Search: “research”

Accelerated and Stable Convergence with Anchored Optimistic Method

arXiv:2606.21528v1 Announce Type: cross Abstract: We study first-order methods for solving monotone variational inequalities arising in min-max optimization. Classical approaches such as the extragrad

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Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

DGX agent

arXiv:2606.22112v1 Announce Type: new Abstract: The performance of advanced materials for extreme environments is underpinned by their microstructure, including the size and distribution of reinforcin

researcharxiv-cs-cv

23 Jun 2026

ACTIVA: Amortized Causal Effect Estimation via Transformer-based Variational Autoencoder

arXiv:2503.01290v3 Announce Type: replace Abstract: Predicting post-intervention distributions from observational data is central to many scientific and decision-making problems, but remains challengi

Active Sensing and Deferred-Decision Trajectory Optimization for Robust Target Identification

arXiv:2606.22277v1 Announce Type: cross Abstract: We study trajectory optimization in mobile sensing systems that must identify which member of a finite candidate set is the true target, while maintai

Adaptive Beam Selection for Efficient Scanning Probe Tomography

arXiv:2606.21713v1 Announce Type: cross Abstract: In X-ray tomography, reconstruction quality generally improves with larger numbers of projections. However, more projections increase experiment costs

Adaptive Hard-Soft Physics-Informed Neural Networks for Robust Boundary-Constrained PDE Solving

arXiv:2606.23359v1 Announce Type: new Abstract: Physics-informed neural networks (PINNs) provide an effective way to solve partial differential equations (PDEs) by embedding physical principles into t

Adversarial Bandit Optimization with Globally Bounded Perturbations to Convex Losses

arXiv:2606.19891v2 Announce Type: replace Abstract: We study adversarial bandit optimization in which the loss functions may be non-convex and non-smooth. In each round, the learner selects an action

AgroSense 2.0: Cross-Modal Transformer Fusion with Geospatial Raster Integration and Interpretable Multi-Task Learning for Precision Crop Recommendation

arXiv:2606.21892v1 Announce Type: cross Abstract: Crop recommendation systems in precision agriculture have long suffered from a fundamental modality gap: visual soil characterization and chemical nut

AI-Augmented Thyroid Scintigraphy for Robust Classification of Disease

arXiv:2503.00366v3 Announce Type: replace-cross Abstract: Thyroid scintigraphy is vital for diagnosing thyroid disorders, yet deep learning (DL) models in this domain often struggle with limited, imba

Aligning AI-driven discovery with human intuition

arXiv:2410.07397v2 Announce Type: replace Abstract: As data-driven modeling of physical dynamical systems becomes more prevalent, a new challenge is emerging: making these models more compatible and a

All Routes Lead to Collapse

arXiv:2606.22325v1 Announce Type: new Abstract: Attention sinks, representation collapse, and norm stratification are treated as transformer-specific pathologies. We show they are not specific to atte

An approach with Visual and Tabular Mamba to multimodal medical data using Mixed Fusion

arXiv:2606.20738v1 Announce Type: new Abstract: This article presents a complementary approach for integrating multimodal medical data in cancer classification, based on state space models represented

An Empirical Study of OpenPangu Quantization on Ascend NPUs

arXiv:2606.21257v1 Announce Type: new Abstract: OpenPangu models are attractive targets for private and domestic large-language-model deployment, yet their robustness under aggressive post-training qu

An intuitive rearranging of the Yates covariance decomposition for probabilistic verification of forecasts with the Brier score

arXiv:2603.05544v2 Announce Type: replace-cross Abstract: Proper scoring rules are essential for evaluating probabilistic forecasts. We propose a simple algebraic rearrangement of the Yates covariance

Approximating velocity fields with planted attractors via Neural-ODEs for classification purposes

arXiv:2606.23550v1 Announce Type: cross Abstract: In this work, Neural ODEs equipped with a curated collection of equilibrium points have been successfully employed for classification tasks.The plante

Arbor: Explicit Geometric Conditioning for Controllable 3D Asset Generation

arXiv:2606.23514v1 Announce Type: new Abstract: Text and image conditioned 3D models now generate convincing assets, but they still offer little direct control over the space an object should occupy o

Arc-Length Parameterized Interpolating Splines

arXiv:2606.21209v1 Announce Type: cross Abstract: We present an iterative algorithm to compute an arc-length parameterized spline interpolating a set of points. This differs from other methods where t

