All entries

Deep Learning-Based Segmentation of Peritoneal Cancer Index Regions from CT Imaging

arXiv:2604.27697v1 Announce Type: cross Abstract: Peritoneal metastases are currently assessed using diagnostic laparoscopy to determine Sugarbaker's Peritoneal Cancer Index (sPCI), which works by div

Content type

All Blog X Post Paper YouTube Reddit GitHub

Clear filters

DeepTutor: Towards Agentic Personalized Tutoring

DGX agent

arXiv:2604.26962v1 Announce Type: cross Abstract: Education represents one of the most promising real-world applications for Large Language Models (LLMs). However, conventional tutoring systems rely o

model-releasesarxiv-cs-ai

1 May 2026

DeepWeightFlow: Re-Basined Flow Matching for Generating Neural Network Weights

arXiv:2601.05052v2 Announce Type: replace Abstract: Building efficient and effective generative models for neural network weights has been a research focus of significant interest that faces challenge

DEFault++: Automated Fault Detection, Categorization, and Diagnosis for Transformer Architectures

model-releasesarxiv-cs-ai

arXiv:2604.28118v1 Announce Type: cross Abstract: Transformer models are widely deployed in critical AI applications, yet faults in their attention mechanisms, projections, and other internal componen

Defeasible Conditional Obligation in a Two-tiered Preference-based Semantics (Extended Version)

arXiv:2604.26977v1 Announce Type: cross Abstract: In response to a concern raised by Horty, this paper develops a two-tiered, preference-based semantic framework for modeling defeasible conditional ob

Tutorials

Defending Quantum Classifiers against Adversarial Perturbations through Quantum Autoencoders

arXiv:2604.28176v1 Announce Type: cross Abstract: Machine learning models can learn from data samples to carry out various tasks efficiently. When data samples are adversarially manipulated, such as b

tutorialsarxiv-cs-lg

Design and Characteristics of a Thin-Film ThermoMesh for the Efficient Embedded Sensing of a Spatio-Temporally Sparse Heat Source

arXiv:2604.28148v1 Announce Type: new Abstract: This work presents ThermoMesh, a passive thin-film thermoelectric mesh sensor designed to detect and characterize spatio-temporally sparse heat sources

researcharxiv-cs-ro

Design Structure Matrix Modularization with Large Language Models

arXiv:2604.28018v1 Announce Type: cross Abstract: Design Structure Matrix (DSM) modularization, the task of partitioning system elements into cohesive modules, is a fundamental combinatorial challenge

Designing Ethical Learning for Agentic AI: Toegye Yi Hwang's Ethical Emotion Regulation Framework

arXiv:2604.26958v1 Announce Type: cross Abstract: Agentic AI systems capable of autonomous goal setting and proactive intervention introduce new challenges for regulating moral-emotional processes in

Detecting Clinical Discrepancies in Health Coaching Agents: A Dual-Stream Memory and Reconciliation Architecture

arXiv:2604.27045v1 Announce Type: cross Abstract: As Large Language Model (LLM) agents transition from single-session tools to persistent systems managing longitudinal healthcare journeys, their memor

Detecting is Easy, Adapting is Hard: Local Expert Growth for Visual Model-Based Reinforcement Learning under Distribution Shift

arXiv:2604.27411v1 Announce Type: new Abstract: Visual model-based reinforcement learning (MBRL) agents can perform well on the training distribution, but often break down once the test environment sh

local-aiarxiv-cs-lg

Detecting Malicious Concepts without Image Generation in AI-Generated Content (AIGC)

arXiv:2502.08921v2 Announce Type: replace-cross Abstract: The task of text-to-image generation has achieved tremendous success in practice, with emerging concept generation models capable of producing

researcharxiv-cs-cv

Diagnosing Capability Gaps in Fine-Tuning Data

arXiv:2604.27547v1 Announce Type: new Abstract: Fine-tuning large language models (LLMs) for domain-specific tasks requires training datasets that comprehensively cover the target capabilities a pract

Differentiable latent structure discovery for interpretable forecasting in clinical time series

arXiv:2604.27967v1 Announce Type: new Abstract: Background: Timely, uncertainty-aware forecasting from irregular electronic health records (EHR) can support critical-care decisions, yet most approache

Differential Subgroup Discovery: Characterizing Where Two Populations Differ, and Why

arXiv:2604.27741v1 Announce Type: new Abstract: We study the problem of understanding where two populations differ within a feature space, which we formalize in the concept of a differential subgroup:

Diffusion-OAMP for Joint Image Compression and Wireless Transmission

arXiv:2604.27952v1 Announce Type: cross Abstract: Joint image compression and wireless transmission remain relatively underexplored compared to generic image restoration, despite its importance in pra

