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ELF: Embedded Language Flows

arXiv:2605.10938v1 Announce Type: cross Abstract: Diffusion and flow-based models have become the de facto approaches for generating continuous data, e.g., in domains such as images and videos. Their

Elucidating Representation Degradation Problem in Diffusion Model Training

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arXiv:2605.10790v1 Announce Type: new Abstract: Diffusion models have achieved remarkable success, yet their training remains inefficient due to a severe optimization bottleneck, which we term Represe

researcharxiv-cs-lg

12 May 2026

Embedding Dimension Lower Bounds for Universality of Deep Sets and Janossy Pooling

arXiv:2605.08377v1 Announce Type: new Abstract: In many practical applications it is important to build symmetries into neural network architectures. Consider the important case of permutation symmetr

Embeddings for Preferences, Not Semantics

arXiv:2605.08360v1 Announce Type: new Abstract: Modern AI is opening the door to collective decision-making in which participants express their views as free-form text rather than voting on a fixed se

EMFormer: Efficient Multi-Scale Transformer for Accumulative Context Weather Forecasting

arXiv:2602.01194v2 Announce Type: replace Abstract: Long-term weather forecasting is critical for socioeconomic planning and disaster preparedness. While recent approaches employ finetuning to extend

EMO: Pretraining Mixture of Experts for Emergent Modularity

arXiv:2605.06663v2 Announce Type: replace Abstract: Large language models are typically deployed as monolithic systems, requiring the full model even when applications need only a narrow subset of cap

Empirical Bayes 1-bit matrix completion

arXiv:2605.09509v1 Announce Type: cross Abstract: The problem of predicting unobserved entries in a binary matrix, known as 1-bit matrix completion, has found diverse applications in fields such as re

Empty SPACE: Cross-Attention Sparsity for Concept Erasure in Diffusion Models

arXiv:2605.10198v1 Announce Type: cross Abstract: Erasing specific concepts from text-to-image diffusion models is essential for avoiding the generation of copyrighted and explicit content. Closed-for

Enabling Structure-Only Initialization and Out-of-Distribution Generalization in GNN-based Molecular Dynamics Simulators

arXiv:2605.09495v1 Announce Type: cross Abstract: Machine learning-based simulators offer the potential to model the dynamics of complex systems more efficiently than classical approaches, while retai

Energy-based models for diagnostic reconstruction and analysis in a laboratory plasma device

arXiv:2605.08645v1 Announce Type: cross Abstract: Energy-based models (EBMs) provide a powerful and flexible way of learning a joint probability distribution over data by constructing an energy surfac

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach

arXiv:2605.08910v1 Announce Type: cross Abstract: The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detectio

Enhancing Few-Shot Out-of-Distribution Detection via the Refinement of Foreground and Background

arXiv:2601.15065v2 Announce Type: replace Abstract: CLIP-based foreground-background (FG-BG) decomposition methods have demonstrated remarkable effectiveness in improving few-shot out-of-distribution

Entropy-informed Decoding: Adaptive Information-Driven Branching

arXiv:2605.09745v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable generative performance, yet their output quality is dependent on the decoding strategy. While sampling

Estimating Heterogeneous Causal Effect on Networks via Orthogonal Learning

arXiv:2509.18484v2 Announce Type: replace-cross Abstract: Estimating causal effects on networks is challenging because treatments may affect both treated units and their neighbors, while network homop

Evading Visual Aphasia: Contrastive Adaptive Semantic Token Pruning for Vision-Language Models

arXiv:2605.09429v1 Announce Type: cross Abstract: Are low-attention visual tokens truly redundant in vision-language reasoning? Existing pruning methods often assume so, ranking visual tokens by shall

Evaluating Developmental Cognition Capabilities of LLMs

arXiv:2605.08549v1 Announce Type: new Abstract: Conversational AI is increasingly personalized around users' preferences, histories, goals, and knowledge, but much less around how users interpret and

Evaluating Pragmatic Reasoning in Large Language Models: Evidence from Scalar Diversity

arXiv:2605.09042v1 Announce Type: new Abstract: Evaluating pragmatic reasoning in large language models (LLMs) remains challenging because model behavior can vary depending on evaluation methods. Prev

