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Estimating the expected output of wide random MLPs more efficiently than sampling

arXiv:2605.05179v1 Announce Type: new Abstract: By far the most common way to estimate an expected loss in machine learning is to draw samples, compute the loss on each one, and take the empirical ave

tutorialsarxiv-cs-lg

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Human DGX agent

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Safety

Evaluating Patient Safety Risks in Generative AI: Development and Validation of a FMECA Framework for Generated Clinical Content

DGX agent

arXiv:2605.04085v1 Announce Type: cross Abstract: Objectives: Large language models (LLMs) are increasingly used for clinical text summarization, yet structured methods to assess associated patient sa

safetyarxiv-cs-cl

7 May 2026

Evaluating Prompting and Execution-Based Methods for Deterministic Computation in LLMs

arXiv:2605.03227v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated strong capabilities in natural language understanding and reasoning. However, their ability to perform ex

researcharxiv-cs-ai

Evaluating Semantic Fragility in Text-to-Audio Generation Systems Under Controlled Prompt Perturbations

arXiv:2603.13824v2 Announce Type: replace-cross Abstract: Recent advances in text-to-audio generation enable models to translate natural-language descriptions into diverse musical output. However, the

safetyarxiv-cs-ai

Evaluation Cards for XAI Metrics

arXiv:2605.04410v1 Announce Type: new Abstract: The evaluation of explainable AI (XAI) methods is affected by a lack of standardization. Metrics are inconsistently defined, incompletely reported, and

Applications

Event-Based Early Warning of Vineyard Disease Risk from Environmental Time Series

arXiv:2605.04548v1 Announce Type: new Abstract: Accurate early warning of vineyard disease risk from environmental observations is essential for timely intervention and more sustainable crop protectio

applicationsarxiv-cs-lg

Every Step Counts: Step-Level Credit Assignment for Tool-Integrated Text-to-SQL

arXiv:2605.04719v1 Announce Type: new Abstract: Tool-integrated Text-to-SQL parsing has emerged as a promising paradigm, framing SQL generation as a sequential decision-making process interleaved with

safetyarxiv-cs-cl

Evidence-based anomaly detection in clinical domains

arXiv:2605.04664v1 Announce Type: new Abstract: Anomaly detection methods can be very useful in identifying interesting or concerning events. In this work, we develop and examine new probabilistic ano

EvoLM: Self-Evolving Language Models through Co-Evolved Discriminative Rubrics

model-releasesarxiv-cs-ai

arXiv:2605.03871v1 Announce Type: new Abstract: Language models encode substantial evaluative knowledge from pretraining, yet current post-training methods rely on external supervision (human annotati

Local Ai

Exact Dual Geometry of SOC-ICNN Value Functions

arXiv:2605.04722v1 Announce Type: new Abstract: Input Convex Neural Networks (ICNNs) are commonly used in a two-stage manner: one first trains a convex network and then minimizes it over its input in

local-aiarxiv-cs-lg

Example-Based Object Detection

arXiv:2605.04501v1 Announce Type: new Abstract: In recent years, object detection has achieved significant progress, especially in the field of open-vocabulary object detection. Unlike traditional met

tutorialsarxiv-cs-cv

Explaining and Preventing Alignment Collapse in Iterative RLHF

arXiv:2605.04266v1 Announce Type: new Abstract: Reinforcement learning from human feedback (RLHF) typically assumes a static or non-strategic reward model (RM). In iterative deployment, however, the p

Applications

Exploring Clustering Capability of Inpainting Model Embeddings for Pattern-based Individual Identification

arXiv:2605.04904v1 Announce Type: new Abstract: In this paper, we explore deep learning techniques for individual identification of animals based on their skin patterns. Individual identification is c

applicationsarxiv-cs-cv

Extending Differential Temporal Difference Methods for Episodic Problems

arXiv:2605.04368v1 Announce Type: new Abstract: Differential temporal difference (TD) methods are value-based reinforcement learning algorithms that have been proposed for infinite-horizon problems. T

safetyarxiv-cs-lg

External Validation of Deep Learning Models for BI-RADS Breast Density Prediction from Ultrasound Images

arXiv:2605.05082v1 Announce Type: cross Abstract: We externally validated three deep learning models (DenseNet121, ViT-B/32, and ResNet50) for predicting mammographic breast density from breast ultras