ARCO-Mars: A Unified Cloud-Optimized Archive of Mars Atmosphere Reanalysis

arXiv:2606.21701v1 Announce Type: cross Abstract: Long-term records of the Martian atmosphere based on general circulation models and reanalysis of atmospheric state variables are important to underst

Artic-O: End-to-End Articulated Object Reconstruction via Latent Geometry Learning

arXiv:2606.21938v1 Announce Type: new Abstract: Reconstructing articulated objects from sparse images requires recovering complete geometry, movable parts, and motion parameters. Recent methods typica

Associative Memory for Non-Stationary Environments: A Self-Sizing Generalization of Hopfield Networks

arXiv:2507.10443v3 Announce Type: replace-cross Abstract: The Hopfield network made associative memory (AM) the model system of neural computation, but it solves the problem only for a stationary worl

Asymptotic Signal Subspace Recovery in Softmax Attention Models

arXiv:2606.22406v1 Announce Type: new Abstract: Attention mechanisms have demonstrated remarkable empirical success in identifying relevant information from large collections of tokens, yet the theore

Attention at Rest Stays at Rest: Breaking Visual Inertia for Cognitive Hallucination Mitigation

arXiv:2604.01989v3 Announce Type: replace Abstract: Like a body at rest that stays at rest, we find that visual attention in multimodal large language models (MLLMs) exhibits pronounced inertia, remai

Attention mechanism for scalable mesh-based neural surrogates of free-surface fluids

arXiv:2606.23251v1 Announce Type: cross Abstract: High-fidelity simulations of free-surface flows using Lagrangian methods such as the Particle Finite Element Method (PFEM) are computationally demandi

Attention-Spectrum Regularization for Replay-Free Continual Multimodal LLMs

arXiv:2606.23063v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) are increasingly required to adapt to non-stationary streams of visual domains, question types, and user instru

Automated sign detection across the Electronic Babylonian Library: A large-scale dataset and end-to-end cuneiform OCR pipeline

arXiv:2606.22608v1 Announce Type: new Abstract: Learning to read cuneiform tablets is an extremely demanding task; consequently, of the roughly half million excavated tablets, only a small fraction ha

AwakeForest: An Interactive Geospatial Platform for Large-Scale Forest Imagery

arXiv:2606.23542v1 Announce Type: new Abstract: Forest imagery analysis often involves multiple tightly coupled vision tasks, which must be performed under substantial variation in geographic regions,

Beyond Damage Assessment: Recyclable Material Detection in Aerial Disaster Imagery Using a Lightweight Patch-Based Framework

arXiv:2606.21279v1 Announce Type: new Abstract: Nowadays, more and more disasters of different natures are appearing. Several disaster assessment approaches have been developed in order to identify da

Beyond Flat Labels: Level-Restricted Contrastive Learning for Hierarchical Fine-Grained Vision Classification

arXiv:2606.21838v1 Announce Type: new Abstract: Multimodal contrastive learning has enabled zero-shot visual classification by aligning images with textual categories. However, in hierarchically struc

Beyond Importance: Interchange-Sobol Sensitivity Reveals Task-Specific Content Channels in Transformer Components

arXiv:2606.20678v1 Announce Type: cross Abstract: Mechanistic interpretability methods summarize a transformer component by a single importance score, conflating two distinct roles: a component may ma

Beyond ROC-AUC: Operating-Point Performance Reporting for Biometric Verification

arXiv:2606.20680v1 Announce Type: new Abstract: A biometric verifier is often deployed with a strict false match budget, so only a narrow, low false match rate (FMR) slice of the score range is used.

Beyond the Next Step: Variable-Length Latent World Models for Long-Horizon Planning

arXiv:2606.21775v1 Announce Type: new Abstract: Recently, world models have emerged as a promising paradigm for building intelligent agents by learning predictive models that estimate future environme

Biological Sex Determination in Cadavers Using Deep Learning Algorithms from Computed Tomography Images of Pelvis and Skull

arXiv:2606.22515v1 Announce Type: new Abstract: Sexual identification of decomposed cadavers challenges traditional methods dependent on visual anthropological analysis. This study evaluates state-of-

BioMatrix: Towards a Comprehensive Biological Foundation Model Spanning the Modality Matrix of Sequences, Structures, and Language

arXiv:2606.22138v1 Announce Type: cross Abstract: We present BioMatrix, the first multimodal foundation model that natively integrates sequences, structures, and natural language for both molecules an