Every finite group admits a just finite presentation

arXiv:2605.10402v1 Announce Type: cross Abstract: A finite presentation of a finite group is called `just finite' if removing any relation from R results in a presentation for an infinite group. It ha

Evidence-based Decision Modeling for Synthetic Face Detection with Uncertainty-driven Active Learning

arXiv:2605.09935v1 Announce Type: new Abstract: With the rapid development of deep generative models, forged facial images are massively exploited for illegal activities. Although existing synthetic f

Evolving Knowledge Distillation for Lightweight Neural Machine Translation

arXiv:2605.09924v1 Announce Type: new Abstract: Recent advancements in Neural Machine Translation (NMT) have significantly improved translation quality. However, the increasing size and complexity of

Evolving-RL: End-to-End Optimization of Experience-Driven Self-Evolving Capability within Agents

arXiv:2605.10663v1 Announce Type: new Abstract: Experience-driven self-evolving agents aim to overcome the static nature of large language models by distilling reusable experience from past interactio

Exact Fixed-Point Constraints in Neural-ODEs with Provable Universality

arXiv:2605.10613v1 Announce Type: cross Abstract: We introduce a technique that enables Neural-ODEs to approximate arbitrary velocity fields with a priori planted fixed-points. Specifically, a recipe

Exact Unlearning from Proxies Induces Closeness Guarantees on Approximate Unlearning

arXiv:2605.10680v1 Announce Type: new Abstract: This paper proposes a paradigm shift linking machine unlearning directly to the structure of the data distributions rather than a mere update of the neu

Explainable Knowledge Tracing via Probabilistic Embeddings and Pattern-based Reasoning

arXiv:2605.09369v1 Announce Type: new Abstract: Knowledge Tracing (KT) models students' knowledge states based on learning interactions to predict performance. While deep learning-based KT models have

Explaining Graph Neural Networks for Node Similarity on Graphs

arXiv:2407.07639v2 Announce Type: replace-cross Abstract: Similarity search is a fundamental task for exploiting information in various applications dealing with graph data, such as citation networks

Explicit and Effectively Symmetric Schemes for Neural SDEs on Lie Groups

arXiv:2509.20599v2 Announce Type: replace Abstract: Backpropagation through (neural) SDE solvers is traditionally approached in two ways: discretise-then-optimise, which offers accurate gradients but

Explicit Bounds on the Hausdorff Distance for Truncated mRPI Sets via Norm-Dependent Contraction Rates

arXiv:2511.18374v2 Announce Type: replace Abstract: We derive a computable closed-form upper bound on the Hausdorff distance between a truncated minimal robust positively invariant (mRPI) set and its

researcharxiv-cs-ro

Exploring 6D Object Pose Estimation with Deformation

arXiv:2604.06720v2 Announce Type: replace Abstract: We present DeSOPE, a large-scale dataset for 6DoF deformed objects. Most 6D object pose methods assume rigid or articulated objects, an assumption t

Exploring and Exploiting Stability in Latent Flow Matching

arXiv:2605.08398v1 Announce Type: cross Abstract: In this work, we show that Latent Flow-Matching (LFM) models are robust to different types of perturbations, including data reduction and model capaci

Extending Confidence-Based Text2Cypher with Grammar and Schema Aware Filtering

arXiv:2605.10318v1 Announce Type: new Abstract: Large language models (LLMs) allow users to query databases using natural language by translating questions into executable queries. Despite strong prog

ExtraVAR: Stage-Aware RoPE Remapping for Resolution Extrapolation in Visual Autoregressive Models

arXiv:2605.10045v1 Announce Type: new Abstract: Visual Autoregressive (VAR) models have emerged as a strong alternative to diffusion for image synthesis, yet their fixed training resolution prevents d

Factual recall in linear associative memories: sharp asymptotics and mechanistic insights

arXiv:2605.10795v1 Announce Type: cross Abstract: Large language models demonstrate remarkable ability in factual recall, yet the fundamental limits of storing and retrieving input--output association

Fast-MIA: Efficient and Scalable Membership Inference for LLMs

arXiv:2510.23074v2 Announce Type: replace-cross Abstract: We propose Fast-MIA (https://github.com/Nikkei/fast-mia), a Python library for efficiently evaluating membership inference attacks (MIA) again

