The first public dataset of 10,217 GPT-Image-2 generated images sourced from Twitter in the week after release, with CLIP taxonomy, OCR, face detection, clustering analyses, and a finding that C2PA provenance data is stripped on upload.
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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
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UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique for machine learning.
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- abstract UMAP (Uniform Manifold Approximation and Projection) is a novel manifold learning technique for dimension reduction. UMAP is constructed from a theoretical framework based in Riemannian geometry and algebraic topology. The result is a practical scalable algorithm that applies to real world data. The UMAP algorithm is competitive with t-SNE for visualization quality, and arguably preserves more of the global structure with superior run time performance. Furthermore, UMAP has no computational restrictions on embedding dimension, making it viable as a general purpose dimension reduction technique
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representative citing papers
t-SNE converges in the large-data limit to a non-convex variational energy with attraction and repulsion terms that admits a unique smooth minimizer but infinitely many discontinuous ones in one dimension.
Adversarial smuggling attacks encode harmful content into human-readable visuals that evade MLLM detection, achieving over 90% attack success rates on models like GPT-5 and Qwen3-VL via the new SmuggleBench benchmark.
FPR manipulation attack perturbs benign MQTT packets to flip labels to attacks in NIDS with 80-100% success, increasing SOC delays without gradient-based methods.
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
Language models can automatically generate high-quality evaluation datasets that reveal new cases of inverse scaling, sycophancy, and concerning goal-seeking behaviors, including some worsened by RLHF.
The paper introduces #PraCegoVer, the first large-scale image captioning dataset in Portuguese sourced from Instagram posts with single user-generated captions per image.
An exact algebraic identity plus low-rank SVD and Haar-measure null-space approximation reduce per-point mean curvature cost from O(m^4) to O(k^2 m + k m p^2) with 50-300x speedups and negligible accuracy loss.
FiSeR uses coarse contrastive separation of natural vs synthetic images plus fine contrastive grouping by generator identity to improve cross-domain AUROC by +10.22 over DIRE baseline on multiple test sets.
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
MAPS provides 2618 validated 3D meshes and a controllable rendering pipeline to attribute vision model recognition failures to specific scene parameters, finding camera distance and elevation as the dominant failure factors across 20 tested models.
A new linked multimodal dataset of Russian domestic and foreign policy speeches with texts, images, captions, harmonized metadata, and expert-refined topic annotations is introduced to support analyses in political communication and LLM applications.
Introduces replay-based continual learning with sequential invariance alignment to learn domain-invariant representations, outperforming baselines on generalization to unseen domains across six datasets in vision, medicine, manufacturing, and ecology.
A CNN with attention and shared latent space recovers SFHs and metallicities from spectro-photometric data with ~0.12 dex age and ~0.03 dex metallicity dispersion while running thousands of times faster than full spectral fitting.
Preference fine-tuning outperforms prompting for personalisation but amplifies sycophancy and relationship-seeking, while simulated users recover aggregate rankings yet show far lower self-consistency and different topic and position biases than real humans.
Spectral Gradient Surgery disentangles class-discriminative and domain-specific signals in distribution-matching distilled datasets by analyzing gradient agreement in the spectral domain, yielding better out-of-distribution performance.
scShapeBench supplies synthetic and real annotated single-cell datasets across four shape categories, with scReebTower outperforming PAGA and Mapper on topology-aware metrics.
Hybrid human-AI networks in 5x5 grids reached lower final polarization than human-only networks after eight rounds of opinion revision on polarizing topics.
A new orthogonal projection module for video anomaly detection suppresses facial attributes via weak face-presence signals and cosine alignment while preserving anomaly-relevant features like pose and motion.
eX2L improves robustness to distribution shifts by penalizing similarity between Grad-CAM maps of a label classifier and a confounder classifier, reaching new SOTA average and worst-group accuracy on the Spawrious benchmark.
