Search: “models”

Annotation Quality in Aspect-Based Sentiment Analysis: A Case Study Comparing Experts, Students, Crowdworkers, and Large Language Model

arXiv:2605.03624v1 Announce Type: new Abstract: Aspect-Based Sentiment Analysis (ABSA) enables fine-grained opinion analysis by identifying sentiments toward specific aspects or targets within a text.

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Benchmarking Local Language Models for Social Robots using Edge Devices

DGX agent

arXiv:2605.03111v1 Announce Type: cross Abstract: Social-educational robots designed for socially interactive pedagogical support, such as the Robot Study Companion (RSC), rely on responsive, privacy-

local-aiarxiv-cs-cl

6 May 2026

CropVLM: A Domain-Adapted Vision-Language Model for Open-Set Crop Analysis

model-releasesarxiv-cs-cv

arXiv:2605.03259v1 Announce Type: new Abstract: High-throughput plant phenotyping, the quantitative measurement of observable plant traits, is critical for modern breeding but remains constrained by a

Kanade: A Simple Disentangled Tokenizer for Spoken Language Modeling

arXiv:2602.00594v2 Announce Type: replace Abstract: A good language model starts with a good tokenizer. Tokenization is especially important for speech modeling, which must handle continuous signals t

Moral Sensitivity in LLMs: A Tiered Evaluation of Contextual Bias via Behavioral Profiling and Mechanistic Interpretability

arXiv:2605.03217v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly deployed in settings that require nuanced ethical reasoning, yet existing bias evaluations treat model out

Agents

Separating Intelligence from Execution: A Workflow Engine for the Model Context Protocol

arXiv:2605.00827v1 Announce Type: cross Abstract: Large Language Model (LLM) agents increasingly interact with external systems through tool-calling protocols such as the Model Context Protocol (MCP).

agentsarxiv-cs-ai

The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence

model-releasesarxiv-cs-ai

arXiv:2605.02106v1 Announce Type: new Abstract: Contemporary artificial intelligence systems achieve strong performance through large-scale parameterization, retrieval augmentation, and training on ex

Automated Interpretability and Feature Discovery in Language Models with Agents

arXiv:2605.01555v1 Announce Type: new Abstract: We introduce an autonomous multiagent framework for mechanistic interpretability that automates both explaining and finding internal features in large l

Bolek: A Multimodal Language Model for Molecular Reasoning

arXiv:2605.02745v1 Announce Type: new Abstract: Molecular property models increasingly support high-stakes drug-discovery decisions, but their outputs are often difficult to audit: classical predictor

safetyarxiv-cs-lg

CombinationTS: A Modular Framework for Understanding Time-Series Forecasting Models

arXiv:2605.01231v1 Announce Type: new Abstract: Recent progress in time-series forecasting has led to rapidly increasing architectural complexity, yet many reported State-of-the-Art gains are statisti

Agents

DynFlowDrive: Flow-Based Dynamic World Modeling for Autonomous Driving

arXiv:2603.19675v2 Announce Type: replace Abstract: Recently, world models have been incorporated into the autonomous driving systems to improve the planning reliability. Existing approaches typically

agentsarxiv-cs-cv

Environment-Aware Indoor LoRaWAN Ranging Using Path Loss Model Inversion and Adaptive RSSI Filtering

arXiv:2505.01185v3 Announce Type: replace-cross Abstract: Achieving sub-10 m indoor ranging with LoRaWAN is challenging because multipath, human blockage, and micro-climate dynamics induce non-station

Extracting memorized pieces of (copyrighted) books from open-weight language models

arXiv:2505.12546v5 Announce Type: replace Abstract: Plaintiffs and defendants in copyright lawsuits over generative AI often make sweeping, opposing claims about the extent to which large language mod

Foundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding

arXiv:2605.00857v1 Announce Type: cross Abstract: Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled tar

researcharxiv-cs-lg

Applications

Led to Mislead: Adversarial Content Injection for Attacks on Neural Ranking Models

arXiv:2605.01591v1 Announce Type: cross Abstract: Neural Ranking Models (NRMs) are central to modern information retrieval but remain highly vulnerable to adversarial manipulation. Existing attacks of

applicationsarxiv-cs-cl

Multi-Rater Calibrated Segmentation Models

arXiv:2605.02437v1 Announce Type: new Abstract: Objective: Accurate probability estimates are essential for the safe deployment of medical image segmentation models in clinical decision-making. Howeve

