Search: “applications”

Shared Lexical Task Representations Explain Behavioral Variability In LLMs

arXiv:2604.22027v1 Announce Type: cross Abstract: One of the most common complaints about large language models (LLMs) is their prompt sensitivity -- that is, the fact that their ability to perform a

27 Apr 2026

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Applications

Sharpness-Aware Poisoning: Enhancing Transferability of Injective Attacks on Recommender Systems

DGX agent

arXiv:2604.22170v1 Announce Type: new Abstract: Recommender Systems~(RS) have been shown to be vulnerable to injective attacks, where attackers inject limited fake user profiles to promote the exposur

applicationsarxiv-cs-lg

27 Apr 2026

Video Analysis and Generation via a Semantic Progress Function

arXiv:2604.22554v1 Announce Type: new Abstract: Transformations produced by image and video generation models often evolve in a highly non-linear manner: long stretches where the content barely change

27 Apr 2026

Who Audits the Auditor? Tamper-Proof Fraud Detection with Blockchain-Anchored Explainable ML

arXiv:2604.22096v1 Announce Type: cross Abstract: In enterprise fraud detection, model accuracy alone is insufficient when insiders can tamper with audit logs or bypass approval workflows. Real-world

27 Apr 2026

A Deployable Embodied Vision-Language Navigation System with Hierarchical Cognition and Context-Aware Exploration

arXiv:2604.21363v1 Announce Type: new Abstract: Bridging the gap between embodied intelligence and embedded deployment remains a key challenge in intelligent robotic systems, where perception, reasoni

applicationsarxiv-cs-ro

A Probabilistic Framework for Improving Dense Object Detection in Underwater Image Data via Annealing-Based Data Augmentation

arXiv:2604.21198v1 Announce Type: new Abstract: Object detection models typically perform well on images captured in controlled environments with stable lighting, water clarity, and viewpoint, but the

Adversarial Evasion in Non-Stationary Malware Detection: Minimizing Drift Signals through Similarity-Constrained Perturbations

arXiv:2604.21310v1 Announce Type: cross Abstract: Deep learning has emerged as a powerful approach for malware detection, demonstrating impressive accuracy across various data representations. However

Agents

AgenticQwen: Training Small Agentic Language Models with Dual Data Flywheels for Industrial-Scale Tool Use

arXiv:2604.21590v1 Announce Type: new Abstract: Modern industrial applications increasingly demand language models that act as agents, capable of multi-step reasoning and tool use in real-world settin

agentsarxiv-cs-cl

Back to the Future: The Role of Past and Future Context Predictability in Incremental Language Production

arXiv:2511.07752v3 Announce Type: replace Abstract: Contextual predictability shapes how we choose and encode words in production. The effects of a word's predictability given preceding or past contex

Behavioral Consistency and Transparency Analysis on Large Language Model API Gateways

arXiv:2604.21083v1 Announce Type: cross Abstract: Third-party Large Language Model (LLM) API gateways are rapidly emerging as unified access points to models offered by multiple vendors. However, the

Bridging Supervision Gaps: A Unified Framework for Remote Sensing Change Detection

arXiv:2601.17747v2 Announce Type: replace Abstract: Change detection (CD) aims to identify surface changes from multi-temporal remote sensing imagery. In real-world scenarios, Pixel-level change label

Capabilities and Evaluation Biases of Large Language Models in Classical Chinese Poetry Generation: A Case Study on Tang Poetry

arXiv:2510.15313v2 Announce Type: replace Abstract: Large Language Models (LLMs) are increasingly applied to creative domains, yet their performance in classical Chinese poetry generation and evaluati

Conditional anomaly detection with soft harmonic functions

arXiv:2604.21462v1 Announce Type: new Abstract: In this paper, we consider the problem of conditional anomaly detection that aims to identify data instances with an unusual response or a class label.

Conjecture and Inquiry: Quantifying Software Performance Requirements via Interactive Retrieval-Augmented Preference Elicitation

arXiv:2604.21380v1 Announce Type: cross Abstract: Since software performance requirements are documented in natural language, quantifying them into mathematical forms is essential for software enginee

Cross-Domain Data Selection and Augmentation for Automatic Compliance Detection

arXiv:2604.21469v1 Announce Type: new Abstract: Automating the detection of regulatory compliance remains a challenging task due to the complexity and variability of legal texts. Models trained on one

Differentially Private De-identification of Dutch Clinical Notes: A Comparative Evaluation

arXiv:2604.21421v1 Announce Type: cross Abstract: Protecting patient privacy in clinical narratives is essential for enabling secondary use of healthcare data under regulations such as GDPR and HIPAA.

