research

When Can Depth Replace Precision? A Resource Theory of Quantized Neural Computation

arXiv:2607.23390v1 Announce Type: new Abstract: When can additional low-bit residual computation replace missing numerical precision for a fixed input-output map? We model a quantized residual system

Research

When Can You Correct Distribution Drift in Temporal Graph Generation? A Sharpening--Drift Tension and an Impossibility for Observation-Based Correction

DGX agent

arXiv:2607.24662v1 Announce Type: new Abstract: Generative models of temporal graphs are trained on one stretch of an evolving network and deployed on the next, and they degrade badly in the gap. We s

researcharxiv-cs-lg

28 Jul 2026

When Rates Are Geometric: Rate-Certificate Transfer for Contact Splittings in Optimization

arXiv:2607.23642v1 Announce Type: cross Abstract: Discrete optimization algorithms are often analyzed through continuous-time limiting ODEs, but a convergence certificate for the ODE is not automatica

Which Workloads Belong in Orbit? A Workload-First Framework for Orbital Data Centers Using Semantic Abstraction

arXiv:2603.20317v2 Announce Type: replace Abstract: Space-based compute is becoming plausible as launch costs fall and data-intensive AI workloads grow. This paper proposes a workload-centric framewor

Why does Greedy Search produce Optimal Clustering Outcomes? A Fixed-Core Assignment Theory

arXiv:2607.24237v1 Announce Type: new Abstract: Many existing clustering methods are designed based on a set-oriented definition---a cluster is a set of similar points---relying a point-to-point simil

Wired for Overconfidence: A Mechanistic Perspective on Inflated Verbalized Confidence in LLMs

arXiv:2604.01457v3 Announce Type: replace Abstract: Large language models are often not just wrong, but confidently wrong: when they produce factually incorrect answers, they tend to verbalize overly

WISERouter: LLM Routing with Workload Budget Constraint

arXiv:2607.23765v1 Announce Type: cross Abstract: Large language models (LLMs) achieve impressive performance across multiple domains, but using the most capable model for every query is prohibitive a

researcharxiv-cs-ai

Wrong Design Intent Is Worse Than None: A Derangement-Control Diagnosis of Header Conditioning in CAD Program Completion

arXiv:2607.23191v1 Announce Type: new Abstract: Fine-tuned code LLMs can be conditioned on a lightweight design-intent header to steer parametric CAD generation, but whether the model actually reads t

XMix: Combating Extremely Noisy Labels via Local Smoothness in Self-Supervised Feature Space

arXiv:2607.23865v1 Announce Type: new Abstract: Supervised deep learning models rely on large, accurately labeled datasets, yet noisy annotations are often unavoidable and can severely degrade perform

A Dual Path Framework with Hotspot Guided Fusion for Three Dimensional CT to PET Synthesis in Head and Neck Cancer

arXiv:2607.21800v1 Announce Type: cross Abstract: 18F-FDG PET/CT plays a central role in staging, treatment planning, and response assessment for head and neck cancer by providing functional informati

A Factorial Study of Synthetic Data Generation for Low-Resource Machine Translation using Grammar Books

arXiv:2607.22376v1 Announce Type: new Abstract: Most endangered languages lack the parallel data required for machine translation, despite the existence of descriptive grammar books. We introduce a pi

A Framework for Individual Tree Growth Reconstruction Using Multi-Platform Laser Scanning

arXiv:2607.22129v1 Announce Type: new Abstract: Accurate tree-level forest monitoring using laser scanning data requires reliable tree delineation, consistent tree correspondence across multitemporal

A Leakage-Free Stacked Ensemble Method for Multiclass Classification

arXiv:2607.22081v1 Announce Type: new Abstract: Multiclass classification is a fundamental problem across a wide range of domains. It is still challenging due to possession of high inter-class similar

A Smooth Phase-Separation Model for Weak-Boundary Segmentation of Homogeneous Structures

arXiv:2607.22053v1 Announce Type: new Abstract: Segmentation of adjacent structures with similar intensity distributions remains a challenging problem in image analysis, particularly when object bound

ACME: A Multi-Cultural, Multi-Embodiment Social-Navigation Dataset

arXiv:2607.21964v1 Announce Type: new Abstract: Understanding how robots and humans move in shared spaces is essential for designing effective social robot navigation policies and predicting human beh

researcharxiv-cs-ro

AdamNX: An Adam improvement algorithm based on a novel exponential decay mechanism for the second-order moment estimate

arXiv:2511.13465v5 Announce Type: replace Abstract: This paper studies the exponential decay mechanism of the second-moment estimate in Adam. We propose AdamNX and a time-varying second-moment decay r

