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Hybrid Multi-Dimensional MRI Prostate Cancer Detection via Hadamard Network-Based Bias Correction and Residual Networks

arXiv:2604.17107v1 Announce Type: new Abstract: Magnetic Resonance Imaging (MRI) is vital for prostate cancer (PCa) diagnosis. While advanced techniques such as Hybrid Multi-dimensional MRI (HM-MRI) h

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Hybrid Quantum Neural Networks for Enhanced Breast Cancer Thermographic Classification: A Novel Quantum-Classical Integration Approach

DGX agent

arXiv:2604.16953v1 Announce Type: cross Abstract: Breast cancer diagnosis through thermographic image analysis remains a critical challenge in medical AI, with classical deep learning approaches facin

applicationsarxiv-cs-cv

21 Apr 2026

Hybrid Spectro-Temporal Fusion Framework for Structural Health Monitoring

arXiv:2604.16589v1 Announce Type: new Abstract: Structural health monitoring plays a critical role in ensuring structural safety by analyzing vibration responses from engineering systems. This paper p

safetyarxiv-cs-lg

Hybrid-Vector Retrieval for Visually Rich Documents: Combining Single-Vector Efficiency and Multi-Vector Accuracy

arXiv:2510.22215v2 Announce Type: replace-cross Abstract: Retrieval over visually rich documents is essential for tasks such as legal discovery, scientific search, and enterprise knowledge management.

HyKey: Hyperspectral Keypoint Detection and Matching in Minimally Invasive Surgery

arXiv:2604.17446v1 Announce Type: new Abstract: Purpose: 3D reconstruction in minimally invasive surgery (MIS) enables enhanced surgical guidance through improved visualisation, tool tracking, and aug

Hyperbolic Enhanced Representation Learning for Incomplete Multi-view Clustering

arXiv:2604.16959v1 Announce Type: cross Abstract: Incomplete Multi-View Clustering (IMVC) faces the challenge of learning discriminative representations from fragmentary observations while maintaining

Hyperspectral Unmixing Hierarchies

arXiv:2604.16969v1 Announce Type: new Abstract: Unmixing reveals the spatial distribution and spectral details of different constituents, called endmembers, in a hyperspectral image. Because unmixing

ICAT: Incident-Case-Grounded Adaptive Testing for Physical-Risk Prediction in Embodied World Models

arXiv:2604.16405v1 Announce Type: cross Abstract: Video-generative world models are increasingly used as neural simulators for embodied planning and policy learning, yet their ability to predict physi

IceBreaker for Conversational Agents: Breaking the First-Message Barrier with Personalized Starters

arXiv:2604.18375v1 Announce Type: new Abstract: Conversational agents, such as ChatGPT and Doubao, have become essential daily assistants for billions of users. To further enhance engagement, these sy

safetyarxiv-cs-cl

ICLAD: In-Context Learning with Comparison-Guidance for Audio Deepfake Detection

arXiv:2604.16749v1 Announce Type: cross Abstract: Audio deepfakes pose a significant security threat, yet current state-of-the-art (SOTA) detection systems do not generalize well to realistic in-the-w

Identifying Ethical Biases in Action Recognition Models

arXiv:2604.17971v1 Announce Type: new Abstract: Human Action Recognition (HAR) models are increasingly deployed in high-stakes environments, yet their fairness across different human appearances has n

IDOBE: Infectious Disease Outbreak forecasting Benchmark Ecosystem

model-releasesarxiv-cs-lg

arXiv:2604.18521v1 Announce Type: new Abstract: Epidemic forecasting has become an integral part of real-time infectious disease outbreak response. While collaborative ensembles composed of statistica

iDocV2: Leveraging Self-Supervision and Open-Set Detection for Improving Pattern Spotting in Historical Documents

arXiv:2604.16726v1 Announce Type: new Abstract: Considering the imminent massification of digital books, it has become critical to facilitate searching collections through graphical patterns. Current

IMA-MoE: An Interpretable Modality-Aware Mixture-of-Experts Framework for Characterizing the Neurobiological Signatures of Binge Eating Disorder

arXiv:2604.17028v1 Announce Type: new Abstract: Binge eating disorder (BED) is the most prevalent eating disorder. However, current diagnostic frameworks remain largely grounded in symptom-based crite

Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations

arXiv:2604.18083v1 Announce Type: new Abstract: Reconstructing continuous environmental fields from sparse and irregular observations remains a central challenge in environmental modelling and biodive

safetyarxiv-cs-lg

ImpRIF: Stronger Implicit Reasoning Leads to Better Complex Instruction Following

arXiv:2602.21228v2 Announce Type: replace Abstract: As applications of large language models (LLMs) become increasingly complex, the demand for robust complex instruction following capabilities is gro

