The paper derives the exact posterior score in closed form for linear Gaussian inverse problems, enabling efficient posterior sampling via denoising. It introduces Exact Posterior Score (EPS), a training objective that preserves pretraining structure and achieves superior performance on fidelity, perceptual, and distributional metrics with fewer denoiser evaluations than gradient-based methods.
vLLM has introduced a new streaming parser for Qwen3+ available in its nightly build, addressing issues like mid-turn stopping and failed streaming tool calls due to chunk boundaries. The update reportedly resolves these problems in limited testing, improving reliability for agentic workflows.
HalBench evaluates 29 open-source LLMs on a custom benchmark for sycophancy and hallucination. Qwen 3.6 and Gemma 4 outperform larger models, with Qwen 3.6 achieving 36.6% pushback—higher than GPT-5.4 and Gemini 3.1 Pro. Model size does not correlate with honest responses, indicating that architecture and training data matter more than parameters.