An Explainable FFT-Based Spatial-Frequency Fusion Framework for Deepfake Detection

arXiv:2607.17441v1 Announce Type: cross Abstract: Deepfake generation has raised growing concerns regarding digital media authenticity, misinformation, identity fraud, and public trust. Recent studies

An Insight on Evaluation Metrics Under the Imbalanced Case of Anomaly Detection

arXiv:2607.22286v1 Announce Type: new Abstract: Anomaly detection is inherently characterised by severe class imbalance, making the interpretation of evaluation metrics challenging. Although metrics s

Analyzing Toxic Behavior and Its Impact on the Mastodon Community

arXiv:2607.21980v1 Announce Type: new Abstract: Mastodon as a decentralized federation of independently moderated social servers poses unique challenges for the detection and mitigation of toxic conte

Ask for More Than Bayes Optimal: A Theory of Indecisions for Selective Hypothesis Testing

arXiv:2412.12807v4 Announce Type: replace-cross Abstract: Selective classification is a powerful tool for automated decision-making in high-risk scenarios, allowing classifiers to act only when confid

Atlas 2 -- Foundation models for clinical deployment

arXiv:2601.05148v2 Announce Type: replace Abstract: Pathology foundation models substantially advanced the possibilities in computational pathology --- yet tradeoffs in terms of performance, robustnes

Automatic Map Density Selection for Locally-Performant Visual Place Recognition

arXiv:2602.21473v3 Announce Type: replace Abstract: A key challenge in translating Visual Place Recognition (VPR) from the lab to long-term deployment is ensuring a priori that a system can meet user-

Beyond Binary Rooftop Mapping: A Four-Class Deep Learning Framework for Green Roof Potential Assessment from Open Swiss Geospatial Data

arXiv:2607.22342v1 Announce Type: new Abstract: The development of effective urban climate adaptation strategies requires comprehensive spatial information on rooftops and buildings, since such inform

Beyond Negative-Ridge Endpoints: Mixed-Sign Spectral Regularization via Negative-Shifted Gradient Descent

arXiv:2607.22474v1 Announce Type: new Abstract: In overparameterized linear regression, many weak spectral directions act like a ridge penalty on the signal-bearing spectrum; negative ridge is the nat

CARE: Anti-entanglement Ultrasound Image Segmentation via Channel-Aware Region Extrication

arXiv:2508.13899v2 Announce Type: replace Abstract: Accurate ultrasound image segmentation is fundamentally challenged by target-context entanglement, where lesion cues are easily mixed with surroundi

Carpe Diem: Critical Learning Period-Aware Contract-Based Incentives for Federated Learning

arXiv:2503.07869v4 Announce Type: replace Abstract: Critical learning periods (CLPs) in federated learning (FL) refer to early stages during which low-quality contributions (e.g., sparse training data

Class-Balanced Softmax: A Bayes Theory-Based Method for Long-Tailed Recognition

arXiv:2607.22258v1 Announce Type: cross Abstract: Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks. However, their performanc

Complexity Bounds and Approaches to Learning Projected Gradient Descent Solver Iterates

arXiv:2607.22467v1 Announce Type: new Abstract: Data scarcity poses a fundamental challenge in training generative models to produce initial guesses for parametric optimization problems that are other

Computer Vision Based Neurology Brain Activity Rejection Architecture and Implementation

arXiv:2607.21654v1 Announce Type: cross Abstract: The electroencephalogram (EEG) is a valuable and widely applied tool for investigating brain disorders and behavioral changes. It offers a minimally r

Correlation-Aware and Gaussianity-Preserving Robust Latent Angular Watermarking for Diffusion Models

arXiv:2607.22386v1 Announce Type: new Abstract: Latent domain watermarking for diffusion models embeds watermarks directly into the latent prior, enjoying non-intrusiveness to model parameters and sea

Data eccentricity, asymptotics of Gaussian RBF reproducing kernel Hilbert space, and kernel PCA

arXiv:2607.21823v1 Announce Type: new Abstract: We show that, up to isotropic scaling, the Gaussian RBF reproducing kernel Hilbert space (RKHS) is asymptotically isometric to Euclidean space in the la

