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Cultural Benchmarking of LLMs in Standard and Dialectal Arabic Dialogues

arXiv:2605.00119v1 Announce Type: new Abstract: There is a significant gap in evaluating cultural reasoning in LLMs using conversational datasets that capture culturally rich and dialectal contexts. M

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[D] What Happened to Neurips Creative AI Track? [R]

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The NeurIPS Creative AI track became part of the main conference proceedings for 2025, with papers presented as posters during the conference , marking a change from 2024 when the track was not part o

researchr-machinelearning

4 May 2026

Decouple before Integration: Test-time Synthesis of SFT and RLVR Task Vectors

arXiv:2605.00610v1 Announce Type: new Abstract: SFT and RLVR represent two fundamental yet distinct paradigms for LLM post-training, each excelling in distinct dimensions. SFT expands knowledge breadt

Deep Kernel Learning for Stratifying Glaucoma Trajectories

arXiv:2605.00708v1 Announce Type: new Abstract: Effectively stratifying patient risk in chronic diseases like glaucoma is a major clinical challenge. Clinicians need tools to identify patients at high

DiffMI: Breaking Face Recognition Privacy via Diffusion-Driven Training-Free Model Inversion

arXiv:2504.18015v4 Announce Type: replace-cross Abstract: Face recognition poses serious privacy risks due to its reliance on sensitive and immutable biometric data. While modern systems mitigate priv

Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance

arXiv:2507.18654v2 Announce Type: replace-cross Abstract: Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured

Directed Social Regard: Surfacing Targeted Advocacy, Opposition, Aid, Harms, and Victimization in Online Media

arXiv:2605.00776v1 Announce Type: new Abstract: The language in online platforms, influence operations, and political rhetoric frequently directs a mix of pro-social sentiment (e.g., advocacy, helpful

Discount Model Search for Quality Diversity Optimization in High-Dimensional Measure Spaces

arXiv:2601.01082v5 Announce Type: replace Abstract: Quality diversity (QD) optimization searches for a collection of solutions that optimize an objective while attaining diverse outputs of a user-spec

Discrete Cosine Transform Based Decorrelated Attention for Vision Transformers

arXiv:2405.13901v4 Announce Type: replace Abstract: Self-attention is central to the success of Transformer architectures; however, learning the query, key, and value projections from random initializ

Distance-Aware Error for Spline Networks: A Bottom-Up Approach to Uncertainty

arXiv:2501.04757v2 Announce Type: replace-cross Abstract: We develop a new class of distance-aware error bounds that tightly characterize the approximation error of spline neural networks. Our bottom-

Distance metric learning for conditional anomaly detection

arXiv:2605.00490v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying unusual or interesting patterns in data. A recently proposed conditional anomaly detection f

Diversity in Large Language Models under Supervised Fine-Tuning

arXiv:2605.00195v1 Announce Type: new Abstract: Supervised Fine-Tuning (SFT) is essential for aligning Large Language Models (LLMs) with user intent, yet it is believed to suppress generative diversit

DMDSC: A Dynamic-Margin Deep Simplex Classifier for Open-Set Recognition on Medical Image Datasets

arXiv:2605.00675v1 Announce Type: new Abstract: Medical imaging datasets are often characterized by extreme class imbalances, where rare pathologies are significantly underrepresented compared to comm

EASE: Federated Multimodal Unlearning via Entanglement-Aware Anchor Closure

arXiv:2605.00733v1 Announce Type: cross Abstract: Federated Multimodal Learning (FML) trains multimodal models across decentralized clients while keeping their image-text pairs private. However, joint

Efficient Finite Initialization with Partial Norms for Tensorized Neural Networks and Tensor Networks Algorithms

arXiv:2309.06577v5 Announce Type: replace Abstract: We present two algorithms to initialize layers of tensorized neural networks and general tensor network algorithms using partial computations of the

Elimination Templates in Macaulay2

arXiv:2605.00278v1 Announce Type: cross Abstract: We introduce the package exttt{EliminationTemplates} for the Macaulay2 computer algebra system, which provides tools for constructing automatic solver