Reliability-Oriented Multilingual Orthopedic Diagnosis: A Domain-Adaptive Modeling and a Conceptual Validation Framework

arXiv:2605.02266v1 Announce Type: new Abstract: Large Language Models (LLMs) are increasingly proposed for clinical decision support including multilingual diagnosis in low-resource settings. However,

safetyarxiv-cs-cl

SRA: Span Representation Alignment for Large Language Model Distillation

arXiv:2605.01205v1 Announce Type: new Abstract: Cross-Tokenizer Knowledge Distillation (CTKD) enables knowledge transfer between a large language model and a smaller student, even when they employ dif

safetyarxiv-cs-cl

Synthetic Designed Experiments for Diagnosing Vision Model Failure

arXiv:2605.00832v1 Announce Type: new Abstract: Current synthetic data pipelines for computer vision generate images without diagnosing what the downstream model actually needs. This open-loop paradig

The Case for ESM3 as a General-Purpose AI Model with Systemic Risk Under the EU AI Act

arXiv:2605.01611v1 Announce Type: cross Abstract: Due to ambiguity in the wording of the EU AI Act, we examine the question of to what extent frontier biological foundation models such as ESM3 are sub

safetyarxiv-cs-lg

Watch Your Step: Information Injection in Diffusion Models via Shadow Timestep Embedding

arXiv:2605.00935v1 Announce Type: cross Abstract: Diffusion models have become the foundation of modern generative systems, with most research focusing primarily on improving generation efficiency and

When Less is Enough: Efficient Inference via Collaborative Reasoning

arXiv:2605.01111v1 Announce Type: cross Abstract: In this work, we introduce DUET (Dual-model Efficient Two-stage inference), a collaborative inference framework in which a capable model and a lightwe

Concolic Testing on Individual Fairness of Neural Network Models

arXiv:2509.06864v2 Announce Type: replace Abstract: This paper introduces PyFair, a formal framework for evaluating and verifying individual fairness of Deep Neural Networks (DNNs). By adapting the co

Diffusion Models for Solving Inverse Problems via Posterior Sampling with Piecewise Guidance

arXiv:2507.18654v2 Announce Type: replace-cross Abstract: Diffusion models are powerful tools for sampling from high-dimensional distributions by progressively transforming pure noise into structured

Tutorials

Efficient Mutation Testing of Quantum Machine Learning Models

arXiv:2605.00107v1 Announce Type: cross Abstract: Quantum machine learning integrates the strengths of quantum computing and machine learning, enabling models to learn complex features using fewer par

tutorialsarxiv-cs-lg

From Local to Global to Mechanistic: An iERF-Centered Unified Framework for Interpreting Vision Models

arXiv:2605.00474v1 Announce Type: new Abstract: Modern vision models achieve remarkable accuracy, but explaining where evidence arises, what the model encodes, and how internal computations assemble t

Probing Multimodal Large Language Models on Cognitive Biases in Chinese Short-Video Misinformation

arXiv:2601.06600v2 Announce Type: replace Abstract: Short-video platforms have become major channels for misinformation, where deceptive claims frequently leverage visual experiments and social cues.

SC-Taxo: Hierarchical Taxonomy Generation under Semantic Consistency Constraints using Large Language Models

arXiv:2605.00620v1 Announce Type: new Abstract: Scientific literature is expanding at an unprecedented pace, making it increasingly challenging to efficiently organize and access domain knowledge. A h

Trees to Flows and Back: Unifying Decision Trees and Diffusion Models

arXiv:2605.00414v1 Announce Type: new Abstract: Decision trees and diffusion models are ostensibly disparate model classes, one discrete and hierarchical, the other continuous and dynamic. This work u

researcharxiv-cs-lg

Analytical Correction for Subsampling Bias in Drifting Models

arXiv:2604.27239v1 Announce Type: new Abstract: Drifting models are capable one-step generative models trained to follow a drifting field. The field combines attractive and repulsive softmax-weighted

safetyarxiv-cs-lg

CoAX: Cognitive-Oriented Attribution eXplanation User Model of Human Understanding of AI Explanations

arXiv:2604.27354v1 Announce Type: new Abstract: Explainable AI (XAI) aims to improve user understanding and decisions when using AI models. However, despite innovations in XAI, recent user evaluations

