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Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques

arXiv:2605.02947v1 Announce Type: new Abstract: This study proposes a novel approach to extract topological properties, specifically the Euler characteristic, from input images using neural networks w

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Prediction horizon shapes representations in predictive learning

DGX agent

arXiv:2511.09290v2 Announce Type: replace Abstract: Predictive learning has emerged as a central paradigm for training models across diverse data domains and is increasingly viewed as a foundation for

researcharxiv-cs-lg

6 May 2026

President Biden made health insurance more affordable for millions of Americans in the ACA marketplace. The Republicans’ actions increased p…

President Biden made health insurance more affordable for millions of Americans in the ACA marketplace. The Republicans’ actions increased prices by 58% on average—amounting to huge extra costs for th

researchyann-lecun--x

Pretrained Model Representations as Acquisition Signals for Active Learning of MLIPs

arXiv:2605.03964v1 Announce Type: new Abstract: Training machine learning interatomic potentials (MLIPs) for reactive chemistry is often bottlenecked by the high cost of quantum chemical labels and th

Proteo-R1: Reasoning Foundation Models for De Novo Protein Design

arXiv:2605.02937v1 Announce Type: new Abstract: Deep learning in de novo protein design has achieved atomic-level fidelity. However, existing models remain largely non-deliberative: they directly synt

Provable Accuracy Collapse in Embedding-Based Representations under Dimensionality Mismatch

arXiv:2605.03346v1 Announce Type: cross Abstract: Embedding-based representations in Euclidean space R^d are a cornerstone of modern machine learning, where a major goal is to use the smallest dimensi

psifx -- Psychological and Social Interactions Feature Extraction Package

arXiv:2407.10266v5 Announce Type: replace Abstract: psifx is a plug-and-play multi-modal feature extraction toolkit, aiming to facilitate and democratize the use of state-of-the-art machine learning t

Quaternion Wavelet-Conditioned Diffusion Models for Image Super-Resolution

arXiv:2505.00334v3 Announce Type: replace Abstract: Image Super-Resolution is a fundamental problem in computer vision with broad applications spacing from medical imaging to satellite analysis. The a

Random test functions, H^{-1} norm equivalence, and stochastic variational physics-informed neural networks

arXiv:2605.03542v1 Announce Type: cross Abstract: The dual norm characterisation of weak solutions of second-order linear elliptic partial differential equations is mathematically natural but computat

Realizable Bayes-Consistency for General Metric Losses

arXiv:2605.03823v1 Announce Type: new Abstract: We study strong universal Bayes-consistency in the realizable setting for learning with general metric losses, extending classical characterizations bey

Reasoning-Guided Grounding: Elevating Video Anomaly Detection through Multimodal Large Language Models

arXiv:2605.02912v1 Announce Type: new Abstract: Video Anomaly Detection (VAD) has traditionally been framed as binary classification or outlier detection, providing neither interpretable reasoning nor

Red states have disproportionately benefited from the health insurance subsidies expanded by Biden and then cut by Trump. Georgia is a clear…

Red states have disproportionately benefited from the health insurance subsidies expanded by Biden and then cut by Trump. Georgia is a clear example: in just one year, marketplace enrollment has dropp

researchyann-lecun--x

Reliable AI Needs to Externalize Implicit Knowledge: A Human-AI Collaboration Perspective

arXiv:2605.02010v1 Announce Type: new Abstract: This position paper argues that reliable AI requires infrastructure for human validation of implicit knowledge. AI learns from both explicit knowledge (

Repurposing and Evaluating the (In)Feasibility of Dataset Poisoning enabled Watermarking for Contrastive Learning

arXiv:2605.01834v1 Announce Type: cross Abstract: Contrastive learning (CL) reduces annotation cost via auto-derived supervisory signals. Since large-scale in-house CL datasets are infeasible, relianc

Reservoir property image slices from the Groningen gas field for image translation and segmentation

arXiv:2605.03942v1 Announce Type: new Abstract: Reservoir characterization workflows increasingly rely on image-based and machine-learning/deep learning or even generative AI approaches, but openly av

Rethinking Explanations: Formalizing Contrast in Description Logics

arXiv:2605.01442v1 Announce Type: new Abstract: There has been a growing interest in explaining entailments over description logic (DL) knowledge bases. The existing explanation formalisms focus on ju