Doubly Saturated Ramsey Graphs: A Case Study in Computer-Assisted Mathematical Discovery

arXiv:2604.21187v1 Announce Type: cross Abstract: Ramsey-good graphs are graphs that contain neither a clique of size s nor an independent set of size t. We study doubly saturated Ramsey-good graphs,

EduCoder: An Open-Source Annotation System for Education Transcript Data

arXiv:2507.05385v4 Announce Type: replace Abstract: We introduce EduCoder, a domain-specialized tool designed to support utterance-level annotation of educational dialogue. While general-purpose text

Exploring the Role of Synthetic Data Augmentation in Controllable Human-Centric Video Generation

arXiv:2604.21291v1 Announce Type: cross Abstract: Controllable human video generation aims to produce realistic videos of humans with explicitly guided motions and appearances,serving as a foundation

GARG-AML against Smurfing: A Scalable and Interpretable Graph-Based Framework for Anti-Money Laundering

arXiv:2506.04292v3 Announce Type: replace-cross Abstract: Purpose: We introduce GARG-AML, a fast and transparent graph-based method to catch `smurfing', a common money-laundering tactic. It assigns a

Hybrid Deep Learning Approach for Coupled Demand Forecasting and Supply Chain Optimization

arXiv:2604.21567v1 Announce Type: cross Abstract: Supply chain resilience and efficiency are vital in industries characterized by volatile demand and uncertain supply, such as textiles and personal pr

HypEHR: Hyperbolic Modeling of Electronic Health Records for Efficient Question Answering

arXiv:2604.21027v1 Announce Type: new Abstract: Electronic health record (EHR) question answering is often handled by LLM-based pipelines that are costly to deploy and do not explicitly leverage the h

InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training

arXiv:2509.21275v3 Announce Type: replace-cross Abstract: Long context training is crucial for LLM's context extension. Existing schemes, such as sequence parallelism, incur substantial communication

Integrated packing, placement, scheduling, and routing of personalized production: a pharmaceutical Industry 4.0 use-case with a planar transport system

arXiv:2604.21029v1 Announce Type: cross Abstract: The recent emergence of planar transport systems necessitates re-evaluation of Flexible Manufacturing Systems (FMS) to address the simultaneous schedu

Mapping the Political Discourse in the Brazilian Chamber of Deputies: A Multi-Faceted Computational Approach

arXiv:2604.21897v1 Announce Type: new Abstract: Analyses of legislative behavior often rely on voting records, overlooking the rich semantic and rhetorical content of political speech. In this paper,

Nonlinear Causal Discovery through a Sequential Edge Orientation Approach

arXiv:2506.05590v3 Announce Type: replace-cross Abstract: Recent advances have established the identifiability of a directed acyclic graph (DAG) under additive noise models (ANMs), spurring the develo

On Reasoning Behind Next Occupation Recommendation

arXiv:2604.21204v1 Announce Type: cross Abstract: In this work, we develop a novel reasoning approach to enhance the performance of large language models (LLMs) in future occupation prediction. In thi

Promoting Simple Agents: Ensemble Methods for Event-Log Prediction

arXiv:2604.21629v1 Announce Type: cross Abstract: We compare lightweight automata-based models (n-grams) with neural architectures (LSTM, Transformer) for next-activity prediction in streaming event l

Reasoning Primitives in Hybrid and Non-Hybrid LLMs

arXiv:2604.21454v1 Announce Type: cross Abstract: Reasoning in large language models is often treated as a monolithic capability, but its observed gains may arise from more basic operations. We study

Research

Relocation of compact sets in R^n by diffeomorphisms and linear separability of datasets in R^n

arXiv:2604.21393v1 Announce Type: new Abstract: Relocation of compact sets in an n-dimensional manifold by self-diffeomorphism is of its own interest as well as significant potential applications to d

researcharxiv-cs-lg

Situationally-aware Path Planning Exploiting 3D Scene Graphs

arXiv:2508.06283v2 Announce Type: replace Abstract: 3D Scene Graphs integrate both metric and semantic information, yet their structure remains underutilized for improving path planning efficiency and

applicationsarxiv-cs-ro

TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale

arXiv:2604.21889v1 Announce Type: cross Abstract: Real-time detection and mitigation of technical anomalies are critical for large-scale cloud-native services, where even minutes of downtime can resul

