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Entropy-Dominated Temporal Vocal Dynamics as Digital Biomarkers for Depression Detection

arXiv:2604.26998v1 Announce Type: cross Abstract: Automated depression detection often relies on static aggregation of conversational signals, potentially obscuring clinically meaningful behavioral dy

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Entropy of Ukrainian

DGX agent

arXiv:2604.27534v1 Announce Type: new Abstract: In natural language processing, the entropy of a language is a measure of its unpredictability and complexity. The first study on this subject was condu

model-releasesarxiv-cs-cl

1 May 2026

Epistemic reflections on AI answering our questions: overwatch, erudite, logician, interlocutor

arXiv:2304.14352v2 Announce Type: replace-cross Abstract: Currently, there is a trend for the wider public to rely on LLMs for financial or legal consultation, medical and mental support (Chatterji et

applicationsarxiv-cs-ai

Evaluating Epistemic Guardrails in AI Reading Assistants: A Behavioral Audit of a Minimal Prototype

arXiv:2604.27275v1 Announce Type: cross Abstract: Large language model (LLM) reading assistants are increasingly used in settings that require interpretation rather than simple retrieval. In these con

Evaluating TabPFN for Mild Cognitive Impairment to Alzheimer's Disease Conversion in Data Limited Settings

arXiv:2604.27195v1 Announce Type: new Abstract: Accurate prediction of conversion from Mild Cognitive Impairment (MCI) to Alzheimers Diseases (AD) is essential for early intervention, however, develop

EviMem: Evidence-Gap-Driven Iterative Retrieval for Long-Term Conversational Memory

arXiv:2604.27695v1 Announce Type: cross Abstract: Long-term conversational memory requires retrieving evidence scattered across multiple sessions, yet single-pass retrieval fails on temporal and multi

researcharxiv-cs-cl

ExoActor: Exocentric Video Generation as Generalizable Interactive Humanoid Control

arXiv:2604.27711v1 Announce Type: new Abstract: Humanoid control systems have made significant progress in recent years, yet modeling fluent interaction-rich behavior between a robot, its surrounding

applicationsarxiv-cs-ro

Explainable Load Forecasting with Covariate-Informed Time Series Foundation Models

arXiv:2604.28149v1 Announce Type: new Abstract: Time Series Foundation Models (TSFMs) have recently emerged as general-purpose forecasting models and show considerable potential for applications in en

researcharxiv-cs-lg

Exploration Hacking: Can LLMs Learn to Resist RL Training?

arXiv:2604.28182v1 Announce Type: cross Abstract: Reinforcement learning (RL) has become essential to the post-training of large language models (LLMs) for reasoning, agentic capabilities and alignmen

safetyarxiv-cs-cl

Exploring Applications of Transfer-State Large Language Models: Cognitive Profiling and Socratic AI Tutoring

arXiv:2604.27454v1 Announce Type: new Abstract: Large language models (LLMs) sometimes exhibit qualitative shifts in response style under sustained self-referential dialogue conditions (Berg et al., 2

safetyarxiv-cs-cl

Exploring Interaction Paradigms for LLM Agents in Scientific Visualization

arXiv:2604.27996v1 Announce Type: new Abstract: This paper examines how different types of large language model (LLM) agents perform on scientific visualization (SciVis) tasks, where users generate vi

Exploring the Adoption Intention in Using AI-Enabled Educational Tools Among Preservice Teachers in the Philippines: A Partial-Least Square Modeling

arXiv:2604.27346v1 Announce Type: cross Abstract: This study examines the factors influencing pre-service teachers' behavioral intention to use AI-enabled educational tools during their practicum, usi

Exploring the Limits of Pruning: Task-Specific Neurons, Model Collapse, and Recovery in Task-Specific Large Language Models

model-releasesarxiv-cs-cl

arXiv:2604.27115v1 Announce Type: new Abstract: Neuron pruning is widely used to reduce the computational cost and parameter footprint of large language models, yet it remains unclear whether neurons

Exploring Vision Neural Network Pruning via Screening Methodology

arXiv:2502.07189v2 Announce Type: replace Abstract: The remarkable performance of modern deep neural networks (DNNs) is largely driven by their massive scale, often comprising tens to hundreds of mill

EXPO: Stable Reinforcement Learning with Expressive Policies

arXiv:2507.07986v3 Announce Type: replace-cross Abstract: We study the problem of training and fine-tuning expressive policies with online reinforcement learning (RL) given an offline dataset. Trainin