PROBE recasts MLIP uncertainty quantification as selective classification by training a compact discriminative classifier on frozen per-atom backbone embeddings, yielding a reliability probability that tracks actual error better than ensemble disagreement.
Sparse autoencoders on ViT class tokens reveal stable Class Activation Profiles for in-distribution data, enabling OOD detection via divergence from core energy profiles.
Cross-cultural survey of 4,641 participants shows LLM emotional support adoption varies widely by country and demographics, with socioeconomic status as strongest predictor of trust and use, and English-speaking nations more accepting than others in Europe.
Moltbook operates as two largely separate layers: a dominant transactional token economy using protocols like MBC-20 and a thinner discursive conversation layer with only 3.6% agent overlap.
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GPT-Image-2 in the Wild: A Twitter Dataset of Self-Reported AI-Generated Images from the First Week of Deployment
The first public dataset of 10,217 GPT-Image-2 generated images sourced from Twitter in the week after release, with CLIP taxonomy, OCR, face detection, clustering analyses, and a finding that C2PA provenance data is stripped on upload.
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t-SNE converges in the large-data limit to a non-convex variational energy with attraction and repulsion terms that admits a unique smooth minimizer but infinitely many discontinuous ones in one dimension.
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Making MLLMs Blind: Adversarial Smuggling Attacks in MLLM Content Moderation
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Uncovering and Understanding FPR Manipulation Attack in Industrial IoT Networks
FPR manipulation attack perturbs benign MQTT packets to flip labels to attacks in NIDS with 80-100% success, increasing SOC delays without gradient-based methods.
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Promptbreeder: Self-Referential Self-Improvement Via Prompt Evolution
Promptbreeder evolves both task prompts and the mutation prompts that improve them using LLMs, outperforming Chain-of-Thought and Plan-and-Solve on arithmetic and commonsense reasoning benchmarks.
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Discovering Language Model Behaviors with Model-Written Evaluations
Language models can automatically generate high-quality evaluation datasets that reveal new cases of inverse scaling, sycophancy, and concerning goal-seeking behaviors, including some worsened by RLHF.
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#PraCegoVer: A Large Dataset for Image Captioning in Portuguese
The paper introduces #PraCegoVer, the first large-scale image captioning dataset in Portuguese sourced from Instagram posts with single user-generated captions per image.
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Efficient Mean Curvature Computation on High-Dimensional Data Manifolds
An exact algebraic identity plus low-rank SVD and Haar-measure null-space approximation reduce per-point mean curvature cost from O(m^4) to O(k^2 m + k m p^2) with 50-300x speedups and negligible accuracy loss.
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FiSeR: Fine-Grained Source Representations for Cross-Domain AI Image Detection
FiSeR uses coarse contrastive separation of natural vs synthetic images plus fine contrastive grouping by generator identity to improve cross-domain AUROC by +10.22 over DIRE baseline on multiple test sets.
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Contrastive self-supervised convolutional autoencoder for core-collapse supernova gravitational-wave detection
A contrastive self-supervised convolutional autoencoder detects core-collapse supernova gravitational waves with performance comparable to supervised CNNs, better generalization to unseen waveforms, and ~120 kpc sensitive distance under Einstein Telescope noise.
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MAPS: A Synthetic Dataset for Probing Vision Models in a Controlled 3D Scene Space
MAPS provides 2618 validated 3D meshes and a controllable rendering pipeline to attribute vision model recognition failures to specific scene parameters, finding camera distance and elevation as the dominant failure factors across 20 tested models.
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Linked Multi-Model Data on Russian Domestic and Foreign Policy Speeches
A new linked multimodal dataset of Russian domestic and foreign policy speeches with texts, images, captions, harmonized metadata, and expert-refined topic annotations is introduced to support analyses in political communication and LLM applications.
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Continual Learning of Domain-Invariant Representations
Introduces replay-based continual learning with sequential invariance alignment to learn domain-invariant representations, outperforming baselines on generalization to unseen domains across six datasets in vision, medicine, manufacturing, and ecology.
