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Emergent Ordinal Geometry in Transformers Trained on Local Comparisons

arXiv:2606.01269v1 Announce Type: new Abstract: Transitive inference is the challenge of inferring that A < C from knowing only adjacent relations (A < B, B < C). It is solved by humans and animals no

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Emergent Transfer of a Physics Foundation Model from Simulation to Laboratory Turbulence

DGX agent

arXiv:2606.01470v1 Announce Type: cross Abstract: Whether physics foundation models can be usefully deployed on laboratory experiments remains an open question for scientific machine learning (ML). We

researcharxiv-cs-ai

2 Jun 2026

EMoE: Training-Free Expert Disagreement for Uncertainty-Aware Text-to-Image Diffusion

arXiv:2505.13273v2 Announce Type: replace Abstract: Large text-to-image diffusion models rarely expose reliable signals of when a prompt is likely to produce a poorly aligned generation, especially wh

safetyarxiv-cs-ai

eMoT: evolving Memory-of-Thought via Symbolic Anchoring and Memory Corrosion

arXiv:2606.02054v1 Announce Type: new Abstract: While Large Language Models (LLMs) achieve impressive performance on multi-step reasoning tasks, their reliability is persistently hindered by critical

Empathic and agentic artificial intelligence in nursing: perspectives on a human-centered framework for cancer care navigation in the United States

arXiv:2606.00010v1 Announce Type: cross Abstract: For patients experiencing cancer, nurse navigation can ease the burden of complex care by enhancing coordination of health services and patient outcom

agentsarxiv-cs-ai

Empathy Applicability Modeling for General Health Queries

model-releasesarxiv-cs-cl

arXiv:2601.09696v2 Announce Type: replace Abstract: LLMs are increasingly being integrated into clinical workflows, yet they often lack clinical empathy, an essential aspect of effective doctor-patien

Encoded but Not Routed: Explaining the Table-Chart Gap in Scientific Claim Verification

arXiv:2606.01679v1 Announce Type: new Abstract: Multimodal LLMs are increasingly used to assist scientific peer review, where a core requirement is verifying whether claims in a paper are supported by

researcharxiv-cs-cl

End-to-End Deep Learning for Predicting Metric Space-Valued Outputs

arXiv:2509.23544v2 Announce Type: replace-cross Abstract: Many modern applications involve predicting structured, non-Euclidean outputs such as probability distributions, networks, and symmetric posit

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

arXiv:2606.00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning. Although advanced m

applicationsarxiv-cs-ai

Enhancing BiGRU with a KAN Block for Legal Document Classification and Summarization

arXiv:2606.00116v1 Announce Type: cross Abstract: This study introduces a novel architecture of KAN-based BiGRU model for the task of classification and summarization of legal documents in a low-resou

applicationsarxiv-cs-ai

Enhancing Blind Source Separation with Dissociative Principal Component Analysis

model-releasesarxiv-cs-cv

arXiv:2411.12321v2 Announce Type: replace Abstract: Principal component analysis (PCA) and its sparse variants (sPCA) are widely used as a precursor to independent component analysis (ICA) for blind s

Enhancing Layer Attention Efficiency through Pruning Redundant Retrievals

arXiv:2503.06473v5 Announce Type: replace-cross Abstract: Growing evidence suggests that layer attention mechanisms, which enhance interaction among layers in deep neural networks, have significantly

Enhancing LLM Metacognition via Cognitive Pairwise Training

model-releasesarxiv-cs-lg

arXiv:2606.00869v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) has become central to LLM reasoning, but its outcome-level rewards can make models more willing to

Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging

arXiv:2606.02339v1 Announce Type: cross Abstract: Entropy minimization (EM) is the dominant objective for test-time adaptation, yet its failure mode, model collapse, remains poorly understood. In this

safetyarxiv-cs-cv

EPIC: Efficient and Parallel Inference under CFG Constraints for Diffusion Language Models

arXiv:2606.00722v1 Announce Type: cross Abstract: Controlling language model outputs is essential for ensuring structural validity, reliability, and downstream usability, and diffusion language models

Equilibrated Diffusion: Frequency-aware Textual Embedding for Equilibrated Image Customization

arXiv:2606.02129v1 Announce Type: new Abstract: Image customization learns target subjects from reference concept images and generates conditioned images per text prompts, mainly modifying styles or b

safetyarxiv-cs-cv

Equilibrium Propagation for Non-Conservative Systems

arXiv:2602.03670v2 Announce Type: replace-cross Abstract: Equilibrium Propagation (EP) is a physics-inspired learning algorithm that uses stationary states of a dynamical system both for inference and