BIT-Nav: Brain-Inspired Trajectory Memory for Embodied Navigation

arXiv:2606.21398v1 Announce Type: new Abstract: Vision-Language Models (VLMs) for embodied navigation rely on selecting a fixed number of frames from a growing trajectory history. As episodes extend,

Boosting Neural Video Codec via Scale-Driven Online Flow Refinement

arXiv:2606.23023v1 Announce Type: new Abstract: Although state-of-the-art neural video codecs (NVCs) have achieved remarkable performance, they suffer from limited generalization when encountering com

Boundary-by-Mask: Few-Shot Instance Segmentation with Mask-Conditioned Boundary Learning for Texture-Poor Industrial Parts

arXiv:2606.21594v1 Announce Type: new Abstract: Recent advances in large pre-trained models have led to remarkable progress in instance segmentation on general images. However, industrial scenarios re

Bracing for Impact: Robust Humanoid Push Recovery and Locomotion with Reduced Order Models

arXiv:2505.11495v2 Announce Type: replace Abstract: Push recovery during locomotion will facilitate the deployment of humanoid robots in human-centered environments. In this paper, we present a unifie

Brain-Adapter: A Dual-Stream Vision-Language MIL Framework for Comprehensive 3D CT Diagnosis of Acute Intracranial Pathologies

arXiv:2606.23494v1 Announce Type: new Abstract: Automated diagnosis of 3D brain CT scans is essential for critical care, yet it remains challenging due to the heavy reliance on manual annotations and

Bridging Multi-Valued Heuristics and Dimensionality Reduction in Multi-Objective Search

arXiv:2606.20644v1 Announce Type: cross Abstract: Multi-objective shortest-path (MOSP) algorithms traditionally rely on single-valued heuristics (SVHs), which associate each state with a single admiss

Bridging Semantics and Kinematics: A Modular Framework for Zero-Shot Robotic Manipulation

arXiv:2606.23157v1 Announce Type: new Abstract: This paper presents a modular training-free framework for zero-shot, language-guided robotic manipulation in semi-structured environments. The architect

C^2GR: Coupled Comprehensive Generative Replay for a Continually Learnable Universal Segmentation Model

arXiv:2606.23473v1 Announce Type: new Abstract: Universal segmentation models exhibit significant potential for diverse tasks involving different imaging modalities and segmentation objectives. Task-I

Calibrated Sampling-Free Uncertainty Estimation in Bayesian Deep Learning

arXiv:2606.16214v3 Announce Type: replace Abstract: Modern deep learning models remain notoriously prone to overconfidence, limiting their reliability in high-stakes applications. Bayesian methods aim

Can Reasoning Models Detect Changes to their Chains of Thought?

arXiv:2606.22085v1 Announce Type: cross Abstract: There are many reasons one may want to edit a model's chain of thought (CoT) -- e.g., to prefill it with reasoning from a stronger model or to remove

Can Single-View Mesh Reconstruction Generalize to Robot Camera Rotation?

arXiv:2606.22987v1 Announce Type: new Abstract: Single-view mesh reconstruction predicts object meshes and spatial layouts from a single observation, making it attractive for fast robot spatial reason

Category-Adaptive Cross-Modal Semantic Refinement and Transfer for Open-Vocabulary Multi-Label Recognition

arXiv:2412.06190v2 Announce Type: replace Abstract: Benefiting from the generalization capability of CLIP, recent vision language pre-training (VLP) models have demonstrated the ability to capture a w

Causal Reward World Models: Zero-shot Reward Design for Automated Skill Generation

arXiv:2606.23280v1 Announce Type: new Abstract: Automated Reward Design (ARD) aims to replace manual reward engineering in reinforcement learning with language-driven reward function synthesis. Howeve

Causal Variational Deep Embedding: A Family of Interventional Generators for Confounded Images

arXiv:2606.21806v1 Announce Type: new Abstract: Deep generative models reproduce the observational distribution of their training data, inheriting any spurious associations it contains. A common sourc

Central limit theorem for the averaged Adam optimizer

arXiv:2606.21433v1 Announce Type: cross Abstract: In this article, we analyse convergence of the averaged Adam optimizer to an attracting zero of the Adam vector field. We provide a central limit theo