arXiv Machine Learning

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints for Reliable Decision-Making

arXiv:2602. 07008v3 Announce Type: replace-cross Abstract: Reliable models should not only predict correctly, but also justify decisions with acceptable evidence.

arXiv Computer Vision

Aug 25

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints

arXiv:2602.07008v5 Announce Type: replace Abstract: Reliable models should not only predict correctly, but also base their decisions on acceptable evidence. However, conventional supervised learning...

By Ruoyu Chen, Shangquan Sun, Xiaoqing Guo, Kangwei Liu, Sanyi Zhang, Zhangcheng Wang, Shiming Liu, Qunli Zhang, Wei Wang, Hua Zhang, Xiaochun Cao

agents computer-vision safety

arXiv Machine Learning

Sep 10

Learn to Rank: Visual Attribution by Learning Importance Ranking

arXiv:2604.05819v2 Announce Type: replace-cross Abstract: Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical...

By David Schinagl, Christian Fruhwirth-Reisinger, Alexander Prutsch, Samuel Schulter, Horst Possegger

llms computer-vision safety

arXiv Machine Learning

Sep 25

A Probabilistic Approach for Model Alignment with Human Comparisons

The paper proposes a two‑stage framework, SL+LHF, that first learns low‑dimensional representations from noisy labeled data and then refines model alignment using human comparison feedback via a probabilistic bisection approach. It introduces the label‑noise‑to‑comparison‑accuracy (LNCA) ratio to theoretically identify when this framework outperforms pure supervised learning, showing that trading labels for comparisons reduces sample complexity when labels are scarce. Experiments on a high‑dimensional crowdfunding prediction task and an Amazon Mechanical Turk study confirm that incorporating human or large language model evaluators improves accuracy under a fixed query budget.

By Junyu Cao, Mohsen Bayati

llms safety

arXiv Machine Learning

Jun 16

Enhancing Visual Feature Attribution via Weighted Integrated Gradients

arXiv:2505. 03201v4 Announce Type: replace-cross Abstract: Integrated Gradients (IG) is a widely used attribution method in explainable AI, particularly in computer vision applications where reliable feature attribution is essential.

By Kien Tran Duc Tuan, Tam Nguyen Trong, Son Nguyen Hoang, Khoat Than, Anh Nguyen Duc

llms computer-vision

arXiv Machine Learning

Jun 26

Staying VIGILant: Mitigating Visual Laziness via Counterfactual Visual Alignment in MLLMs

arXiv:2606. 26387v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) extend large language models (LLMs) with visual perception, enabling joint reasoning over images and text.

By Xi Xiao, Chen Liu, Chih-Ting Liao, Yunbei Zhang, Qizhen Lan, Yuxiang Wei, Lin Zhao, Janet Wang, Jianyang Gu, Muchao Ye, Tianyang Wang, Hao Xu

llms reinforcement-learning multimodal benchmarks safety

arXiv AI

Jun 24

MGI: Member vs Generated Inference

arXiv:2606. 23872v1 Announce Type: cross Abstract: As generative models increasingly produce samples that are indistinguishable from human-created content, it becomes difficult to determine whether a given data point was part of a model's natural training set or was generated by the model itself, especially when models memorize and reproduce training data.

By Bihe Zhao, Michel Meintz, Juangui Xu, Franziska Boenisch, Adam Dziedzic

diffusion

arXiv Computer Vision

Sep 16

Extremely coarse learning objectives induce human-aligned representations in AI vision models

arXiv:2605.05556v2 Announce Type: replace Abstract: Artificial neural networks trained on visual tasks develop internal representations resembling those of the primate visual system, a discovery that...

By Yash Mehta, Michael F. Bonner

llms rag safety

arXiv AI

Jun 30

On the Faithfulness of Post-Hoc Concept Bottleneck Models

arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.

By Laines Schmalwasser, Jan Blunk, Niklas Penzel, Julia Niebling, Joachim Denzler

llms multimodal benchmarks

arXiv Machine Learning

Jul 7

Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification

arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.

By Nicolas Sournac, Ahmed Baha Ben Jmaa, Bertrand Braeckeveldt

benchmarks safety

arXiv AI

Jul 15

Representation and Reference Selection in Training-Free Synthetic Image Attribution

arXiv:2607. 12052v1 Announce Type: cross Abstract: Synthetic image attribution aims at identifying the generator responsible for a given AI-generated image.

By Meiling Li, Pietro Bongini, Benedetta Tondi, Mauro Barni

arXiv Computer Vision

Aug 26

Bridging Adversarial and Collaborative Learning for AI-Generated Image Quality Assessment

arXiv:2608.24372v1 Announce Type: new Abstract: AI-generated image quality assessment (AIGIQA) requires jointly reasoning about perceptual fidelity and prompt alignment, two quality dimensions that a...

By Baoliang Chen, Qing Lin, Sijie Mai

benchmarks safety

arXiv AI

Aug 20

From Inference to Adaptation: A Unified Optimal Transport View of Vision Language Model

arXiv:2608. 18339v1 Announce Type: cross Abstract: Vision-language models (VLMs) have demonstrated remarkable zero-shot capabilities yet remain sensitive to real-world distribution shifts during inference.

By Qi Yu, Zhichen Zeng, Katherine Tieu, Xiyuan Yang, Ruizhong Qiu, Yuchen Yan, Lihui Liu, Yanjun Zhao, Lingjie Chen, Jingrui He, Hanghang Tong

llms rag computer-vision multimodal benchmarks safety

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