Deep Sigma Point Processes for RCS Modeling in Spaceborne SAR Imagery

arXiv:2607.21745v1 Announce Type: cross Abstract: Radar cross-section (RCS) modeling is foundational to advancing the utility and sensitivity of spaceborne radar systems. This study introduces a deep

Developing and Validating the Spanish Version of the Large Language Models Dependency Scale (LLM-D12-SP)

arXiv:2607.22041v1 Announce Type: new Abstract: There is a growing need for reliable and culturally validated instruments to assess psychological dependency on large language models (LLMs), particular

Diffusion Models in Medical Image Inpainting: Challenges, Solution Taxonomy, and Future Directions

arXiv:2607.21904v1 Announce Type: cross Abstract: Image inpainting aims to reconstruct missing or corrupted regions of an image while preserving as much as possible, visual and semantic consistency. I

Distributional Determinantal Point Process for Repulsive Clustering of Distributions

arXiv:2607.21847v1 Announce Type: cross Abstract: We introduce the distributional determinantal point process (dDPP) as a novel repulsive point process whose atoms are probability distributions rather

dRAE: Representation Autoencoder with Hyper-Spherical Codes

arXiv:2607.22148v1 Announce Type: new Abstract: In this work, we aim to discretize the high-dimensional visual representations to bridge the gap with language models - a non-trivial challenge, as exis

Dysphagia Risk Stratification in Head and Neck Cancer via Two-Stage PRO-Clinical Stacking

arXiv:2607.22514v1 Announce Type: new Abstract: Dysphagia is a debilitating late effect of head and neck cancer (HNC) treatment, yet timely identification of at-risk patients remains challenging in su

Efficient Online LLM Watermark Detection via Rao-Blackwellized E-Processes

arXiv:2607.21958v1 Announce Type: cross Abstract: As large language models (LLMs) are increasingly deployed, reliable and efficient mechanisms for distinguishing AI-generated text from human-written c

Encoding orders and trees in real-valued functions

arXiv:2607.21761v1 Announce Type: cross Abstract: We prove function-theoretic analogues of a quantitative result of Hodges on extracting the order property from a sufficiently large 2-tree coded in a

Entanglement geometry separates circuit cutting, classical hardness, and trainability

arXiv:2607.17872v2 Announce Type: replace-cross Abstract: Circuit cutting promises to scale quantum computations beyond current hardware, but variational quantum advantage also requires low cutting ov

Evolution-Aware MSA Reasoning for Subsampling via Factor Graphs

arXiv:2607.22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoida

FAIR: Feature-Augmented Implicit Regularization for AI-generated Fake Image Detection

arXiv:2607.22087v1 Announce Type: new Abstract: Generalization remains a critical bottleneck in AI-generated image detection. Because many modern generators are proprietary or adversarially modified,

From Isolated Tasks to Structured Capabilities: A Multilayer Taxonomy for Large Language Models

arXiv:2607.22182v1 Announce Type: new Abstract: Large language model (LLM) evaluation spans diverse tasks and benchmarks, yet evidence remains organized around tasks rather than the capabilities they

From level set evolution to threshold optimization: A grayscale level set framework for image segmentation

arXiv:2607.22255v1 Announce Type: new Abstract: The segmentation of multiple degradations has been a challenging problem in the field of image segmentation. Existing level set approaches commonly adop

From Perturbation Correction to Geometry-Aware Sampling: Sharpness-Guided Equilibrium Sampling for Balanced Flat Minima in Long-Tailed Learning

arXiv:2607.21999v1 Announce Type: new Abstract: Long-tailed learning couples two sources of poor generalization: head classes dominate training exposure, while under-represented classes often converge

From Score Approximation to Distribution Approximation in Score-Based Diffusion Models

arXiv:2607.22199v1 Announce Type: new Abstract: Score-based diffusion models have achieved remarkable empirical success in generative modeling, yet their approximation-theoretic foundations remain inc

General Value Functions for Remaining Useful Life and Failure-Mode Prediction

arXiv:2607.22268v1 Announce Type: cross Abstract: Remaining useful life (RUL) prediction and failure-mode classification are central tasks in predictive maintenance. Many data-driven pipelines use fix

Generalized Gaussian Temporal Difference Error for Uncertainty-aware Reinforcement Learning

arXiv:2408.02295v4 Announce Type: replace Abstract: Conventional uncertainty-aware temporal difference (TD) learning often models TD errors as zero-mean Gaussian. This assumption can miss the heavy-ta