Exponential families from a single KL identity

arXiv:2604.28036v1 Announce Type: new Abstract: Exponential families encompass the distributions central to modern machine learning -- softmax, Gaussians, and Boltzmann distributions -- and underlie t

researcharxiv-cs-lg

Fairness for distribution network operations and planning

arXiv:2604.27669v1 Announce Type: new Abstract: The incorporation of fairness into the distribution network (DN) planning and operation has become a key goal of recent studies. The cost of implementin

Fake3DGS: A Benchmark for 3D Manipulation Detection in Neural Rendering

model-releasesarxiv-cs-cv

arXiv:2604.27590v1 Announce Type: new Abstract: Recent advances in 3D reconstruction and neural rendering,particularly 3D Gaussian Splatting, make it feasible and simple to edit 3D scenes and re-rende

Faster 3D Gaussian Splatting Convergence via Structure-Aware Densification

arXiv:2604.28016v1 Announce Type: new Abstract: 3D Gaussian Splatting has emerged as a powerful scene representation for real-time novel-view synthesis. However, its standard adaptive density control

researcharxiv-cs-cv

Federated Knowledge Distillation for Multi-Model Architectures Lithography Hotspot Detection

model-releasesarxiv-cs-lg

arXiv:2501.04066v2 Announce Type: replace Abstract: As a special type of multimedia data, Lithography Hotspot Detection (LHD) training often requires stronger privacy protection than conventional mult

FedHarmony: Harmonizing Heterogeneous Label Correlations in Federated Multi-Label Learning

arXiv:2604.28024v1 Announce Type: new Abstract: Federated Multi-Label Learning is a distributed paradigm where multiple clients possess heterogeneous multi-label data and perform collaborative learnin

Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation

model-releasesarxiv-cs-lg

arXiv:2604.27014v1 Announce Type: new Abstract: The scarcity of high-quality annotated medical data, particularly in mental health, poses a significant bottleneck for training robust machine learning

FiLMMeD: Feature-wise Linear Modulation for Cross-Problem Multi-Depot Vehicle Routing

arXiv:2604.28102v1 Announce Type: new Abstract: Solving practical multi-depot vehicle routing problems (MDVRP) is a challenging optimization task central to modern logistics, increasingly driven by e-

FinCARDS: Card-Based Analyst Reranking for Financial Document Question Answering

arXiv:2601.06992v2 Announce Type: replace-cross Abstract: Financial question answering (QA) over long corporate filings requires evidence to satisfy strict constraints on entities, financial metrics,

applicationsarxiv-cs-ai

FinChain: A Symbolic Benchmark for Verifiable Chain-of-Thought Financial Reasoning

arXiv:2506.02515v4 Announce Type: replace-cross Abstract: Multi-step symbolic reasoning is essential for robust financial analysis; yet, current benchmarks largely overlook this capability. Existing d

FineState-Bench: Benchmarking State-Conditioned Grounding for Fine-grained GUI State Setting

model-releasesarxiv-cs-cv

arXiv:2604.27974v1 Announce Type: new Abstract: Despite the rapid progress of large vision-language models (LVLMs), fine-grained, state-conditioned GUI interaction remains challenging. Current evaluat

Fitting Horn DL Ontologies to ABox and Query Examples: A Tale of Simulation Quantifiers and Finite Models

arXiv:2604.26976v1 Announce Type: cross Abstract: We study the problem of fitting a description logic (DL) ontology to a given set of positive and negative examples that take the form of an ABox and a

Flattery in Motion: Benchmarking and Analyzing Sycophancy in Video-LLMs

arXiv:2506.07180v3 Announce Type: replace-cross Abstract: As video large language models (Video-LLMs) become increasingly integrated into real-world applications that demand grounded multimodal reason

Hardware

FlexiTac: A Low-Cost, Open-Source, Scalable Tactile Sensing Solution for Robotic Systems

arXiv:2604.28156v1 Announce Type: cross Abstract: We present FlexiTac, a low-cost, open-source, and scalable piezoresistive tactile sensing solution designed for robotic end-effectors. FlexiTac is a p

hardwarearxiv-cs-ai

FluxMoE: Decoupling Expert Residency for High-Performance MoE Serving

model-releasesarxiv-cs-lg

arXiv:2604.02715v2 Announce Type: replace Abstract: Mixture-of-Experts (MoE) models have become a dominant paradigm for scaling large language models, but their rapidly growing parameter sizes introdu

Agents

Flying by Inference: Active Inference World Models for Adaptive UAV Swarms

arXiv:2604.27935v1 Announce Type: new Abstract: This paper presents an expert-guided active-inference-inspired framework for adaptive UAV swarm trajectory planning. The proposed method converts multi-

agentsarxiv-cs-ro

FMCL: Class-Aware Client Clustering with Foundation Model Representations for Heterogeneous Federated Learning

arXiv:2604.27510v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative model training across distributed clients without sharing raw data, yet its performance deteriorates und

researcharxiv-cs-cv

Focal Modulation and Bidirectional Feature Fusion Network for Medical Image Segmentation

arXiv:2510.20933v2 Announce Type: replace-cross Abstract: Medical image segmentation is essential for clinical applications such as disease diagnosis, treatment planning, and disease development monit