Improving Dynamic Object Interactions in Text-to-Video Generation with AI Feedback

arXiv:2412.02617v2 Announce Type: replace-cross Abstract: Large text-to-video models hold immense potential for a wide range of downstream applications. However, they struggle to accurately depict dyn

Improving LLM Code Reasoning via Semantic Equivalence Self-Play with Formal Verification

model-releasesarxiv-cs-cl

arXiv:2604.17010v1 Announce Type: new Abstract: We introduce a self-play framework for semantic equivalence in Haskell, utilizing formal verification to guide adversarial training between a generator

Improving Radio Interferometry Imaging by Explicitly Modeling Cross-Domain Consistency in Reconstruction

arXiv:2604.16794v1 Announce Type: new Abstract: Radio astronomy plays a crucial role in understanding the universe, particularly within the realm of non-thermal astrophysics. Images of celestial objec

Improving reproducibility by controlling random seed stability in machine learning based estimation via bagging

arXiv:2604.17694v1 Announce Type: cross Abstract: Predictions from machine learning algorithms can vary across random seeds, inducing instability in downstream debiased machine learning estimators. We

researcharxiv-cs-lg

Improving Speech Recognition of Named Entities in Classroom Speech with LLM Revision and Phonetic-Semantic Context

arXiv:2506.10779v2 Announce Type: replace Abstract: Classroom speech and lectures often contain named entities (NEs) such as names of people and special terminology. While automatic speech recognition

Applications

In-Context Learning Under Regime Change

arXiv:2604.16988v1 Announce Type: new Abstract: Non-stationary sequences arise naturally in control, forecasting, and decision-making. The data-generating process shifts at unknown times, and models m

applicationsarxiv-cs-lg

In-Context Symbolic Regression for Robustness-Improved Kolmogorov-Arnold Networks

model-releasesarxiv-cs-lg

arXiv:2603.15250v2 Announce Type: replace Abstract: Symbolic regression aims to replace black-box predictors with concise analytical expressions that can be inspected and validated in scientific machi

In Search of Lost DNA Sequence Pretraining

arXiv:2604.16570v1 Announce Type: new Abstract: DNA sequence encoding is fundamental to gene function prediction, protein synthesis, and diverse downstream biological tasks. Despite the substantial pr

researcharxiv-cs-lg

In Situ Training of Implicit Neural Compressors for Scientific Simulations via Sketch-Based Regularization

arXiv:2511.02659v3 Announce Type: replace Abstract: Focusing on implicit neural representations, we present a novel in situ training protocol that employs limited memory buffers of full and sketched d

researcharxiv-cs-lg

Incentivizing Parametric Knowledge via Reinforcement Learning with Verifiable Rewards for Cross-Cultural Entity Translation

arXiv:2604.16881v1 Announce Type: new Abstract: Cross-cultural entity translation remains challenging for large language models (LLMs) as literal or phonetic renderings are usually yielded instead of

IncepDeHazeGAN: Novel Satellite Image Dehazing

arXiv:2604.16609v1 Announce Type: new Abstract: Dehazing is a technique in computer vision for enhancing the visual quality of images captured in cloudy or foggy conditions. Dehazing helps to recover

Incoherent Deformation, Not Capacity: Diagnosing and Mitigating Overfitting in Dynamic Gaussian Splatting

arXiv:2604.16747v1 Announce Type: new Abstract: Dynamic 3D Gaussian Splatting methods achieve strong training-view PSNR on monocular video but generalize poorly: on the D-NeRF benchmark we measure an

IncreFA: Breaking the Static Wall of Generative Model Attribution

arXiv:2604.17736v1 Announce Type: new Abstract: As AI generative models evolve at unprecedented speed, image attribution has become a moving target. New diffusion, adversarial and autoregressive gener

Tutorials

Incremental learning for audio classification with Hebbian Deep Neural Networks

arXiv:2604.18270v1 Announce Type: cross Abstract: The ability of humans for lifelong learning is an inspiration for deep learning methods and in particular for continual learning. In this work, we app

tutorialsarxiv-cs-lg

Inductive Convolution Nuclear Norm Minimization for Tensor Completion with Arbitrary Sampling

arXiv:2604.17001v1 Announce Type: new Abstract: The recently established Convolution Nuclear Norm Minimization (CNNM) addresses the problem of extit{tensor completion with arbitrary sampling} (TCAS),

Inertia in Moral and Value Judgments of Large Language Models

arXiv:2408.09049v3 Announce Type: replace Abstract: Large Language Models (LLMs) behave non-deterministically, and prompting has become a common method for steering their outputs. A popular strategy i

safetyarxiv-cs-cl

Inference-Time Temporal Probability Smoothing for Stable Video Segmentation with SAM2 under Weak Prompts

arXiv:2604.17115v1 Announce Type: new Abstract: Interactive video segmentation models such as SAM2 have demonstrated strong generalization across diverse visual domains. However, under weak user super

