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Do Emotions Influence Moral Judgment in Large Language Models?

arXiv:2604.19125v1 Announce Type: new Abstract: Large language models have been extensively studied for emotion recognition and moral reasoning as distinct capabilities, yet the extent to which emotio

safetyarxiv-cs-cl

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Human DGX agent

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Do LLMs Game Formalization? Evaluating Faithfulness in Logical Reasoning

DGX agent

arXiv:2604.19459v1 Announce Type: new Abstract: Formal verification guarantees proof validity but not formalization faithfulness. For natural-language logical reasoning, where models construct axiom s

model-releasesarxiv-cs-ai

22 Apr 2026

Does Self-Consistency Improve the Recall of Encyclopedic Knowledge?

model-releasesarxiv-cs-cl

arXiv:2604.19395v1 Announce Type: new Abstract: While self-consistency is known to improve performance on symbolic reasoning, its effect on the recall of encyclopedic knowledge is unclear due to a lac

DP-FlogTinyLLM: Differentially private federated log anomaly detection using Tiny LLMs

arXiv:2604.19118v1 Announce Type: cross Abstract: Modern distributed systems generate massive volumes of log data that are critical for detecting anomalies and cyber threats. However, in real world se

DR-MMSearchAgent: Deepening Reasoning in Multimodal Search Agents

arXiv:2604.19264v1 Announce Type: new Abstract: Agentic multimodal models have garnered significant attention for their ability to leverage external tools to tackle complex tasks. However, it is obser

agentsarxiv-cs-cv

Drift Localization using Conformal Predictions

arXiv:2602.19790v2 Announce Type: replace Abstract: Concept drift -- the change of the distribution over time -- poses significant challenges for learning systems and is of central interest for monito

DT2IT-MRM: Debiased Preference Construction and Iterative Training for Multimodal Reward Modeling

arXiv:2604.19544v1 Announce Type: new Abstract: Multimodal reward models (MRMs) play a crucial role in aligning Multimodal Large Language Models (MLLMs) with human preferences. Training a good MRM req

Dual Triangle Attention: Effective Bidirectional Attention Without Positional Embeddings

arXiv:2604.18603v1 Announce Type: cross Abstract: Bidirectional transformers are the foundation of many sequence modeling tasks across natural, biological, and chemical language domains, but they are

safetyarxiv-cs-lg

DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning

model-releasesarxiv-cs-cv

arXiv:2604.18829v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved impressive performance on visual perception and reasoning tasks with RGB imagery, yet they remain

DW-Bench: Benchmarking LLMs on Data Warehouse Graph Topology Reasoning

arXiv:2604.18964v1 Announce Type: new Abstract: This paper introduces DW-Bench, a new benchmark that evaluates large language models (LLMs) on graph-topology reasoning over data warehouse schemas, exp

Dynamic Model Routing and Cascading for Efficient LLM Inference: A Survey

arXiv:2603.04445v2 Announce Type: replace-cross Abstract: The rapid growth of large language models (LLMs) with diverse capabilities, costs, and domains has created a critical need for intelligent mod

Early Pruning for Public Transport Routing

arXiv:2603.12592v2 Announce Type: replace-cross Abstract: Routing algorithms for public transport, particularly the widely used RAPTOR and its variants, often face performance bottlenecks during the t

Easy Samples Are All You Need: Self-Evolving LLMs via Data-Efficient Reinforcement Learning

arXiv:2604.18639v1 Announce Type: cross Abstract: Previous LLMs-based RL studies typically follow either supervised learning with high annotation costs, or unsupervised paradigms using voting or entro

Efficient Autoregressive Inference for Transformer Probabilistic Models

arXiv:2510.09477v2 Announce Type: replace-cross Abstract: Set-based transformer models for amortized probabilistic inference and meta-learning, such as neural processes, prior-fitted networks, and tab

Hardware

Efficient Mixture-of-Experts LLM Inference with Apple Silicon NPUs

arXiv:2604.18788v1 Announce Type: new Abstract: Apple Neural Engine (ANE) is a dedicated neural processing unit (NPU) present in every Apple Silicon chip. Mixture-of-Experts (MoE) LLMs improve inferen

hardwarearxiv-cs-lg

EfficientPENet: Real-Time Depth Completion from Sparse LiDAR via Lightweight Multi-Modal Fusion

model-releasesarxiv-cs-cv

arXiv:2604.18790v1 Announce Type: new Abstract: Depth completion from sparse LiDAR measurements and corresponding RGB images is a prerequisite for accurate 3D perception in robotic systems. Existing m