Focus Session: Autonomous Systems Dependability in the era of AI: Design Challenges in Safety, Security, Reliability and Certification

arXiv:2604.27807v1 Announce Type: new Abstract: The design of embedded safety-critical systems such as those used in next-generation automotive and autonomous platforms, is increasingly challenged by

Foreclassing: A new machine learning perspective on human decision making with temporal data

arXiv:2503.04956v2 Announce Type: replace-cross Abstract: Time series forecasts are widely used to inform decisions. Human decision-makers interpret these forecasts, incorporate prior experience and u

FP-IRL: Fokker--Planck Inverse Reinforcement Learning -- A Physics-Constrained Approach to Markov Decision Processes

arXiv:2306.10407v3 Announce Type: replace-cross Abstract: Inverse reinforcement learning (IRL) is a powerful paradigm for uncovering the incentive structure that drives agent behavior, by inferring an

Agents

Framework for Collaborative Operation of Autonomous Delivery Vehicles Within a Marshaling Yard

arXiv:2604.28057v1 Announce Type: new Abstract: As autonomous vehicles slowly deploy into urban roads for limited use cases with significant edge case issues, closed facilities like marshaling yards p

agentsarxiv-cs-ro

FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction

model-releasesarxiv-cs-cv

arXiv:2604.28115v1 Announce Type: cross Abstract: Existing learning-based occupancy prediction methods rely on large-scale 3D annotations and generalize poorly across environments. We present FreeOcc,

Frequency-Adaptive Discrete Cosine-ViT-ResNet Architecture for Sparse-Data Vision

arXiv:2505.22701v3 Announce Type: replace Abstract: A major challenge in rare animal image classification is the scarcity of data, as many species usually have only a small number of labeled samples.

researcharxiv-cs-cv

Frequency-Aware Semantic Fusion with Gated Injection for AI-generated Image Detection

arXiv:2604.27875v1 Announce Type: new Abstract: AI-generated images are becoming increasingly realistic and diverse, posing significant challenges for generalizable detection. While Vision Foundation

safetyarxiv-cs-cv

From Action Labels to Sets: Rethinking Action Supervision for Imitation Learning from Corrective Feedback

arXiv:2502.07645v3 Announce Type: replace Abstract: Behavior cloning (BC) optimizes policies by treating human demonstrations as pointwise action labels. While effective with accurate action labels, t

safetyarxiv-cs-ro

From Coarse to Fine: Benchmarking and Reward Modeling for Writing-Centric Generation Tasks

arXiv:2604.27453v1 Announce Type: new Abstract: Large language models have achieved remarkable progress in text generation but still struggle with generative writing tasks. In terms of evaluation, exi

researcharxiv-cs-cl

From Competition to Collaboration: Designing Sustainable Mechanisms Between LLMs and Online Forums

arXiv:2602.04572v2 Announce Type: replace Abstract: While Generative AI (GenAI) systems draw users away from (Q&A) forums, they also depend on the very data those forums produce to improve their perfo

Agents

From Context to Skills: Can Language Models Learn from Context Skillfully?

arXiv:2604.27660v1 Announce Type: new Abstract: Many real-world tasks require language models (LMs) to reason over complex contexts that exceed their parametric knowledge. This calls for context learn

agentsarxiv-cs-ai

From LLM-Driven Trading Card Generation to Procedural Relatedness: A Pokemon Case Study

arXiv:2604.27972v1 Announce Type: new Abstract: Since the dawn of Trading Card Games, the genre has grown into a multi-billion-dollar industry engaging millions of analog and digital players worldwide

applicationsarxiv-cs-ai

From Mirage to Grounding: Towards Reliable Multimodal Circuit-to-Verilog Code Generation

arXiv:2604.27969v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are increasingly used to translate visual artifacts into code, from UI mockups into HTML to scientific plots

From Prompt to Physical Actuation: Holistic Threat Modeling of LLM-Enabled Robotic Systems

arXiv:2604.27267v1 Announce Type: cross Abstract: As large language models are integrated into autonomous robotic systems for task planning and control, compromised inputs or unsafe model outputs can

From surveillance to signalling: escalation channels as environmental controls for agentic AI

arXiv:2510.05192v2 Announce Type: replace-cross Abstract: When AI agents operating with access to sensitive information encounter a conflict between completing an assigned task and following rules or