Rethinking Temporal Consistency in Video Object-Centric Learning: From Prediction to Correspondence

arXiv:2605.03650v1 Announce Type: new Abstract: The de facto approach in video object-centric learning maintains temporal consistency through learned dynamics modules that predict future object repres

Revisiting Graph-Tokenizing Large Language Models: A Systematic Evaluation of Graph Token Understanding

arXiv:2605.03514v1 Announce Type: new Abstract: The remarkable success of large language models (LLMs) has motivated researchers to adapt them as universal predictors for various graph tasks. As a wid

RFKjr == Lyssenko

Yann LeCun compares RFK Jr. to Trofim Lysenko, the Soviet biologist whose pseudoscientific theories caused agricultural disaster, suggesting RFK Jr. promotes similarly discredited or anti-scientific i

researchyann-lecun--x

Rose-SQL: Role-State Evolution Guided Structured Reasoning for Multi-Turn Text-to-SQL

arXiv:2605.03720v1 Announce Type: new Abstract: Recent advances in Large Reasoning Models (LRMs) trained with Long Chain-of-Thought have demonstrated remarkable capabilities in code generation and mat

Scaling Laws and Symmetry, Evidence from Neural Force Fields

arXiv:2510.09768v2 Announce Type: replace Abstract: We present an empirical study in the geometric task of learning interatomic potentials, which shows equivariance matters even more at larger scales;

Scoring Edit Impact in Grammatical Error Correction via Embedded Association Graphs

arXiv:2604.06573v2 Announce Type: replace Abstract: A Grammatical Error Correction (GEC) system produces a sequence of edits to correct an erroneous sentence. The quality of these edits is typically e

Semantically Enriching Investor Micro-blogs for Opinion-Aware Emotion Analysis: A Practical Approach

arXiv:2605.03092v1 Announce Type: new Abstract: While sentiment analysis is the staple of financial NLP, capturing the nuances of 'why' behind that sentiment remains a challenge. There have been attem

Sensorless State Estimation and Control for Agile Cable-Suspended Payload Transport by Quadrotors

arXiv:2605.03666v1 Announce Type: new Abstract: This work proposes a novel control and estimation approach for aerial manipulation of a cable-suspended load using Unmanned Aerial Vehicles (UAVs). Comm

researcharxiv-cs-ro

Sentiment Analysis of Indonesian Spotify Reviews Using Machine Learning and BiLSTM

arXiv:2605.03443v1 Announce Type: new Abstract: This paper benchmarks classical machine learning and deep learning approaches for three-class sentiment classification of Indonesian Spotify reviews. Us

Should We Still Pretrain Encoders with Masked Language Modeling?

arXiv:2507.00994v4 Announce Type: replace Abstract: Learning high-quality text representations is fundamental to a wide range of NLP tasks. While encoder pretraining has traditionally relied on Masked

Skeleton-Snippet Contrastive Learning with Multiscale Feature Fusion for Action Localization

arXiv:2512.16504v3 Announce Type: replace Abstract: The self-supervised pretraining paradigm has achieved great success in learning 3D action representations for skeleton-based action recognition usin

Sparse Data Tree Canopy Segmentation: Fine-Tuning Leading Pretrained Models on Only 150 Images

arXiv:2601.10931v2 Announce Type: replace Abstract: Tree canopy detection from aerial imagery is an important task for environmental monitoring, urban planning, and ecosystem analysis. Simulating real

Spatiotemporal Convolutions on EEG signal -- A Representation Learning Perspective on Efficient and Explainable EEG Classification with Convolutional Neural Nets

arXiv:2605.03874v1 Announce Type: new Abstract: Classification of EEG signals using shallow Convolutional Neural Networks (CNNs) is a prevalent and successful approach across a variety of fields. Most

SpecMD: A Comprehensive Study on Speculative Expert Prefetching

researchapple-ml-research

Mixture-of-Experts (MoE) models enable sparse expert activation, meaning that only a subset of the model’s parameters is used during each inference. However, to translate this sparsity into practical

Spectral- and Energy-efficient Multi-BS Multi-RIS Pinching-antenna Systems: A GNN-based Approach

arXiv:2605.01307v1 Announce Type: cross Abstract: This paper investigates coordinated downlink transmission in a multi-base station (multi-BS) multi-reconfigurable intelligent surface (multi-RIS)-assi

