AI safety and alignment

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

10,300 stories · RSS feed

arXiv AI
Jul 2

A Multi-Resolution Finite-Volume Inspired Deep Learning Framework for Spatiotemporal Dynamics Prediction

arXiv:2607. 00460v1 Announce Type: cross Abstract: Predicting complex spatiotemporal dynamics in physical processes often demands computationally expensive numerical methods or data-driven neural networks that suffer from high training costs, error accumulation, and limited generalizability to unseen parameters.

By Xin-Yang Liu, Xiantao Fan, Jian-Xun Wang
arXiv Machine Learning
Jul 2

Generated Contents Enrichment

arXiv:2405. 03650v4 Announce Type: replace-cross Abstract: We study Generated Contents Enrichment (GCE), a conditional image-generation task in which a sparse scene description is first enriched through an explicit scene representation and then rendered into semantically richer visual content.

By Mahdi Naseri, Jiayan Qiu, Zhou Wang
arXiv AI
Jul 2

Learning Gait-Aware Quadruped Locomotion with Temporal Logic Specifications

arXiv:2607. 00442v1 Announce Type: cross Abstract: Reinforcement learning (RL) for quadruped locomotion commonly depends on fixed, hand-crafted, and Markovian reward functions that limit both interpretability of learned policies and lack explicit control over gait behaviors.

By Merve Atasever, Cagan Bakirci, Alfredo Reina Corona, Keyan Azbijari, Jyotirmoy V. Deshmukh
arXiv AI
Jul 2

A Two-stage Transformer Framework for Temporal Localization of Distracted Driver Behaviors

arXiv:2603. 21048v2 Announce Type: replace-cross Abstract: The identification of hazardous driving behaviors from in-cabin video streams is essential for enhancing road safety and supporting the detection of traffic violations and unsafe driver actions.

By Gia-Bao Doan, Nam-Khoa Huynh, Minh-Nhat-Huy Ho, Khanh-Thanh-Khoa Nguyen, Thi-Thu-Hien Pham, Thanh-Hai Le
arXiv AI
Jul 2

Adversarial Pragmatics for AI Safety Evaluation: A Benchmark for Instruction Conflict, Embedded Commands, and Policy Ambiguity

arXiv:2607. 01153v1 Announce Type: cross Abstract: Safety evaluations for language models increasingly depend on judgments about ambiguous natural-language behaviour: whether a model has followed an instruction, refused appropriately, complied with a policy, resisted an embedded command, or misreported progress in an agentic task.

By Brett Reynolds