Erased but Not Forgotten: How Backdoors Compromise Concept Erasure

arXiv:2504.21072v2 Announce Type: replace-cross Abstract: The expansion of text-to-image diffusion models has raised concerns about harmful outputs, from fabricated depictions of public figures to sex

ERICA: Quantifying Replicability of Cluster Analysis

arXiv:2606.00302v1 Announce Type: cross Abstract: Despite being ubiquitous in science, clustering remains a technique whose results are not quantitatively scrutinized via a framework. We present an an

researcharxiv-cs-lg

Error Bounds for a Diffusion Model-Based Drift Estimator

model-releasesarxiv-cs-lg

arXiv:2606.02115v1 Announce Type: cross Abstract: Parameter estimation in stochastic differential equations is a classical statistical problem of much importance in many scientific fields. Recent work

ES-Merging: Biological MLLM Merging via Embedding Space Signals

arXiv:2603.14405v2 Announce Type: replace-cross Abstract: Biological multimodal large language models (MLLMs) have emerged as powerful foundation models for scientific discovery. However, existing mod

Escaping the BLEU Trap: A Signal-Grounded Framework with Decoupled Semantic Guidance for EEG-to-Text Decoding

arXiv:2603.03312v3 Announce Type: replace-cross Abstract: Decoding natural language from non-invasive EEG signals is a promising yet challenging task. However, current state-of-the-art models remain c

Escaping the Mode Lottery: Multi-Response Training Improves Language Model Generalization

model-releasesarxiv-cs-cl

arXiv:2606.00544v1 Announce Type: cross Abstract: Modern language-model fine-tuning typically pairs each prompt with a single response, even though many prompts admit multiple valid completions. This

EST-PRM: Stress-Testing Process Reward Models Before They Become Load-Bearing

arXiv:2606.00437v1 Announce Type: new Abstract: Process reward models (PRMs) are widely used in language-model training with dense step-level supervision. They assume PRM scores are stable proxies for

researcharxiv-cs-lg

Estimating Mutual Information between Time Series and Temporal Event Sequences Across Diverse Analysis Tasks

arXiv:2606.01602v1 Announce Type: cross Abstract: Pairwise dependence measures such as correlation and causality are fundamental to temporal data mining, yet there is still no principled and robust wa

safetyarxiv-cs-ai

ETC: Extreme Token Compression via Task-aware Visual Information Distillation in VLMs

arXiv:2606.00543v1 Announce Type: new Abstract: In Vision-Language Models (VLMs), high-resolution images produce a large number of visual tokens, resulting in high computational costs and KV-cache ove

researcharxiv-cs-cv

EuraGovExam: A Multilingual Multimodal Benchmark from Real-World Civil Service Exams

arXiv:2603.27223v2 Announce Type: replace-cross Abstract: We present EuraGovExam, a multilingual and multimodal benchmark sourced from real-world civil service examinations across five representative

EuroBERT: Scaling Multilingual Encoders for European Languages

arXiv:2503.05500v3 Announce Type: replace-cross Abstract: General-purpose multilingual vector representations, used in retrieval, regression and classification, are traditionally obtained from bidirec

Ev-Trust: An Evolutionarily Stable Trust Mechanism for Decentralized LLM-Based Multi-Agent Service Economies

arXiv:2512.16167v3 Announce Type: replace-cross Abstract: Decentralized LLM-based multi-agent service economies face three vulnerabilities that undermine traditional trust mechanisms: reduced cost of

agentsarxiv-cs-ai

EVA-Net: Subject-Independent EEG Motor Decoding with Video-Derived Motor Priors

arXiv:2606.01884v1 Announce Type: new Abstract: Practical non-invasive Brain-Computer Interface (BCI) systems require EEG decoders with strong cross-subject generalization and minimal calibration. How

Evaluating and Learning Robust Bandit Policies Under Uncertain Causal Mechanisms

arXiv:2508.02812v3 Announce Type: replace Abstract: Causal graphical models can encode large amounts structural knowledge, both from the background knowledge of domain experts and the structural knowl

safetyarxiv-cs-lg

Evaluating Bivariate Causal Statements Based on Mutual Compatibility

arXiv:2606.00278v1 Announce Type: new Abstract: For many real-world systems, causal ground truth is difficult to obtain, making claims about causal effects hard to assess. We develop methods for evalu

applicationsarxiv-cs-ai

Evaluating Interactive Reasoning in Large Language Models: A Hierarchical Benchmark with Executable Games

arXiv:2606.00103v1 Announce Type: new Abstract: We introduce a multi-turn interactive framework for reasoning evaluation that treats reasoning as active evidence acquisition and belief updating. Where