Embodied Interpretability: Linking Causal Understanding to Generalization in Vision-Language-Action Models

arXiv:2605.00321v1 Announce Type: new Abstract: Vision-Language-Action (VLA) policies often fail under distribution shift, suggesting that decisions may depend on spurious visual correlations rather t

researcharxiv-cs-ro

End-to-End Autoregressive Image Generation with 1D Semantic Tokenizer

arXiv:2605.00503v1 Announce Type: new Abstract: Autoregressive image modeling relies on visual tokenizers to compress images into compact latent representations. We design an end-to-end training pipel

Escaping Mode Collapse in LLM Generation via Geometric Regulation

arXiv:2605.00435v1 Announce Type: new Abstract: Mode collapse is a persistent challenge in generative modeling and appears in autoregressive text generation as behaviors ranging from explicit looping

Even our toughest critics come around eventually

Nous Research likely discusses how their AI models or research have gained acceptance even among skeptical observers, suggesting that rigorous development and demonstrated capabilities eventually conv

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💜 🎓 Excited to welcome @ylecun as our 2026 Tandon Commencement speaker! #NYUTandonMade

Yann LeCun has been announced as the 2026 commencement speaker for NYU Tandon School of Engineering. LeCun, a prominent computer scientist and AI researcher, will deliver the keynote address at the gr

researchyann-lecun--x

Exploring the Limits of End-to-End Feature-Affinity Propagation for Single-Point Supervised Infrared Small Target Detection

arXiv:2605.00722v1 Announce Type: new Abstract: Single-point supervised infrared small target detection (IRSTD) drastically reduces dense annotation costs. Current state-of-the-art (SOTA) methods achi

Federated Learning with Hypergradient-based Online Update of Aggregation Weights

arXiv:2605.00458v1 Announce Type: new Abstract: Federated learning using mobile and Internet of Things devices requires not only the ability to handle heterogeneity of clients' data distributions but

Federated Weather Modeling on Sensor Data

arXiv:2605.00322v1 Announce Type: new Abstract: Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including gr

Feedback Lunch: Learned Feedback Codes for Secure Communications

arXiv:2510.16620v3 Announce Type: replace-cross Abstract: We consider reversely-degraded secure-communication channels, for which the secrecy capacity is zero if there is no channel feedback. Specific

Flow matching for Sentinel-2 super-resolution: implementation, application, and implications

arXiv:2605.00367v1 Announce Type: new Abstract: Developing robust techniques for super-resolution of satellite imagery involves navigating commonly observed trade-offs between spectral fidelity and pe

Free Energy Surface Sampling via Reduced Flow Matching

arXiv:2605.00337v1 Announce Type: new Abstract: Sampling the free energy surface, namely, the distribution of collective variables (CVs), is a crucial problem in statistical physics, as it underpins a

From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models

arXiv:2605.00474v1 Announce Type: new Abstract: Modern vision models achieve remarkable accuracy, but explaining where evidence arises, what the model encodes, and how internal computations assemble t

GAFSV-Net: A Vision Framework for Online Signature Verification

arXiv:2605.00120v1 Announce Type: new Abstract: Online signature verification (OSV) requires distinguishing skilled forgeries from genuine samples under high intra-class variability and with very few

GD4: Graph-based Discrete Denoising Diffusion for MIMO Detection

arXiv:2605.00423v1 Announce Type: new Abstract: In wireless communications, recovering the optimal solution to the multiple-input multiple-output (MIMO) detection problem is NP-hard. Obtaining high-qu

Generating Statistical Charts with Validation-Driven LLM Workflows

arXiv:2605.00800v1 Announce Type: new Abstract: Generating diverse, readable statistical charts from tabular data remains challenging for LLMs, as many failures become apparent after rendering and are

Generative Modeling under Non-Monotone MAR Missingness via Approximate Wasserstein Gradient Flows

arXiv:2604.04567v2 Announce Type: replace-cross Abstract: The prevalence of missing values in data science poses a substantial risk to any further analyses. Despite a wealth of research, principled no