Fast Training of Mixture-of-Experts for Time Series Forecasting via Expert Loss Integration

arXiv:2605.10330v1 Announce Type: cross Abstract: We propose a novel adaptive Mixture-of-Experts (MoE) framework for time series forecasting that enhances expert specialization by incorporating expert

FedAdaVR: Adaptive Variance Reduction for Robust Federated Learning under Limited Client Participation

arXiv:2601.22204v2 Announce Type: replace Abstract: Federated learning (FL) encounters substantial challenges due to heterogeneity, leading to gradient noise, client drift, and partial client particip

FedCIGAR: A Personalized Reconstruction Approach for Federated Graph-level Anomaly Detection

arXiv:2605.09428v1 Announce Type: new Abstract: Graph-level anomaly detection (GLAD) is crucial for ensuring the reliability of graph-driven applications by identifying abnormal graphs that deviate fr

Federated Concept-Based Models: Interpretable models with distributed supervision

arXiv:2602.04093v2 Announce Type: replace Abstract: Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept ann

FERA: Uncertainty-Aware Federated Reasoning for Large Language Models

arXiv:2605.10082v1 Announce Type: new Abstract: Large language models (LLMs) exhibit strong reasoning capabilities when guided by high-quality demonstrations, yet such data is often distributed across

Fetal Brain Imaging: A Composite Neural Network Approach for Keyframe Detection in Ultrasound Videos

arXiv:2605.09750v1 Announce Type: new Abstract: This article presents a novel approach to keyframe detection in ultrasound videos, with a particular focus on fetal brain imaging. The proposed model is

Field-Localized Forgery Detection for Digital Identity Documents

arXiv:2605.09089v1 Announce Type: cross Abstract: Digital identity verification systems used in remote onboarding rely on document images to authenticate users, making them vulnerable to localized man

Finer is Better (with the Right Scaling)

arXiv:2605.08565v1 Announce Type: new Abstract: Microscaling is a critical technique for preserving the quality of Large Language Models (LLMs) quantized to ultra-low precision formats. Intuitively, f

FinMoji: A Framework for Emoji-driven Sentiment Analysis in Financial Social Media

arXiv:2605.09469v1 Announce Type: new Abstract: This paper explores the use of emojis in financial sentiment analysis, focusing on the social media platform StockTwits. Emojis, increasingly prevalent

Fitting Is Not Enough: Smoothness in Extremely Quantized LLMs

arXiv:2605.08894v1 Announce Type: cross Abstract: Large language models (LLMs) achieve strong performance but incur high deployment costs, motivating extremely low-bit but lossy quantization. Existing

Fixed-Point Neural Optimal Transport without Implicit Differentiation

arXiv:2605.10792v1 Announce Type: cross Abstract: We propose an implicit neural formulation of optimal transport that eliminates adversarial min--max optimization and multi-network architectures commo

FlowADMM: Plug-and-play ADMM with Flow-based Renoise-Denoise Priors

arXiv:2605.08640v1 Announce Type: new Abstract: Plug-and-play (PnP) methods for solving inverse problems have recently achieved strong performance by leveraging denoising priors based on powerful gene

Foundations of Reliable Inference: Reliability-Efficiency Co-Design

arXiv:2605.10351v1 Announce Type: new Abstract: Reliable inference requires that artificial intelligence (AI) models provide trustworthy uncertainty estimates, not merely accurate predictions. Recent

Fourier Feature Methods for Nonlinear Causal Discovery: FFML Scoring, TRFF Scoring, and FFCI Testing in Mixed Data

arXiv:2605.05743v2 Announce Type: replace-cross Abstract: Gaussian process (GP) marginal likelihood scores and kernel conditional independence tests are theoretically appealing for nonlinear causal di

FQPDR: Federated Quantum Neural Network for Privacy-preserving Early Detection of Diabetic Retinopathy

arXiv:2605.08324v1 Announce Type: cross Abstract: Diabetic Retinopathy (DR) is a common complication of diabetes that can lead to blindness of people. Detecting DR at the earliest stage is essential t