EgoMotion: Hierarchical Reasoning and Diffusion for Egocentric Vision-Language Motion Generation

arXiv:2604.19105v1 Announce Type: new Abstract: Faithfully modeling human behavior in dynamic environments is a foundational challenge for embodied intelligence. While conditional motion synthesis has

EgoSelf: From Memory to Personalized Egocentric Assistant

arXiv:2604.19564v1 Announce Type: cross Abstract: Egocentric assistants often rely on first-person view data to capture user behavior and context for personalized services. Since different users exhib

EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrieval

arXiv:2604.17458v2 Announce Type: replace Abstract: Graph-based Retrieval-Augmented Generation (GraphRAG) enhances LLMs by structuring corpus into graphs to facilitate multi-hop reasoning. While recen

Emotion-Cause Pair Extraction in Conversations via Semantic Decoupling and Graph Alignment

model-releasesarxiv-cs-cl

arXiv:2604.19547v1 Announce Type: new Abstract: Emotion-Cause Pair Extraction in Conversations (ECPEC) aims to identify the set of causal relations between emotion utterances and their triggering caus

Applications

Enabling Vibration-Based Gesture Recognition on Everyday Furniture via Energy-Efficient FPGA Implementation of 1D Convolutional Networks

arXiv:2510.23156v2 Announce Type: replace-cross Abstract: The growing demand for smart home interfaces has increased interest in non-intrusive sensing methods like vibration-based gesture recognition.

applicationsarxiv-cs-ai

Tutorials

End-to-End Large Portfolio Optimization for Variance Minimization with Neural Networks through Covariance Cleaning

arXiv:2507.01918v3 Announce Type: replace-cross Abstract: We develop a rotation-invariant neural network that provides the global minimum-variance portfolio by jointly learning how to lag-transform hi

tutorialsarxiv-cs-ai

Energy-Weighted Flow Matching: Unlocking Continuous Normalizing Flows for Efficient and Scalable Boltzmann Sampling

model-releasesarxiv-cs-lg

arXiv:2509.03726v2 Announce Type: replace-cross Abstract: Sampling from unnormalized target distributions, e.g. Boltzmann distributions mu_{ext{target}}(x) propto exp(-E(x)/T), is fundamental to many

Enforcing Reciprocity in Operator Learning for Seismic Wave Propagation

arXiv:2602.11631v2 Announce Type: replace-cross Abstract: Accurate and efficient wavefield modeling underpins seismic structure and source studies. Traditional methods comply with physical laws but ar

Enhancing Construction Worker Safety in Extreme Heat: A Machine Learning Approach Utilizing Wearable Technology for Predictive Health Analytics

arXiv:2604.19559v1 Announce Type: new Abstract: Construction workers are highly vulnerable to heat stress, yet tools that translate real-time physiological data into actionable safety intelligence rem

Enhancing Unsupervised Keyword Extraction in Academic Papers through Integrating Highlights with Abstract

arXiv:2604.19505v1 Announce Type: cross Abstract: Automatic keyword extraction from academic papers is a key area of interest in natural language processing and information retrieval. Although previou

Ensembling Pruned Attention Heads For Uncertainty-Aware Efficient Transformers

arXiv:2510.18358v2 Announce Type: replace-cross Abstract: Uncertainty quantification (UQ) is essential for deploying deep neural networks in safety-critical settings. Although methods like Deep Ensemb

safetyarxiv-cs-cv

Environmental Sound Deepfake Detection Using Deep-Learning Framework

arXiv:2604.19652v1 Announce Type: cross Abstract: In this paper, we propose a deep-learning framework for environmental sound deepfake detection (ESDD) -- the task of identifying whether the sound sce

Epistemic orientation in parliamentary discourse is associated with deliberative democracy

arXiv:2604.19699v1 Announce Type: new Abstract: The pursuit of truth is central to democratic deliberation and governance, yet political discourse reflects varying epistemic orientations, ranging from

Epistemic Skills: Reasoning about Knowledge and Oblivion

arXiv:2504.01733v4 Announce Type: replace Abstract: This paper presents a class of epistemic logics that captures the dynamics of acquiring knowledge and descending into oblivion, while incorporating

EPS: Efficient Patch Sampling for Video Overfitting in Deep Super-Resolution Model Training

arXiv:2411.16312v2 Announce Type: replace Abstract: Leveraging the overfitting property of deep neural networks (DNNs) is trending in video delivery systems to enhance video quality within bandwidth l

Error-free Training for MedMNIST Datasets

arXiv:2604.18916v1 Announce Type: new Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be

