AI Coding Live News

webAI CEO David Stout announced on X on November 4 (17:37 UTC) that the company is open-sourcing TwIL-8B. The official Hugging Face model card says it is post-trained from IBM Granite 4.2 8B with full-parameter SFT, GRPO/DAPO reinforcement learning and a length-aware RL stage, and ships in balanced and verbose variants. Self-reported scores include LiveCodeBench 81.6 (verbose), MATH-500 98.2 and IFBench 74.4, about 93% and 92% of the best frontier score, while GPQA Diamond is only 70.2. A 4.98 GiB Q4_K_M GGUF runs locally; the license is the webAI Non-Commercial License.

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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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Hangzhou-based AI-for-science company Deep Principle released Hydrogen-1.0 on November 2, its first general-purpose scientific model. It post-trains Zhipu's GLM-5.3 with SFT and RL on chemistry, materials and life-science data plus its own lab records and discovery trajectories, and ships about 753B parameters of BF16 weights on Hugging Face under the MIT license. By the vendor's own evaluation it scores 38.3% on FrontierScience-Research versus 33.3% for GPT-6 Astra; none of the results have been independently reproduced yet.

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While supervised fine-tuning (SFT) distills agentic tool-use capabilities from frontier LLMs into small language models (SLMs, 0.6B to 8B parameters), student agents frequently suffer from 'progress stagnation'—collapsing into repetitive, unproductive tool-calling loops as interaction trajectories extend. In arXiv:2610.19439, researchers introduced Progress-Aware Distillation (ProD). ProD defines a rigorous stagnation scoring formulation that dynamically identifies and penalizes stagnant behavior during student trajectory rollouts via an adaptive SimPO-inspired margin. Experiments across 8 diverse SLMs prove ProD effectively eradicates repetitive loops while boosting task completion efficiency and success rates.

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Tackling reward hacking and benchmark saturation in coding agent environments, CMU researchers introduce Scalable Weak-to-Strong Verifier Hardening (arXiv:2610.15924)—combining an isolated verifier sandbox with an iterative hacker-fixer loop that slashes exploit success across Terminal-Bench, KernelBench, and SETA, and is now officially adopted by Terminal-Bench. Complementing this, Recursive Environment Scaling (RES, arXiv:2610.16397) recursively mutates execution dependencies across 294 tasks—exposing a 57.14-77.14 point drop on Claw-Eval-Live while boosting equal-budget SFT performance by 7.00-14.61 percentage points across all nine model-benchmark pairs.

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Advancing autonomous AI-for-AI (AI4AI) engineering, researchers from Shanghai AI Lab (InternScience) release MLExpert (arXiv:2610.22344), a 35B Mixture-of-Experts agentic model for autonomous machine learning engineering. Powered by the Kaggle-MLE Factory, hierarchical agentic SFT, and Windowed Interaction RL (WIRL), MLExpert boosts average medal rates on MLE-Bench Lite from 37.9% to 72.7%—matching frontier giants like Kimi-K3 and GLM-5.3 while generalizing to agent scaffold optimization.

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To test whether frontier agents can autonomously conduct end-to-end post-training research, researchers introduce Athanor, an agent-native system for autonomous post-training (AutoPostTrain) on real GPUs. In a 104-hour campaign without human advice or external teacher models, four Claude Opus 5.5 agents—each operating an 8-GPU node—improved raw Qwen3-4B-Base from 15.9 to 67.3 on the dev suite and from 9.2 to 48.2 on an unseen held-out suite, surpassing the official Qwen3-4B-Instruct-2507 (66.5 and 47.9).

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Microsoft Research and Stanford University have unveiled AutoEnvScaling, an automated data flywheel that frames RL environment creation itself as a terminal execution task. A proposer agent builds diverse environments directly from solver rollouts, slashing cost per valid environment by 75% while achieving a 25x higher valid environment yield over prompt-only baselines. Crucially, unifying environment design and solving within a single model enables Recursive Self-Improvement (RSI), yielding +12.2 points on Terminal-Bench 2.1 and doubling scores on Terminal-Bench 4.0.