FAAST: Forward-Only Associative Learning via Closed-Form Fast Weights for Test-Time Supervised Adaptation

arXiv:2605.04651v1 Announce Type: cross Abstract: Adapting pretrained models typically involves a trade-off between the high training costs of backpropagation and the heavy inference overhead of memor

researcharxiv-cs-cl

FairEnc: A Fair Vision-Language Model with Fair Vision and Text Encoders for Glaucoma Detection

arXiv:2605.04882v1 Announce Type: new Abstract: Automated glaucoma detection is critical for preventing irreversible vision loss and reducing the burden on healthcare systems. However, ensuring fairne

safetyarxiv-cs-cv

FaithfulFaces: Pose-Faithful Facial Identity Preservation for Text-to-Video Generation

arXiv:2605.04702v1 Announce Type: new Abstract: Identity-preserving text-to-video generation (IPT2V) empowers users to produce diverse and imaginative videos with consistent human facial identity. Des

FASQ: Flexible Accelerated Subspace Quantization for Calibration-Free LLM Compression

arXiv:2605.04084v1 Announce Type: new Abstract: Compressing large language models (LLMs) for deployment on commodity GPUs remains challenging: conventional scalar quantization is limited to fixed bit-

Local Ai

FaSTA^*: Fast-Slow Toolpath Agent with Subroutine Mining for Efficient Multi-turn Image Editing

arXiv:2506.20911v2 Announce Type: replace Abstract: We develop a cost-efficient neurosymbolic agent to address challenging multi-turn image editing tasks such as ``Detect the bench in the image while

local-aiarxiv-cs-cv

Feature Identification via the Empirical NTK

arXiv:2510.00468v4 Announce Type: replace Abstract: We provide evidence that eigenanalysis of the empirical neural tangent kernel (eNTK) can surface feature directions in trained neural networks. Acro

Feature importance analysis for patient management decisions

arXiv:2605.04666v1 Announce Type: new Abstract: The objective of this paper is to understand what characteristics and features of clinical data influence physician's decision about ordering laboratory

Federated Learning for Early Prediction of EV Charging Demand

arXiv:2605.04993v1 Announce Type: new Abstract: Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimizati

Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

model-releasesarxiv-cs-cv

arXiv:2605.05034v1 Announce Type: new Abstract: Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large a

Applications

FideDiff: Efficient Diffusion Model for High-Fidelity Image Motion Deblurring

arXiv:2510.01641v3 Announce Type: replace Abstract: Recent advancements in image motion deblurring, driven by CNNs and transformers, have made significant progress. Large-scale pre-trained diffusion m

applicationsarxiv-cs-cv

Fixed-Length Dense Fingerprint Representation with Alignment and Robust Enhancement

arXiv:2505.03597v2 Announce Type: replace Abstract: Fixed-length fingerprint representations, which map each fingerprint to a compact and fixed-size feature vector, are computationally efficient and w

safetyarxiv-cs-cv

FL-Sailer: Efficient and Privacy-Preserving Federated Learning for Scalable Single-Cell Epigenetic Data Analysis via Adaptive Sampling

arXiv:2605.04519v1 Announce Type: new Abstract: Single-cell ATAC-seq (scATAC-seq) enables high-resolution mapping of chromatin accessibility, yet privacy regulations and data size constraints hinder m

FlatASCEND: Autoregressive Clinical Sequence Generation with Continuous Time Prediction and Association-Based Pharmacological Testing

arXiv:2605.04071v1 Announce Type: new Abstract: Autoregressive models can predict clinical events, but generating patient-conditioned multi-step trajectories that respond to intervention tokens and te

Agents

FlowDIS: Language-Guided Dichotomous Image Segmentation with Flow Matching

arXiv:2605.05077v1 Announce Type: new Abstract: Accurate image segmentation is essential for modern computer vision applications such as image editing, autonomous driving, and medical image analysis.

agentsarxiv-cs-cv

FLUID: Continuous-Time Hyperconnected Sparse Transformer for Sink-Free Learning

arXiv:2605.04421v1 Announce Type: new Abstract: Continuous-time (CT) Transformers improve irregular and long-range modeling over CT-RNNs by exploiting inputs or outputs embeddings with continuous dyna

safetyarxiv-cs-lg

FMI_SU_Yotkova_Kastreva at SemEval-2026 Task 13: Lightweight Detection of LLM-Generated Code via Stylometric Signals

arXiv:2605.04157v1 Announce Type: new Abstract: SemEval-2026 Task 13 investigates machine-generated code detection across multiple programming languages and application scenarios, asking participating

researcharxiv-cs-cl

Forget BIT, It is All about TOKEN: Towards Semantic Information Theory for LLMs

arXiv:2511.01202v3 Announce Type: replace-cross Abstract: Despite the unprecedented empirical triumphs of LLMs across diverse real-world applications, the prevailing research paradigm remains overwhel

tutorialsarxiv-cs-ai

Fragile Knowledge, Robust Instruction-Following: The Width Pruning Dichotomy in Llama-3.2

arXiv:2512.22671v2 Announce Type: replace Abstract: Structured width pruning of GLU-MLP layers, guided by the Maximum Absolute Weight (MAW) criterion, reveals a systematic dichotomy in how reducing th