EssayCBM: Rubric-Aligned Concept Bottleneck Models for Transparent Essay Grading

arXiv:2512.20817v2 Announce Type: replace Abstract: Automated essay scoring (AES) has advanced significantly with neural language models, yet most systems remain opaque, offering little visibility int

Evaluating Answer Leakage Robustness of LLM Tutors against Adversarial Student Attacks

arXiv:2604.18660v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in education, yet their default helpfulness often conflicts with pedagogical principles. Prior work

Applications

Evaluating Histogram Matching for Robust Deep learning-Based Grapevine Disease Detection

arXiv:2604.19510v1 Announce Type: new Abstract: Variability in illumination is a primary factor limiting deep learning robustness for field-based plant disease detection. This study evaluates Histogra

applicationsarxiv-cs-cv

Evaluating LLM-Driven Summarisation of Parliamentary Debates with Computational Argumentation

arXiv:2604.19331v1 Announce Type: new Abstract: Understanding how policy is debated and justified in parliament is a fundamental aspect of the democratic process. However, the volume and complexity of

safetyarxiv-cs-cl

Evaluating LLM-Generated Obfuscated XSS Payloads for Machine Learning-Based Detection

arXiv:2604.19526v1 Announce Type: cross Abstract: Cross-site scripting (XSS) remains a persistent web security vulnerability, especially because obfuscation can change the surface form of a malicious

Local Ai

Evaluation-driven Scaling for Scientific Discovery

arXiv:2604.19341v1 Announce Type: cross Abstract: Language models are increasingly used in scientific discovery to generate hypotheses, propose candidate solutions, implement systems, and iteratively

local-aiarxiv-cs-ai

Evaluation of Winning Solutions of 2025 Low Power Computer Vision Challenge

arXiv:2604.19054v1 Announce Type: new Abstract: The IEEE Low-Power Computer Vision Challenge (LPCVC) aims to promote the development of efficient vision models for edge devices, balancing accuracy wit

EvoMaster: A Foundational Evolving Agent Framework for Agentic Science at Scale

arXiv:2604.17406v2 Announce Type: replace Abstract: The convergence of large language models and agents is catalyzing a new era of scientific discovery: Agentic Science. While the scientific method is

agentsarxiv-cs-ai

EVPO: Explained Variance Policy Optimization for Adaptive Critic Utilization in LLM Post-Training

arXiv:2604.19485v1 Announce Type: cross Abstract: Reinforcement learning (RL) for LLM post-training faces a fundamental design choice: whether to use a learned critic as a baseline for policy optimiza

Experiments or Outcomes? Probing Scientific Feasibility in Large Language Models

arXiv:2604.18786v1 Announce Type: cross Abstract: Scientific feasibility assessment asks whether a claim is consistent with established knowledge and whether experimental evidence could support or ref

ExpertGen: Scalable Sim-to-Real Expert Policy Learning from Imperfect Behavior Priors

arXiv:2603.15956v2 Announce Type: replace-cross Abstract: Learning generalizable and robust behavior cloning policies requires large volumes of high-quality robotics data. While human demonstrations (

Explicit Trait Inference for Multi-Agent Coordination

arXiv:2604.19278v1 Announce Type: new Abstract: LLM-based multi-agent systems (MAS) show promise on complex tasks but remain prone to coordination failures such as goal drift, error cascades, and misa

agentsarxiv-cs-ai

Explore Like Humans: Autonomous Exploration with Online SG-Memo Construction for Embodied Agents

arXiv:2604.19034v1 Announce Type: new Abstract: Constructing structured spatial memory is essential for enabling long-horizon reasoning in complex embodied navigation tasks. Current memory constructio

agentsarxiv-cs-cv

Applications

Exploring Language-Agnosticity in Function Vectors: A Case Study in Machine Translation

arXiv:2604.19678v1 Announce Type: new Abstract: Function vectors (FVs) are vector representations of tasks extracted from model activations during in-context learning. While prior work has shown that

applicationsarxiv-cs-cl

Face Anything: 4D Face Reconstruction from Any Image Sequence

arXiv:2604.19702v1 Announce Type: new Abstract: Accurate reconstruction and tracking of dynamic human faces from image sequences is challenging because non-rigid deformations, expression changes, and

Failure Modes in Multi-Hop QA: The Weakest Link Effect and the Recognition Bottleneck

arXiv:2601.12499v2 Announce Type: replace Abstract: Despite scaling to massive context windows, Large Language Models (LLMs) struggle with multi-hop reasoning due to inherent position bias, which caus