameters. The weights, the Granary v2 training data and fine-tuning recipes are all released.

Zoom

On Oct 26 Mirendil unveiled Fern, its first model, pretrained from scratch on its own Mirendil-Base (0.1-0.5T params) with a context window of up to ~10M tokens, about 0.5M lines of code or days of agent traces. Fern 0.1 got only a short round of post-training and targets AI R&D work such as auditing training data, debugging runs from logs and reading agent traces end to end. Vendor-reported: 73.4% on SWE-Bench Verified and 100% single-needle RULER retrieval at 10M, while multi-hop QA drops to 45% at 10M. Access is early-access only; no price or weights yet.

Zoom

Training data analysis agents is bottlenecked by privacy restrictions and unverifiable open-ended insights. Researchers introduce Data World (arXiv:2610.21656), a multi-agent engine that specifies underlying causal world mechanisms before generating heterogeneous files and verifiable rubrics across 1,037 disjoint worlds. Pure RL-zero training on Qwen3.6-35B-A3B yields DataWorld-35B-A3B, jumping +20.90 points on DABstep-Hard and +14.28 on DAComp. Alongside MRTPO (arXiv:2610.21439) for token-level multi-reward credit assignment, these works unlock scalable RL for data and SQL agents.

Zoom

Recent leaps in language model performance stem predominantly from verifiable agentic training data rather than architectural revisions alone. However, generating verifiable tasks for supervised fine-tuning and reinforcement learning still relies heavily on expensive human-in-the-loop engineering, hindering recursive self-improvement (RSI). Researchers from CUHK and collaborating organizations introduce AutoDataBench, the first benchmark evaluating an agent's capability to autonomously synthesize verified training tasks against industrial production acceptance standards. Unlike traditional evaluation schemes requiring expensive end-to-end retraining runs, AutoDataBench directly inspects generated tasks across validity, novelty, difficulty, and behavioral coverage. Evaluated on three executable benchmarks, existing frontier agents score below 20/100 within a 45-minute budget; however, quadrupling the test-time budget significantly boosts success rates while maintaining unit costs, proving that autonomous synthetic data pipelines are viable when scaled with compute.

Zoom