Distributional Alignment Games for Answer-Level Fine-Tuning

arXiv:2604.27166v1 Announce Type: new Abstract: We focus on the problem of Answer-Level Fine-Tuning (ALFT), where the goal is to optimize a language model based on the correctness or properties of its

safetyarxiv-cs-lg

Do Papers Tell the Whole Story? A Benchmark and Framework for Uncovering Hidden Implementation Gaps in Bioinformatics

arXiv:2603.22018v2 Announce Type: replace Abstract: Ensuring consistency between research papers and their corresponding software code implementations is a fundamental prerequisite for guaranteeing th

Do Sparse Autoencoders Capture Concept Manifolds?

arXiv:2604.28119v1 Announce Type: cross Abstract: Sparse autoencoders (SAEs) are widely used to extract interpretable features from neural network representations, often under the implicit assumption

Do What I Say: A Spoken Prompt Dataset for Instruction-Following

model-releasesarxiv-cs-cl

arXiv:2603.09881v2 Announce Type: replace Abstract: Speech Large Language Models (SLLMs) have rapidly expanded, supporting a wide range of tasks. These models are typically evaluated using text prompt

Do World Action Models Generalize Better than VLAs? A Robustness Study

model-releasesarxiv-cs-ro

arXiv:2603.22078v3 Announce Type: replace Abstract: Robot action planning in the real world is challenging as it requires not only understanding the current state of the environment but also predictin

DOT-Sim: Differentiable Optical Tactile Simulation with Precise Real-to-Sim Physical Calibration

arXiv:2604.27367v1 Announce Type: cross Abstract: Simulating optical tactile sensors presents significant challenges due to their high deformability and intricate optical properties. To address these

safetyarxiv-cs-cv

DPN-LE: Dual Personality Neuron Localization and Editing for Large Language Models

model-releasesarxiv-cs-cl

arXiv:2604.27929v1 Announce Type: new Abstract: With the widespread adoption of large language models (LLMs), understanding their personality representation mechanisms has become critical. As a novel

Dreaming Across Towns: Semantic Rollout and Town-Adversarial Regularization for Zero-Shot Held-Out-Town Fixed-Route Driving in CARLA

arXiv:2604.27994v1 Announce Type: new Abstract: Learned driving agents often degrade when deployed in unseen environments. This paper studies a deliberately bounded instance of that problem in the CAR

safetyarxiv-cs-ro

Dynamic Adversarial Fine-Tuning Reorganizes Refusal Geometry

arXiv:2604.27019v1 Announce Type: cross Abstract: Safety-aligned language models must refuse harmful requests without collapsing into broad over-refusal, but the training-time mechanisms behind this t

safetyarxiv-cs-cl

Dynamic Cluster Data Sampling for Efficient and Long-Tail-Aware Vision-Language Pre-training

arXiv:2604.27932v1 Announce Type: new Abstract: The computational cost of training a vision-language model (VLM) can be reduced by sampling the training data. Previous work on efficient VLM pre-traini

researcharxiv-cs-cv

Dynamic Scaled Gradient Descent for Stable Fine-Tuning for Classifications

arXiv:2604.27987v1 Announce Type: new Abstract: Fine-tuning pretrained models has become a standard approach to adapting pretrained knowledge to improve the accuracy on new sparse, imbalance datasets.

EAG-PT: Emission-Aware Gaussians and Path Tracing for Diffuse Indoor Scene Reconstruction and Editing

arXiv:2601.23065v2 Announce Type: replace-cross Abstract: Recent radiance-field-based reconstruction methods, such as NeRF and 3DGS, achieve high visual fidelity for indoor scenes, but often break dow

researcharxiv-cs-cv

Agents

Early Detection of Water Stress by Plant Electrophysiology: Machine Learning for Irrigation Management

arXiv:2604.28038v1 Announce Type: new Abstract: Purpose: Fast detection of plant stress is key to plant phenotyping, precision agriculture, and automated crop management. In particular, efficient irri

agentsarxiv-cs-lg

Ease of dependency distance minimization in star-like structures

arXiv:2604.28034v1 Announce Type: new Abstract: The syntactic structure of a sentence can be represented as a tree where edges indicate syntactic dependencies between words. When that structure is a s

researcharxiv-cs-cl

Echo-{alpha}: Large Agentic Multimodal Reasoning Model for Ultrasound Interpretation

arXiv:2604.28011v1 Announce Type: new Abstract: Ultrasound interpretation requires both precise lesion localization and holistic clinical reasoning, yet existing methods typically excel at only one of

local-aiarxiv-cs-cv

Hardware

EdgeFM: Efficient Edge Inference for Vision-Language Models

arXiv:2604.27476v1 Announce Type: new Abstract: Vision-language models (VLMs) have demonstrated strong applicability in edge industrial applications, yet their deployment remains severely constrained

hardwarearxiv-cs-cv

EdgeSpike: Spiking Neural Networks for Low-Power Autonomous Sensing in Edge IoT Architectures

arXiv:2604.27004v1 Announce Type: cross Abstract: We propose EdgeSpike, a co-designed spiking neural network (SNN) framework for autonomous low-power sensing in edge Internet of Things (IoT) architect