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While LLM agents are rapidly deployed into sandbox workspaces with direct shell and filesystem access, post-training methods remain predominantly one-shot and bound by external teacher models. ClawLoop introduces a self-improving, harness-native training loop that operates without external teachers. Through a self-sustaining cycle of task synthesis with difficulty growth, SFT/RL training, and automated behavioral diagnosis, ClawLoop demonstrates consistent empirical gains across SkillsBench, Claw-Eval-Live, and ClawGym-Bench on Qwen3.6-27B, while successfully transferring data to 4B and 9B models.

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Current software engineering benchmarks and training pipelines predominantly evaluate coding agents on localized bug fixing within existing repositories (such as SWE-bench) or isolated code snippet generation. They fail to teach agents how to explore unfamiliar reference systems, formulate architectural blueprints, and reproduce complete, multi-file software projects from scratch under long-horizon constraints. Researchers introduce ProgramGym (arXiv:2610.13395, repo: github.com/Acm1011/ProgramGym, dataset: ProgramGym/programgym-1648), an automated framework scaling verifiable training environments for program reproduction agents. ProgramGym consists of Program-Env, a feedback-driven engine that converts open-source repositories into verified runtime sandboxes paired with black-box test suites across C, C++, Go, and Rust (generating ProgramGym-1648), alongside Program-Agent, an iterative workflow enforcing role isolation across Analysis, Implementation, and Review stages with revision management. Fine-tuning Qwen3.8-27B on ProgramGym trajectories raises ProgramBench test pass rates from 34.87% to 42.84% (+7.97 points), outperforming GPT-5.4 under mini-SWE-agent (37.70%). Coupling SFT with the Program-Agent scaffold boosts performance to 56.25%, eclipsing Claude Opus 4.7 (50.90%) and Gemini 3.5 Flash (53.60%), with datasets and codebase open-sourced.

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When model capabilities exceed human expert evaluative bandwidth, achieving Recursive Self-Improvement (RSI) without external verifiers or compilers becomes the paramount frontier of AGI. While reinforcement learning with verifiable rewards (RLVR) thrives in formal code and math, open-ended research and complex reasoning tasks lack scalar ground truth, and solitary models evaluating their own outputs suffer from homogeneous confirmation bias. Sakana AI introduces MASS (arXiv:2610.12176, project: sakana.ai/mass), an RSI framework anchored in Multi-Agent Self-Supervision. Driven by a single base model (such as Qwen3.6-27B), MASS tasks the model with concurrently acting as task solver, workflow optimizer, and self-evaluator. The framework alternates between evolutionary multi-agent workflow discovery and supervised fine-tuning (SFT) over synthesized trajectories. Across two successive MASS self-evolution cycles, performance per output token increases by 1.2x to 1.6x across open-ended research suites (MLR-Bench, ScienceAgentBench, DSBench, AstaBench). Head-to-head win rates against the base model surge from 53.9% in round 1 to 69.9% in round 2, while a student model trained on multi-agent traces decisively beats a single-agent baseline trained on 1.4x more tokens.

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Humans solve complex spatial transformations by mentally simulating physical visual transitions, maintaining visual imaginations to ground multi-step reasoning. In contrast, existing Vision-Language Models (VLMs) tackle spatial questions almost entirely via text-token chains of thought, triggering acute hallucinations when predicting 3D rotations or spatial clearances. Researchers from Carnegie Mellon University and MBZUAI introduce WM-VLM (arXiv:2609.34826), integrating a lightweight world model branch directly into foundation VLMs to synthesize intermediate visual states during inference. The framework adopts a two-stage training paradigm: first training the model to foresee and render verifiable intermediate visual states, and then training it to ground downstream logical deductions upon those synthesized states. Evaluated on rigorous 2D and 3D mental rotation tasks, WM-VLM outperforms standard SFT baselines by up to 39.25 percentage points. Ablations prove that performance collapses when generated visual frames are removed or corrupted, establishing that internal world models provide indispensable spatial cognitive scaffolds.

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