Debiasing Reward Models via Causally Motivated Inference-Time Intervention

arXiv:2604.27495v1 Announce Type: cross Abstract: Reward models (RMs) play a central role in aligning large language models (LLMs) with human preferences. However, RMs are often sensitive to spurious

Geometry-Calibrated Conformal Abstention for Language Models

arXiv:2604.27914v1 Announce Type: new Abstract: When language models lack relevant knowledge for a given query, they frequently generate plausible responses that can be hallucinations, rather than adm

Policy-Grounded Safety Evaluation of 20 Large Language Models

arXiv:2507.14719v2 Announce Type: replace Abstract: As large language models (LLMs) become increasingly integrated into real-world applications, scalable and rigorous safety evaluation is essential. T

Rethinking Pulmonary Embolism Segmentation: A Study of Current Approaches and Challenges with an Open Weight Model

arXiv:2509.18308v3 Announce Type: replace Abstract: Pulmonary Embolism (PE) is a life-threatening condition for which accurate and timely detection is critical to patient care. However, our systematic

Data-Centric Foundation Models in Computational Healthcare: A Survey

arXiv:2401.02458v3 Announce Type: replace-cross Abstract: The advent of foundation models (FMs) as an emerging suite of AI techniques has struck a wave of opportunities in computational healthcare. Th

Information Extraction from Electricity Invoices with General-Purpose Large Language Models

arXiv:2604.25927v1 Announce Type: new Abstract: Information extraction from semi-structured business documents remains a critical challenge for enterprise management. This study evaluates the capabili

Tutorials

Language Diffusion Models are Associative Memories Capable of Retrieving Unseen Data

arXiv:2604.26841v1 Announce Type: cross Abstract: When do language diffusion models memorize their training data, and how to quantitatively assess their true generative regime? We address these questi

tutorialsarxiv-cs-ai

Sociodemographic Biases in Educational Counselling by Large Language Models

arXiv:2604.25932v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly integrated into educational settings, understanding their potential biases is critical. This study ex

STARRY: Spatial-Temporal Action-Centric World Modeling for Robotic Manipulation

arXiv:2604.26848v1 Announce Type: new Abstract: Robotic manipulation critically requires reasoning about future spatial-temporal interactions, yet existing VLA policies and world-model-enhanced polici

safetyarxiv-cs-ro

Applications

A multi-stage soft computing framework for complex disease modelling and decision support: A liver cirrhosis case study

arXiv:2604.24796v1 Announce Type: cross Abstract: Liver cirrhosis is a major global health problem causing millions of deaths annually, and timely detection with aggressive treatment can significantly

applicationsarxiv-cs-lg

ADE: Adaptive Dictionary Embeddings -- Scaling Multi-Anchor Representations to Large Language Models

arXiv:2604.24940v1 Announce Type: new Abstract: Word embeddings are fundamental to natural language processing, yet traditional approaches represent each word with a single vector, creating representa

jina-embeddings-v5-text: Task-Targeted Embedding Distillation

arXiv:2602.15547v2 Announce Type: replace Abstract: Text embedding models are widely used for semantic similarity tasks, including information retrieval, clustering, and classification. General-purpos

Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots

arXiv:2604.25691v1 Announce Type: new Abstract: Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and

safetyarxiv-cs-ro

Named Entity Recognition of Historical Texts via Large Language Model

arXiv:2508.18090v2 Announce Type: replace-cross Abstract: Large language models (LLMs) have demonstrated remarkable versatility across a wide range of natural language processing tasks and domains. On

Prefill-Time Intervention for Mitigating Hallucination in Large Vision-Language Models

arXiv:2604.25642v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable progress in visual-textual understanding, yet their reliability is critically undermined b

Privileged Foresight Distillation: Zero-Cost Future Correction for World Action Models

arXiv:2604.25859v1 Announce Type: new Abstract: World action models jointly predict future video and action during training, raising an open question about what role the future-prediction branch actua

researcharxiv-cs-ro

Refinement via Regeneration: Enlarging Modification Space Boosts Image Refinement in Unified Multimodal Models

arXiv:2604.25636v1 Announce Type: new Abstract: Unified multimodal models (UMMs) integrate visual understanding and generation within a single framework. For text-to-image (T2I) tasks, this unified ca

safetyarxiv-cs-cv