Search: “applications”

SeqRoute: Global Budget-Aware Sequential LLM Routing via Offline Reinforcement Learning

arXiv:2605.25424v1 Announce Type: cross Abstract: Existing LLM routing frameworks treat queries as independent events, neglecting the sequential nature of real-world user sessions constrained by globa

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Applications

Simulating Human Memory with Language Models

DGX agent

arXiv:2605.25680v1 Announce Type: cross Abstract: Language models are increasingly being deployed as user simulators, but their memory is far more reliable than that of real users. To measure this gap

applicationsarxiv-cs-ai

26 May 2026

SPACE: Unifying Symmetric and Asymmetric Routing Problems for Generalist Neural Solver

arXiv:2605.24484v1 Announce Type: new Abstract: Generalist neural routing solvers have shown great potential in solving diverse vehicle routing problems (VRPs) with a unified model. However, existing

StrTransformer: Source-Wise Structured Transformers for Unsupervised Blind Source Recovery

arXiv:2605.25648v1 Announce Type: cross Abstract: This paper proposes StrTransformer, a source-wise structured Transformer framework for blind source recovery and branch-wise latent modeling. Instead

Temporal Concept Drift in Legal Judgment Prediction: Neural Baselines Across Three Epochs of Ukrainian Court Decisions

arXiv:2605.24452v1 Announce Type: cross Abstract: Legal NLP benchmarks evaluate models on randomly split data, implicitly assuming that legal language is stationary. We test this assumption by fine-tu

The Meme Is the Message: Generative Memesis and AI Visuals in the 2024 USA Presidential Elections

arXiv:2411.00934v2 Announce Type: replace-cross Abstract: Visual content on social media has become increasingly influential in shaping political discourse and civic engagement, but it also limits par

Towards a Universal Causal Reasoner

arXiv:2605.24873v1 Announce Type: cross Abstract: Despite the importance of causal reasoning, training LLMs to reason causally remains underexplored. Existing data efforts mostly focus on benchmarking

Transformers over-extend what humans underlearn: the case of Spanish L-shaped morphome

arXiv:2507.21556v3 Announce Type: replace Abstract: The cognitive reality of irregular morphological patterns has been debated for decades: do speakers extend them to novel forms, or are they lexical

Translators as Invisible Teachers of AI: Copyright, Translation Memory, and the Political Economy of Linguistic Data

arXiv:2605.24842v1 Announce Type: new Abstract: This paper examines how the labour of translators has been transformed into foundational data capital for the age of artificial intelligence (AI). Trans

Unbalanced Incomplete Multi-view Clustering via the Scheme of View Evolution: Weak Views are Meat; Strong Views do Eat

arXiv:2011.10254v3 Announce Type: replace-cross Abstract: Incomplete multi-view clustering is an important technique to deal with real-world incomplete multi-view data. Previous works assume that all

Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval

arXiv:2605.24530v1 Announce Type: new Abstract: Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities. Traditional text-based approache

VectorArk: Learning Practical Image Vectorization with Rounded Polygon Representation

arXiv:2605.24398v1 Announce Type: cross Abstract: Recent vision-language model (VLM)-based approaches have achieved impressive results on image vectorization tasks. However, they are typically evaluat

WhisTLE: Deeply Supervised, Text-Only Domain Adaptation for Pretrained Speech Recognition Transformers

arXiv:2509.10452v2 Announce Type: replace Abstract: Pretrained automatic speech recognition (ASR) models such as Whisper perform well but still need domain adaptation to handle unseen parlance. In man

WISE: Web Information Satire and Fakeness Evaluation

arXiv:2512.24000v3 Announce Type: replace Abstract: Distinguishing fake or untrue news from satire or humor poses a unique challenge due to their overlapping linguistic features and divergent intent.