Speculative Coupled Decoding for Training-Free Lossless Acceleration of Autoregressive Visual Generation

arXiv:2510.24211v2 Announce Type: replace Abstract: Autoregressive (AR) modeling has recently emerged as a promising new paradigm in visual generation, but its practical adoption is severely constrain

Speculative Speculative Decoding

arXiv:2603.03251v3 Announce Type: replace Abstract: Autoregressive decoding is bottlenecked by its sequential nature. Speculative decoding has become a standard way to accelerate inference by using a

SPRINT: Robust Model Attribution of Generated Images via Secret Pixel Reconstruction

arXiv:2508.05691v3 Announce Type: replace-cross Abstract: Detecting the source model of AI-generated images is a growing accountability problem. AI fingerprinting techniques address this by detecting

Static Analysis of Recursive SHACL

arXiv:2605.02787v1 Announce Type: cross Abstract: SHACL (Shapes Constraint Language) expresses constraints on RDF data by means of so-called shapes. Its central service is validation: verifying whethe

Steer Like the LLM: Activation Steering that Mimics Prompting

arXiv:2605.03907v1 Announce Type: new Abstract: Large language models can be steered at inference time through prompting or activation interventions, but activation steering methods often underperform

Stochastic Schrodinger Diffusion Models for Pure-State Ensemble Generation

arXiv:2605.03573v1 Announce Type: cross Abstract: In quantum machine learning (QML), classical data are often encoded as quantum pure states and processed directly as quantum representations, motivati

StreakMind: AI detection and analysis of satellite streaks in astronomical images with automated database integration

arXiv:2605.03429v1 Announce Type: cross Abstract: Artificial satellites and space debris increasingly contaminate astronomical images, affecting scientific surveys and producing large volumes of strea

Structural Ranking of the Cognitive Plausibility of Computational Models of Analogy and Metaphors with the Minimal Cognitive Grid

arXiv:2605.01359v1 Announce Type: new Abstract: In this paper, we employ the Minimal Cognitive Grid (MCG), a framework created to evaluate the cognitive plausibility of artificial systems, to offer a

Super-fast Rates of Convergence for Neural Network Classifiers under the Hard Margin Condition

arXiv:2505.08262v2 Announce Type: replace Abstract: We study the classical binary classification problem for hypothesis spaces of Deep Neural Networks (DNNs) under Tsybakov's low-noise condition with

Taking the GP Out of the Loop

arXiv:2506.12818v3 Announce Type: replace Abstract: Bayesian optimization (BO) has traditionally solved black-box problems where function evaluation is expensive and, therefore, observations are few.

Task Vector Geometry Underlies Dual Modes of Task Inference in Transformers

arXiv:2605.03780v1 Announce Type: cross Abstract: Transformers are effective at inferring the latent task from context via two inference modes: recognizing a task seen during training, and adapting to

Tempered Guided Diffusion

arXiv:2605.03712v1 Announce Type: cross Abstract: Training-free conditional diffusion provides a flexible alternative to task-specific conditional model training, but existing samplers often allocate

The Coding Limits of Robust Watermarking for Generative Models

arXiv:2509.10577v3 Announce Type: replace-cross Abstract: We study a basic question about cryptographic watermarking for generative models: how reliable can a watermark remain when an adversary is all

The Counterexample Game: Iterated Conceptual Analysis and Repair in Language Models

arXiv:2605.03936v1 Announce Type: new Abstract: Conceptual analysis -- proposing definitions and refining them through counterexamples -- is central to philosophical methodology. We study whether lang

The Download: seafloor science and military chatbots

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Inexpensive seafloor-hopping submersibles could stoke deep-sea

researchmit-tech-review

The Enemy from Within: A Study of Political Delegitimization Discourse in Israeli Political Speech

arXiv:2508.15524v3 Announce Type: replace Abstract: We present the first large-scale computational study of political delegitimization discourse (PDD), defined as symbolic attacks on the normative val

There are only two honest metrics when it comes to benchmarking intelligence: novelty and efficiency. You don't need intelligence to solve a…

researchfrancois-chollet--x

There are only two honest metrics when it comes to benchmarking intelligence: novelty and efficiency. You don't need intelligence to solve a known problem (only memory). And you don't need intelligenc