EDU-CIRCUIT-HW: Evaluating Multimodal Large Language Models on Real-World University-Level STEM Student Handwritten Solutions

model-releasesarxiv-cs-ai

arXiv:2602.00095v3 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) hold significant promise for revolutionizing traditional education and reducing teachers' workload. H

Effective Prompt Pool Learning for Continual Category Discovery

arXiv:2407.19001v3 Announce Type: replace Abstract: This paper studies effective prompt pool learning for Continual Category Discovery (CCD), a challenging open-world setting where a model must discov

local-aiarxiv-cs-cv

Efficient-DLM: From Autoregressive to Diffusion Language Models, and Beyond in Speed

arXiv:2512.14067v2 Announce Type: replace-cross Abstract: Diffusion language models (dLMs) have emerged as a promising paradigm that enables parallel, non-autoregressive generation, but their learning

Efficient Multivector Retrieval with Token-Aware Clustering and Hierarchical Indexing

arXiv:2604.28142v1 Announce Type: cross Abstract: Multivector retrieval models achieve state-of-the-art effectiveness through fine-grained token-level representations, but their deployment incurs subs

Efficient Preimage Approximation for Neural Network Certification

arXiv:2505.22798v3 Announce Type: replace-cross Abstract: The growing reliance on artificial intelligence in safety- and security-critical applications is raising concerns about the robustness of neur

Tutorials

Efficient Sparse Selective-Update RNNs for Long-Range Sequence Modeling

arXiv:2603.02226v2 Announce Type: replace Abstract: Real-world sequential signals, such as audio or video, contain critical information that is often embedded within long periods of silence or noise.

tutorialsarxiv-cs-lg

Hardware

Efficient Training on Multiple Consumer GPUs with RoundPipe

arXiv:2604.27085v1 Announce Type: cross Abstract: Fine-tuning Large Language Models (LLMs) on consumer-grade GPUs is highly cost-effective, yet constrained by limited GPU memory and slow PCIe intercon

hardwarearxiv-cs-ai

Tutorials

Electrostatic Clutch-Based Mechanical Multiplexer with Increased Force Capability

arXiv:2501.08469v4 Announce Type: replace Abstract: Robotic systems with many degrees of freedom (DoF) are constrained by the demands of dedicating a motor to each joint, and while mechanical multiple

tutorialsarxiv-cs-ro

ELiC: Efficient LiDAR Geometry Compression via Cross-Bit-depth Feature Propagation and Bag-of-Encoders

arXiv:2511.14070v3 Announce Type: replace-cross Abstract: Hierarchical LiDAR geometry compression encodes voxel occupancies from low to high bit-depths, yet prior methods treat each depth independentl

local-aiarxiv-cs-cv

EmDT: Embedding Diffusion Transformer for Tabular Data Generation in Fraud Detection

arXiv:2603.13566v2 Announce Type: replace-cross Abstract: Imbalanced datasets pose a difficulty in fraud detection, as classifiers are often biased toward the majority class and perform poorly on rare

Emotion-Aware Clickbait Attack in Social Media

arXiv:2604.27369v1 Announce Type: new Abstract: Clickbait is characterized by disproportionately high emotional intensity relative to informational content, often reinforced by specific structural pat

researcharxiv-cs-cl

Enabling Reconfiguration-Communication Overlap for Collective Communication in Optical Networks

arXiv:2510.19322v3 Announce Type: replace-cross Abstract: Collective communication (CC) is critical for scaling distributed machine learning (DML). The predictable traffic patterns of DML present a gr

Agents

End-to-end autonomous scientific discovery on a real optical platform

arXiv:2604.27092v1 Announce Type: new Abstract: Scientific research has long been human-led, driving new knowledge and transformative technologies through the continual revision of questions, methods

agentsarxiv-cs-ai

Agents

End-to-End Evaluation and Governance of an EHR-Embedded AI Agent for Clinicians

arXiv:2604.27309v1 Announce Type: new Abstract: Clinical AI systems require not just point-in-time evaluation but continuous governance: the ongoing practice of monitoring, evaluating, iterating, and

agentsarxiv-cs-ai

Applications

Energy-Efficient Plant Monitoring via Knowledge Distillation

arXiv:2604.27178v1 Announce Type: new Abstract: Recent advances in large-scale visual representation learning have significantly improved performance in plant species and plant disease recognition tas

applicationsarxiv-cs-cv