ARES: Automated Rubric Synthesis for Scalable LLM Reinforcement Learning

arXiv:2605.23454v1 Announce Type: new Abstract: Rubric-based rewards offer a promising way to extend reinforcement learning (RL) for large language models beyond tasks with automatically verifiable an

Articulatory strategy as a source of variation in acoustic vowel dynamics

arXiv:2605.23416v1 Announce Type: new Abstract: Acoustic vowel dynamics have some speaker-identifying characteristics, which have been ascribed to individual properties of articulatory strategies: for

Automated Random Embedding for Practical Bayesian Optimization with Unknown Effective Dimension

arXiv:2605.23473v1 Announce Type: cross Abstract: Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding,

Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series

arXiv:2605.23744v1 Announce Type: new Abstract: Anomaly detection in multivariate time series (MTS) is hindered by dynamic inter-variable dependencies and feature entanglement under spectral noise, an

Coupled Training with Privileged Information and Unlabeled Data

arXiv:2605.23268v1 Announce Type: cross Abstract: In many prediction problems, we have extra information during training (for example, measurements that are expensive or slow to collect) that will not

CP or DP? Why Not Both: A Case Study in the Partial Shop Scheduling Problem

arXiv:2605.23569v1 Announce Type: new Abstract: Dynamic Programming (DP) and Constraint Programming (CP) are well-established paradigms for solving combinatorial optimization problems. Usually, these

Emotion Recognition in Sign Language Conversation

arXiv:2605.23328v1 Announce Type: new Abstract: Emotion Recognition in Conversation is a core component of affective computing, while current resources of sign language emotion datasets primarily focu

GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation

arXiv:2605.22882v1 Announce Type: new Abstract: Video world models can generate realistic futures from a single instruction, but they often fail to preserve consistent point-level motion over time. As

applicationsarxiv-cs-cv

Hinge Regression Trees and HRT-Boost: Newton-Optimized Oblique Learning for Compact Tabular Models

arXiv:2605.23422v1 Announce Type: new Abstract: Learning high-quality oblique decision trees remains a significant challenge due to the discrete and non-convex nature of split optimization. We present

How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework

arXiv:2605.23651v1 Announce Type: new Abstract: While factual correctness and task-performance have been in focus of Large Language Model (LLM) research for a long time, the fundamental question of ho

Interactive Query Answering on Knowledge Graphs with Soft Entity Constraints

arXiv:2508.13663v5 Announce Type: replace Abstract: Methods for query answering over incomplete knowledge graphs retrieve entities that are likely to be answers, which is particularly useful when such

Is a Document Educational or Just Wikipedia-Style? -- Pitfalls of Classifier-Based Quality Filtering

arXiv:2605.23721v1 Announce Type: new Abstract: Classifier-based Quality Filtering has recently emerged as a fundamental technique in constructing pre-training corpora. The ability to deploy a single

Knowledge Distillation for Low-Resource Open-source Text-to-SQL Model

arXiv:2605.22843v1 Announce Type: new Abstract: Text-to-SQL converts natural language questions into executable SQL queries, enabling non-technical users to access relational databases for analytics a

Learning Individual Dynamics from Sparse Cross-Sectional Snapshots

arXiv:2605.23470v1 Announce Type: cross Abstract: Predicting how a dynamical unit evolves over time - how an individual ages, an epidemic spreads, or a physical system degrades - typically requires de

MedSAE: Dissecting MedCLIP Representations with Sparse Autoencoders

arXiv:2510.26411v2 Announce Type: replace Abstract: Artificial intelligence in healthcare requires models that are accurate and interpretable. We advance mechanistic interpretability in medical vision

PLACE: Prompt Learning for Attributed Community Search in Large Graphs

arXiv:2507.05311v2 Announce Type: replace-cross Abstract: In this paper, we propose PLACE (Prompt Learning for Attributed Community Search), an innovative graph prompt learning framework for ACS. Enli

RelPrism: A Multi-Faceted Pre-training Framework with Self-Generated Tasks for Relational Databases

arXiv:2605.23241v1 Announce Type: new Abstract: Relational databases (RDBs) remain the cornerstone of modern data systems and support diverse predictive tasks. Recent relational deep learning (RDL) me

Security of LLM-generated Code: A Comparative Analysis

arXiv:2605.23091v1 Announce Type: cross Abstract: The majority of software developers use or are planning to use Artificial Intelligence (AI) tools in their development processes. Their top reasons in

Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping

arXiv:2605.23160v1 Announce Type: cross Abstract: We present Semantic-Aware Guided Exploration, SAGE, a system for open-vocabulary exploration in unknown 3D indoor environments that preserves coverage

applicationsarxiv-cs-cv

Sparser Block-Sparse Attention via Token Permutation

arXiv:2510.21270v2 Announce Type: replace-cross Abstract: Scaling the context length of large language models (LLMs) offers significant benefits but is computationally expensive. This expense stems pr

Structure-Guided Entity Resolution: Fine-Tuning LLMs for Robust Name Matching in Complex Linguistic Contexts

arXiv:2605.23597v1 Announce Type: new Abstract: Matching person names across heterogeneous records is a core challenge in entity resolution, especially within linguistically and culturally complex env

Super-Linear: A Lightweight Pretrained Mixture of Linear Experts for Time Series Forecasting

arXiv:2509.15105v3 Announce Type: replace Abstract: Time series forecasting (TSF) is critical in domains like energy, finance, healthcare, and logistics, requiring models that generalize across divers

Safety

Task-Awareness Improves LLM Generations and Uncertainty

arXiv:2601.21500v2 Announce Type: replace Abstract: In many applications of LLMs, natural language responses often have an underlying structure such as representing discrete labels, numerical values,

safetyarxiv-cs-lg

The Deterministic Horizon: Impossibility Results as Design Specifications for Trustworthy AI Systems

arXiv:2605.23024v1 Announce Type: new Abstract: Large language models now write software, draft legal documents, and produce clinical notes, yet fundamental limits, from Turing and Arrow to the No Fre

Uncertainty-aware classification and triage of structural heart disease using electrocardiography and echocardiography metrics

arXiv:2605.22968v1 Announce Type: cross Abstract: Machine learning methods provide a methodological innovation that can help screen for cardiovascular disease through noninvasive and readily available

Vecchia-Inducing-Points Full-Scale Approximations for Gaussian Processes

arXiv:2507.05064v4 Announce Type: replace-cross Abstract: Gaussian processes are flexible, probabilistic, non-parametric models widely used in machine learning and statistics. However, their scalabili

Model Releases

Vector Retrieval with Similarity and Diversity: How Hard Is It?

model-releasesarxiv-cs-cl

arXiv:2407.04573v4 Announce Type: replace-cross Abstract: Dense vector retrieval is an important building block of modern machine learning systems, underlying applications ranging from semantic search

When AI Takes Sides on Questions of Faith: Persistent Asymmetries in AI-Mediated Faith Guidance

arXiv:2605.22975v1 Announce Type: new Abstract: We ask whether large language models (LLMs) treat queries about religious conversion symmetrically. The answer is no. When asked for advice on hypotheti

A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting

arXiv:2605.22390v1 Announce Type: new Abstract: Accurate wind power forecasting requires reliable uncertainty quantification, yet most existing methods report a single predictive uncertainty that conf

A Reproducible Log-Driven AutoML Framework for Interpretable Pipeline Optimization in Healthcare Risk Prediction

arXiv:2605.21528v1 Announce Type: new Abstract: Accurate and reproducible disease risk prediction remains challenging due to heterogeneous features, limited samples, and severe class imbalance. This s

Adaptive Measurement Allocation for Learning Kernelized SVMs Under Noisy Observations

arXiv:2605.22275v1 Announce Type: new Abstract: Kernel methods are typically formulated under the assumption of exact, noise-free access to the Gram matrix. However, in emerging settings such as quant

Model Releases

Characterizing the Fault Response of the Intel Neural Compute Stick 2 Under Single-Pulse Electromagnetic Fault Injection

model-releasesarxiv-cs-lg

arXiv:2605.22437v1 Announce Type: cross Abstract: Vision processing units and other commercial neural-network inference accelerators are increasingly deployed in safety-relevant edge applications, but

Discovering Entity-Conditioned Lag Heterogeneity: A Lag-Gated Neural Audit Framework for Panel Time Series

arXiv:2605.21542v1 Announce Type: new Abstract: Country-level temporal panels are widely used in empirical analysis. Researchers often need to audit how different entities respond to historical signal

Event-Aware Prompt Learning for Dynamic Graphs

arXiv:2510.11339v2 Announce Type: replace Abstract: Real-world graph typically evolve via a series of events, modeling dynamic interactions between objects across various domains. For dynamic graph le