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Determining star formation histories and age-metallicity relations with convolutional neural networks
A CNN with attention and shared latent space recovers SFHs and metallicities from spectro-photometric data with ~0.12 dex age and ~0.03 dex metallicity dispersion while running thousands of times faster than full spectral fitting.
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PRISM-X: Experiments on Personalised Fine-Tuning with Human and Simulated Users
Preference fine-tuning outperforms prompting for personalisation but amplifies sycophancy and relationship-seeking, while simulated users recover aggregate rankings yet show far lower self-consistency and different topic and position biases than real humans.
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Spectral Gradient Surgery for Domain-Generalizable Dataset Distillation
Spectral Gradient Surgery disentangles class-discriminative and domain-specific signals in distribution-matching distilled datasets by analyzing gradient agreement in the spectral domain, yielding better out-of-distribution performance.
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scShapeBench: Discovering geometry from high dimensional scRNAseq data
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An Experimental Method to Study Opinion Diffusion in Human-AI Hybrid Societies
Hybrid human-AI networks in 5x5 grids reached lower final polarization than human-only networks after eight rounds of opinion revision on polarizing topics.
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Privacy-Aware Video Anomaly Detection through Orthogonal Subspace Projection
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eXplaining to Learn (eX2L): Regularization Using Contrastive Visual Explanation Pairs for Distribution Shifts
eX2L improves robustness to distribution shifts by penalizing similarity between Grad-CAM maps of a label classifier and a confounder classifier, reaching new SOTA average and worst-group accuracy on the Spawrious benchmark.
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Knowing when to trust machine-learned interatomic potentials
PROBE recasts MLIP uncertainty quantification as selective classification by training a compact discriminative classifier on frozen per-atom backbone embeddings, yielding a reliability probability that tracks actual error better than ensemble disagreement.
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Sparsity as a Key: Unlocking New Insights from Latent Structures for Out-of-Distribution Detection
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From Chatbots to Confidants: A Cross-Cultural Study of LLM Adoption for Emotional Support
Cross-cultural survey of 4,641 participants shows LLM emotional support adoption varies widely by country and demographics, with socioeconomic status as strongest predictor of trust and use, and English-speaking nations more accepting than others in Europe.
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The Platform Is Mostly Not a Platform: Token Economies and Agent Discourse on Moltbook
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Comparison Drives Preference: Reference-Aware Modeling for AI-Generated Video Quality Assessment
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Physics-informed, Generative Adversarial Design of Funicular Shells
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Computational Lesions in Multilingual Language Models Separate Shared and Language-specific Brain Alignment
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L-fuzzy simplicial homology
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Emotion Concepts and their Function in a Large Language Model
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Dynamic Context Evolution for Scalable Synthetic Data Generation
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A Large-Scale Comparative Analysis of Imputation Methods for Single-Cell RNA Sequencing Data
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D-MODD: A Diffusion Model of Opinion Dynamics Derived from Online Data
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Language-Conditioned Safe Trajectory Generation for Spacecraft Rendezvous
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Evalet: Evaluating Large Language Models through Functional Fragmentation
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Sycophancy to Subterfuge: Investigating Reward-Tampering in Large Language Models
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Geodesic Learning via Unsupervised Decision Forests
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Bridging Phase-Field Model and Deep Learning for Predicting 2D and 3D Microstructure Evolution in Ternary Alloys
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When One Point Is Not Enough: Addressing Ambiguous Instances in Dimensionality Reduction by Splitting
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Interpretable Computer Vision for Defect Detection in X-ray Tomography of Aerospace SiC/SiC Composites
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Beyond Action Residuals: Real-World Robot Policy Steering via Bottleneck Latent Reinforcement Learning
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Going PLACES: Participatory Localized Red Teaming for Text-to-Image Safety in the Global South
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