Inflated Excellence or True Performance? Rethinking Medical Diagnostic Benchmarks with Dynamic Evaluation

model-releasesarxiv-cs-cl

arXiv:2510.09275v2 Announce Type: replace Abstract: Medical diagnostics is a high-stakes and complex domain that is critical to patient care. However, current evaluations of large language models (LLM

Information Representation Fairness in Long-Document Embeddings: The Peculiar Interaction of Positional and Language Bias

arXiv:2601.16934v2 Announce Type: replace Abstract: To be discoverable in an embedding-based search process, each part of a document should be reflected in its embedding representation. To quantify an

safetyarxiv-cs-cl

Infrastructure-Centric World Models: Bridging Temporal Depth and Spatial Breadth for Roadside Perception

arXiv:2604.17651v1 Announce Type: new Abstract: World models, generative AI systems that simulate how environments evolve, are transforming autonomous driving, yet all existing approaches adopt an ego

Injecting Structured Biomedical Knowledge into Language Models: Continual Pretraining vs. GraphRAG

model-releasesarxiv-cs-cl

arXiv:2604.16422v1 Announce Type: new Abstract: The injection of domain-specific knowledge is crucial for adapting language models (LMs) to specialized fields such as biomedicine. While most current a

Instant Colorization of Gaussian Splats

arXiv:2604.17155v1 Announce Type: new Abstract: Gaussian Splatting has recently become one of the most popular frameworks for photorealistic 3D scene reconstruction and rendering. While current raster

Instinct vs. Reflection: Unifying Token and Verbalized Confidence in Multimodal Large Models

arXiv:2604.17274v1 Announce Type: new Abstract: Multimodal Large Language Models (MLLMs) have demonstrated exceptional capabilities in various perception and reasoning tasks. Despite this success, ens

Agents

Instruction-as-State: Environment-Guided and State-Conditioned Semantic Understanding for Embodied Navigation

arXiv:2604.18223v1 Announce Type: new Abstract: Vision-and-Language Navigation requires agents to follow natural-language instructions in visually changing environments. A central challenge is the dyn

agentsarxiv-cs-cv

Integrated Wheel Sensor Communication using ESP32 -- A Contribution towards a Digital Twin of the Road System

arXiv:2509.04061v2 Announce Type: replace Abstract: While current onboard state estimation methods are adequate for most driving and safety-related applications, they do not provide insights into the

safetyarxiv-cs-ro

Applications

Integrating Feature Selection and Machine Learning for Nitrogen Assessment in Grapevine Leaves using In-Field Hyperspectral Imaging

arXiv:2507.17869v3 Announce Type: replace-cross Abstract: Nitrogen (N) is one of the most critical nutrients in winegrape production, influencing vine vigor, fruit composition, and wine quality. Becau

applicationsarxiv-cs-cv

INTENT: Invariance and Discrimination-aware Noise Mitigation for Robust Composed Image Retrieval

arXiv:2604.18051v1 Announce Type: new Abstract: Composed Image Retrieval (CIR) is a challenging image retrieval paradigm that enables to retrieve target images based on multimodal queries consisting o

Inter-Agent Relative Representations for Multi-Agent Option Discovery

arXiv:2512.24827v3 Announce Type: replace Abstract: Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the

safetyarxiv-cs-lg

Interdisciplinary Workshop on Mechanical Intelligence: Summary Report

arXiv:2604.16381v1 Announce Type: new Abstract: This report provides a summary of the outcomes of the Interdisciplinary Workshop on Mechanical Intelligence held in 2024. Mechanical Intelligence (MI) r

researcharxiv-cs-ro

InternScenes: A Large-scale Simulatable Indoor Scene Dataset with Realistic Layouts

arXiv:2509.10813v3 Announce Type: replace Abstract: The advancement of Embodied AI heavily relies on large-scale, simulatable 3D scene datasets characterized by scene diversity and realistic layouts.

Interpolating Discrete Diffusion Models with Controllable Resampling

model-releasesarxiv-cs-lg

arXiv:2604.17310v1 Announce Type: new Abstract: Discrete diffusion models form a powerful class of generative models across diverse domains, including text and graphs. However, existing approaches fac

Applications

Introducing the O-Value: A Universal Standardization for Confusion-Matrix-Based Classification Performance Metrics

arXiv:2505.07033v2 Announce Type: replace-cross Abstract: Many classification performance metrics exist, each suited to a specific application. However, these metrics often differ in scale and can exh

applicationsarxiv-cs-lg