Toward a Multi-Layer ML-Based Security Framework for Industrial IoT

arXiv:2603.24111v3 Announce Type: replace-cross Abstract: The Industrial Internet of Things (IIoT) introduces significant security challenges as resource-constrained devices become increasingly integr

Model Releases

Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks

model-releasesarxiv-cs-lg

arXiv:2604.21696v1 Announce Type: new Abstract: Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as t

Trust but Verify: Introducing DAVinCI -- A Framework for Dual Attribution and Verification in Claim Inference for Language Models

arXiv:2604.21193v1 Announce Type: new Abstract: Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across a wide range of NLP tasks, yet they remain prone to factual ina

TV Subgradient-Guided Multi-Source Fusion for Spectral Imaging in Dual-Camera CASSI Systems

arXiv:2509.10897v2 Announce Type: replace Abstract: Balancing spectral, spatial, and temporal resolutions is a key challenge in spectral imaging. The Dual-Camera Coded Aperture Snapshot Spectral Imagi

Model Releases

Unlocking Multi-Spectral Data for Multi-Modal Models with Guided Inputs and Chain-of-Thought Reasoning

model-releasesarxiv-cs-cv

arXiv:2604.21032v1 Announce Type: new Abstract: Multi-spectral imagery is a valuable input signal for Remote Sensing applications, such as land-use and land-cover classification and environmental moni

WildSplatter: Feed-forward 3D Gaussian Splatting with Appearance Control from Unconstrained Images

arXiv:2604.21182v1 Announce Type: new Abstract: We propose WildSplatter, a feed-forward 3D Gaussian Splatting (3DGS) model for unconstrained images with unknown camera parameters and varying lighting

X2-N: A Transformable Wheel-legged Humanoid Robot with Dual-mode Locomotion and Manipulation

arXiv:2604.21541v1 Announce Type: new Abstract: Wheel-legged robots combine the efficiency of wheeled locomotion with the versatility of legged systems, enabling rapid traversal over both continuous a

applicationsarxiv-cs-ro

Adaptive Conformal Anomaly Detection with Time Series Foundation Models for Signal Monitoring

arXiv:2604.20122v1 Announce Type: cross Abstract: We propose a post-hoc adaptive conformal anomaly detection method for monitoring time series that leverages predictions from pre-trained foundation mo

Amodal SAM: A Unified Amodal Segmentation Framework with Generalization

arXiv:2604.20748v1 Announce Type: new Abstract: Amodal segmentation is a challenging task that aims to predict the complete geometric shape of objects, including their occluded regions. Although exist

An explicit operator explains end-to-end computation in the modern neural networks used for sequence and language modeling

arXiv:2604.20595v1 Announce Type: cross Abstract: We establish a mathematical correspondence between state space models, a state-of-the-art architecture for capturing long-range dependencies in data,

CHASM: Unveiling Covert Advertisements on Chinese Social Media

arXiv:2604.20511v1 Announce Type: cross Abstract: Current benchmarks for evaluating large language models (LLMs) in social media moderation completely overlook a serious threat: covert advertisements,

Closing the Domain Gap in Biomedical Imaging by In-Context Control Samples

arXiv:2604.20824v1 Announce Type: new Abstract: The central problem in biomedical imaging are batch effects: systematic technical variations unrelated to the biological signal of interest. These batch

Continuous Semantic Caching for Low-Cost LLM Serving

arXiv:2604.20021v1 Announce Type: cross Abstract: As Large Language Models (LLMs) become increasingly popular, caching responses so that they can be reused by users with semantically similar queries h

Cortex 2.0: Grounding World Models in Real-World Industrial Deployment

arXiv:2604.20246v1 Announce Type: cross Abstract: Industrial robotic manipulation demands reliable long-horizon execution across embodiments, tasks, and changing object distributions. While Vision-Lan

Cross-Modal Taxonomic Generalization in (Vision-) Language Models

arXiv:2603.07474v2 Announce Type: replace-cross Abstract: What is the interplay between semantic representations learned by language models (LM) from surface form alone to those learned from more grou

Do Hallucination Neurons Generalize? Evidence from Cross-Domain Transfer in LLMs

arXiv:2604.19765v1 Announce Type: cross Abstract: Recent work identifies a sparse set of 'hallucination neurons' (H-neurons), less than 0.1% of feed-forward network neurons, that reliably predict when