GMGaze: MoE-Based Context-Aware Gaze Estimation with CLIP and Multiscale Transformer

arXiv:2605.00799v1 Announce Type: new Abstract: Gaze estimation methods commonly use facial appearances to predict the direction of a person gaze. However, previous studies show three major challenges

Gradient Regularized Newton Boosting Trees with Global Convergence

arXiv:2605.00581v1 Announce Type: cross Abstract: Gradient Boosting Decision Trees (GBDTs) dominate tabular machine learning, with modern implementations like XGBoost, LightGBM, and CatBoost being bas

Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey

arXiv:2411.17429v2 Announce Type: replace Abstract: Graph Neural Networks are powerful models for learning from graph-structured data, yet their effectiveness is often limited by two critical challeng

H-RAG at SemEval-2026 Task 8: Hierarchical Parent-Child Retrieval for Multi-Turn RAG Conversations

arXiv:2605.00631v1 Announce Type: new Abstract: We present H-RAG, our submission to SemEval-2026 Task 8 (MTRAGEval), addressing both Task A (Retrieval) and Task C (Generation with Retrieved Passages).

High-Speed Vision Improves Zero-Shot Semantic Understanding of Human Actions

arXiv:2605.00496v1 Announce Type: new Abstract: Understanding human actions from visual observations is essential for human--robot interaction, particularly when semantic interpretation of unfamiliar

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This link directs to Nous Research's subscription management portal where users can view, modify, or cancel their subscription settings and billing information. The associated X post likely announces

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Human-in-the-Loop Meta Bayesian Optimization for Fusion Energy and Scientific Applications

arXiv:2605.00068v1 Announce Type: new Abstract: Inertial Confinement Fusion (ICF) holds transformative promise for sustainable, near-limitless clean energy, yet remains constrained by prohibitively hi

Image Score: Learning and Evaluating Human Preferences for Mercari Search

arXiv:2408.11349v2 Announce Type: replace Abstract: Mercari is the largest C2C e-commerce marketplace in Japan, having more than 20 million active monthly users. Search being the fundamental way to di

Information-geometric adaptive sampling for graph diffusion

arXiv:2605.00250v1 Announce Type: cross Abstract: Standard diffusion models for graph generation typically rely on uniform time-stepping, an approach that overlooks the non-homogeneous dynamics of dis

Information-Theoretic Generalization Bounds for Stochastic Gradient Descent with Predictable Virtual Noise

arXiv:2605.00064v1 Announce Type: new Abstract: Information-theoretic generalization bounds analyze stochastic optimization by relating expected generalization error to the mutual information between

Instance-Aware Pseudo-Labeling and Class-Focused Contrastive Learning for Weakly Supervised Domain Adaptive Segmentation of Electron Microscopy

arXiv:2510.16450v2 Announce Type: replace Abstract: Annotation-efficient segmentation of the numerous mitochondria instances from various electron microscopy (EM) images is highly valuable for biologi

Intrinsic Gradient Suppression for Label-Noise Prompt Tuning in Vision-Language Models

arXiv:2605.00591v1 Announce Type: new Abstract: Contrastive vision-language models like CLIP exhibit remarkable zero-shot generalization. However, prompt tuning remains highly sensitive to label noise

Is Textual Similarity Invariant under Machine Translation? Evidence Based on the Political Manifesto Corpus

arXiv:2605.00618v1 Announce Type: new Abstract: We investigate the extent to which cosine similarity between paragraph embeddings is invariant under machine translation, using the Manifesto Corpus of

It's Never Too Late: Noise Optimization for Collapse Recovery in Trained Diffusion Models

arXiv:2601.00090v2 Announce Type: replace Abstract: Contemporary text-to-image models exhibit a surprising degree of mode collapse, as can be seen when sampling several images given the same text prom

Knowing when to trust machine-learned interatomic potentials

arXiv:2605.00640v1 Announce Type: new Abstract: Prevailing machine-learned interatomic potential (MLIP) uncertainty-quantification methods rely on ensembles of independently trained backbones. These m

Koopman-Assisted Reinforcement Learning

arXiv:2403.02290v2 Announce Type: replace-cross Abstract: The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman equation, are ubiquitous in reinforcement learning and control theor