← Back to all news

AI safety and alignment

Alignment, interpretability, red-teaming, bias and privacy: the research on what these systems do when they misbehave.

17,798 stories · RSS feed

arXiv Computer Vision
Sep 22

AlignMorph: Tuning-Free Diffusion Image Morphing via Explicit Semantic Transport

AlignMorph is a tuning‑free diffusion framework for image morphing that separates geometric alignment from generative denoising. It uses Global Semantic Transport—entropic optimal transport and reliability‑aware latent warping—to achieve diffusion‑compatible semantic alignment, and Coordinate‑Aligned Generation—symmetric bi‑phase attention handoff—to preserve spatial coordinates during denoising. The method eliminates ghosting and delivers superior structural coherence and temporal smoothness on morphing benchmarks without any per‑pair optimization.

By Wuyi Liu, Xu Han, Yuren Chen, Yige Mao, Zishuo Peng, Xianzhi Li
diffusionbenchmarkssafety
More like this →
arXiv Computer Vision
Sep 22

Closed-Circuit Television Data as an Emergent Data Source for Urban Rail Platform Crowding Estimation

The paper explores the use of Closed‑Circuit Television (CCTV) footage to estimate urban rail platform crowding in real time. It compares three computer‑vision methods—object detection and counting, crowd‑level classification with a Vision Transformer, and semantic segmentation—to extract crowd-related features. A novel convex ridge regression technique is introduced to convert segmentation outputs into passenger counts, and the methods are evaluated on a privacy‑preserving dataset of over 600 hours of Washington Metropolitan Area Transit Authority (WMATA) video, showing that CCTV alone can provide valuable real‑time crowd estimates.

By Riccardo Fiorista, Awad Abdelhalim, Anson F. Stewart, Gabriel L. Pincus, Ian Thistle, Jinhua Zhao
llmscomputer-visionbenchmarkssafety
More like this →
arXiv Machine Learning
Sep 22

The Effect of Quantization on Clinical Benchmarks: Accuracy and Safety Across Model Families

The study evaluates how quantization affects accuracy and safety of five 7‑8B language models on clinical benchmarks. INT8 GPTQ shows minimal degradation (≤1.9%) across tasks, while INT4 causes substantial, model‑dependent drops, especially in high‑risk scenarios and safety metrics. Recovery methods such as clinical calibration substitution and QLoRA fine‑tuning yield mixed results, underscoring the need for task‑specific validation.

By Leonard Twagirayezu, Prasenjit Mitra
llmsfine-tuningefficiencybenchmarkssafety
More like this →