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New paper from Google DeepMind on training models to argue well about moral dilemmas. The trained model became more decisive and stopped changing its answer when options were reordered. Sycophancy and reframing still moved its judgments. The setup uses real dilemmas from

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DecisionPivot A diagnostic benchmark of 48 tasks and 40,033 items that shows how item accuracy alone hides large differences between decision models in LLM systems

"BeNice! Closing Looping’s Inference-Memory Gap via KV-Sharing" Looped Transformers reuse the same layers multiple times to improve performance without adding parameters, but their KV-cache memory still grows with every loop. This paper proposes a training method that lets different loops reuse the same KV-cache without losing accuracy, by combining stochastic-depth pretraining with a fine-tuning objective that makes earlier loops compatible with the final loop's cache. On a 1.3B model, KV-sharing reduces total GPU memory usage by 66.2% in a tested configuration. In simulated long-context workloads, it also improves throughput by up to 23.6% and reduces 90pt time-to-first-token by 83.6%, while preserving model quality. alphaxiv.org/abs/2610.31633

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"Agentic World Action Models" What if robots could write their own code to plan actions, while also predicting how the physical world will respond? NVIDIA's new AW-0 combines three capabilities in one model: generating reusable skill programs, predicting future video frames, and producing robot actions. All three are generated together, which enables code-based planning to guide physical execution. On RoboTwin 2.0, AW-0 achieves 50.7% average success, compared to 41.3% for π0.5. Across eight real-world manipulation tasks, it scores 71.1% vs 46.0% for the strongest baseline, including tasks like folding towels and solving a Rubik's Cube. alphaxiv.org/abs/2610.30136

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Highly recommended read on looped and depth-grown models. There is a lot of interest in looped and depth-grown language models for reasoning. The question here is what reusing layers does to in-context learning. At 3B parameters, both grown and looped models gain more from

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AI images never get the pose right? An open-source tool lets you pose them yourself AI images keep getting poses wrong and things in the wrong place? ArtCraft lets you first set up characters, props and the camera in 3D, then has AI draw the image and generate the video. Github: github.com/storytold/artcraft

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😱 Unbeatable! A 30B-class open-source agent pushed to the ceiling! Nanbeige4.3-33B is officially live! It outperforms Qwen-3.8 27B! It achieves SOTA agent performance among open-source models of about 30B parameters, crushing complex coding and professional collaboration tasks across the board. It is both a ready-to-use strong teacher model and can be plugged into real agent frameworks at low cost to get work done. What makes it great: 🔹Terminal-Bench 2.1 80.9 / 4.0 23.8 / DeepSWE 65.5, leading Qwen3.8-27B and others by a wide margin on several coding agent benchmarks 🔹Leads across collaboration tasks such as Automation Bench, JobBench and DeepPlanning, covering real workplace scenarios in many industries 🔹512K ultra-long context + cost-efficient inference, usable directly as a teacher to distill later small models such as Nanbeige4.3-3B How to use it: 1️⃣Go straight to HF to download the model: huggingface.co/Nanbeige/Nanbeig… 2️⃣Load with Transformers (trust_remote_code=True), enable enable_thinking in the chat template (for agent scenarios, preserve_thinking=True is recommended) 3️⃣Recommended deployment: vLLM / SGLang + --reasoning-parser qwen3 --tool-call-parser qwen3_coder; for agent tasks temp=1.0, top_p=0.95 4️⃣Plug it into your agent framework (OpenClaw or any mainstream harness works) and start on real tasks #LLM #Agent #开源模型 #Nanbeige #AgenticAI #30B模型 #Qwen3827B

GameScaling: miHoYo's study on scaling embodied agents in games Continued pre-training from 0.8B to 27B on 1,600 hours of gameplay across 80 diverse virtual worlds. Models are open-source on Hugging Face.

Tencent just released WeVisOne-VL on Hugging Face One vision-language model for detection, counting, pointing, OCR, segmentation and alpha matting — from objects to pixels.