Evaluating Real-World Generalizability of Algorithm Selection Models

model-releasesarxiv-cs-lg

arXiv:2606.02016v1 Announce Type: new Abstract: Algorithm Selection (AS) aims to automatically identify the most suitable optimization algorithm for a given problem instance by leveraging measurable p

Evaluating Reliability Asymmetries in Chinese Factual Search and AI Answers

arXiv:2602.22221v2 Announce Type: replace-cross Abstract: Search engines and AI-powered systems increasingly mediate access to factual information, yet their reliability remains difficult to evaluate

Evaluating the Performance of Deep Learning Models in Whole-body Dynamic 3D Posture Prediction During Load-reaching Activities

arXiv:2511.20615v2 Announce Type: replace-cross Abstract: This study aimed to explore the application of deep neural networks for whole-body human posture prediction during dynamic load-reaching activ

Evaluating the Reversal Curse in Model Editing

model-releasesarxiv-cs-cl

arXiv:2310.10322v3 Announce Type: replace Abstract: Large language models (LLMs) are prone to hallucinate unintended text due to false or outdated knowledge. Since retraining LLMs is resource intensiv

Evaluation of Baseline Methods for IDD-based SSD External Memory Search

arXiv:2606.01840v1 Announce Type: new Abstract: Many difficult search problems cannot be solved by algorithms such as A* using only RAM. Search algorithms which use external memory such as SSDs and HD

Event-Based Vision in Space: Applications, Trends, and Future Directions

arXiv:2606.01280v1 Announce Type: new Abstract: Earth Observation (EO) is undergoing a significant transformation driven by the deployment of novel sensing technologies. Traditional frame-based optica

researcharxiv-cs-cv

Everywhere Learning: Artificial Intelligence with Pointwise Constraints

arXiv:2606.01557v1 Announce Type: new Abstract: Everywhere learning is a new paradigm whereby Artificial Intelligence (AI) systems are trained to satisfy loss constraints with probability one over the

agentsarxiv-cs-lg

Tutorials

Evidence-Gated LLM Priors for Multi-Objective Bayesian Optimization

arXiv:2606.01730v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used as heuristic advisors for black-box optimization, yet their suggestions and self-reported confidence

tutorialsarxiv-cs-ai

EvoBrain: Continual Learning of EEG Foundation Models Across Heterogeneous BCI Tasks

arXiv:2606.01767v1 Announce Type: new Abstract: Electroencephalography (EEG) is the cornerstone of non-invasive brain-computer interfaces (BCIs), yet conventional decoding relies on fragmented, task-s

EvoCut: Multi-Layer Evolution-Aware Visual Token Compression for Efficient Large Vision-Language Models

arXiv:2606.01756v1 Announce Type: new Abstract: Large vision-language models (LVLMs) achieve strong performance on image and video understanding tasks, but their inference efficiency is constrained by

researcharxiv-cs-cv

Local Ai

Evolutionary Discovery of Bivariate Bicycle Codes with LLM-Guided Search

arXiv:2606.02418v1 Announce Type: cross Abstract: Quantum LDPC code discovery requires searching large algebraic design spaces while reliably certifying the parameters and equivalence classes of any c

local-aiarxiv-cs-ai

Evolving to the Aesthetics of a Vision-Language Model

arXiv:2606.00112v1 Announce Type: cross Abstract: Evolutionary systems have demonstrated remarkable results in creative domains, with recent applications in generative typography, design, and music. H

applicationsarxiv-cs-cv

EvoPool: Evolutionary Programmatic Annotation for Label-Efficient Specialized Supervision

arXiv:2606.01617v1 Announce Type: cross Abstract: Large language models excel at general tasks but underperform smaller supervised models in specialized, high-stakes domains where training labels are

agentsarxiv-cs-ai

Examine Clinicians' Modification of Hedging Language in Ambient AI Documentation: A Comparative Study of AI Drafts and Final Notes

arXiv:2606.00018v1 Announce Type: cross Abstract: Ambient AI documentation systems generate clinical note drafts that clinicians frequently revise before signing off into electronic health records, ye

Expanding Spatial and Temporal Context for Robotic Imitation Learning With Scene Graphs

arXiv:2606.01072v1 Announce Type: cross Abstract: Imitation learning enables robots to learn how to execute tasks via observation. However, real-world environments like homes and offices are often sev

safetyarxiv-cs-cv