France or Spain or Germany or France: A Neural Account of Non-Redundant Redundant Disjunctions

arXiv:2602.23547v2 Announce Type: replace Abstract: Sentences like 'She will go to France or Spain, or perhaps to Germany or France.' appear formally redundant, yet become acceptable in contexts such

tutorialsarxiv-cs-cl

Free Energy-Driven Reinforcement Learning with Adaptive Advantage Shaping for Unsupervised Reasoning in LLMs

arXiv:2605.04065v1 Announce Type: new Abstract: Unsupervised reinforcement learning (RL) has emerged as a promising paradigm for enabling self-improvement in large language models (LLMs). However, exi

From Barrier to Bridge: The Case for AI Data Center/Power Grid Co-Design

arXiv:2605.03090v1 Announce Type: cross Abstract: For over a century, the electric grid has relied on a single statistical assumption: load diversity, the principle that the uncorrelated demands of mi

researcharxiv-cs-ai

From Diffusion to Rectified Flow: Rethinking Text-Based Segmentation

arXiv:2605.04590v1 Announce Type: new Abstract: Text-based image segmentation aims to delineate object boundaries within an image from text prompts, offering higher flexibility and broader application

tutorialsarxiv-cs-cv

Local Ai

From Intent to Execution: Composing Agentic Workflows with Agent Recommendation

arXiv:2605.03986v1 Announce Type: new Abstract: Multi-Agent Systems (MAS) built using AI agents fulfill a variety of user intents that may be used to design and build a family of related applications.

local-aiarxiv-cs-ai

Agents

From Knowledge to Action: Outcomes of the 2025 Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry

arXiv:2605.03205v1 Announce Type: cross Abstract: Large language models (LLMs) are rapidly changing how researchers in materials science and chemistry discover, organize, and act on scientific knowled

agentsarxiv-cs-ai

From Language to Logic: A Theoretical Architecture for VLM-Grounded Safe Navigation

arXiv:2605.04327v1 Announce Type: new Abstract: We propose an architecture for integrating high-level, human-provided safety rules and operator-aligned semantic preferences into autonomous robot navig

safetyarxiv-cs-ro

From Parameter Dynamics to Risk Scoring : Quantifying Sample-Level Safety Degradation in LLM Fine-tuning

arXiv:2605.04572v1 Announce Type: cross Abstract: Safety alignment of Large Language Models (LLMs) is extremely fragile, as fine-tuning on a small number of benign samples can erase safety behaviors l

From Passive Feeds to Guided Discovery: AI-Initiated Interaction for Vague Intent in Content Exploration

arXiv:2605.02902v1 Announce Type: cross Abstract: Recommendation feeds work well when people are simply browsing, and search works well when they can formulate a query. Between these two cases is a co

researcharxiv-cs-ai

From Pixels to Tokens: A Systematic Study of Latent Action Supervision for Vision-Language-Action Models

arXiv:2605.04678v1 Announce Type: cross Abstract: Latent actions serve as an intermediate representation that enables consistent modeling of vision-language-action (VLA) models across heterogeneous da

From Priors to Perception: Grounding Video-LLMs in Physical Reality

arXiv:2605.04515v1 Announce Type: new Abstract: While Video Large Language Models (Video-LLMs) excel in general understanding, they exhibit systematic deficits in fine-grained physical reasoning. Exis

From Reach to Insert: Tactile-Augmented Precision Assembly under Sub-Millimeter Tolerances

arXiv:2605.04649v1 Announce Type: new Abstract: High-precision assembly frequently involves tight-tolerance insertions, where even slight pose errors can cause jamming or excessive interaction forces,

safetyarxiv-cs-ro

From SWE-ZERO to SWE-HERO: Execution-free to Execution-based Fine-tuning for Software Engineering Agents

arXiv:2604.01496v2 Announce Type: replace-cross Abstract: We introduce SWE-ZERO to SWE-HERO, a two-stage SFT recipe that achieves state-of-the-art results on SWE-bench by distilling open-weight fronti

From Video-to-PDE: Data-Driven Discovery of Nonlinear Dye Plume Dynamics

arXiv:2605.04535v1 Announce Type: new Abstract: Inferring continuum models directly from video is hampered by two facts: the recorded field is uncalibrated image intensity rather than a physical state

Frontier Lag: A Bibliometric Audit of Capability Misrepresentation in Academic AI Evaluation

arXiv:2605.04135v1 Announce Type: cross Abstract: Readers of applied-domain LLM capability evaluations want to know what AI systems can currently do. That literature answers a related, but consequenti