Search: “models”

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

1 May 2026

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Debiasing Reward Models via Causally Motivated Inference-Time Intervention

DGX agent

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

safetyarxiv-cs-ai

1 May 2026

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

1 May 2026

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

safetyarxiv-cs-ai

1 May 2026

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

safetyarxiv-cs-ai

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

safetyarxiv-cs-ai

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

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

researcharxiv-cs-cv

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

Rethinking Layer Redundancy in Large Language Models: Calibration Objectives and Search for Depth Pruning

arXiv:2604.24938v1 Announce Type: cross Abstract: Depth pruning improves the inference efficiency of large language models by removing Transformer blocks. Prior work has focused on importance criteria

Vision SmolMamba: Spike-Guided Token Pruning for Energy-Efficient Spiking State-Space Vision Models

arXiv:2604.25570v1 Announce Type: new Abstract: Spiking Transformers have shown strong potential for long-range visual modeling through spike-driven self-attention. However, their quadratic token inte

researcharxiv-cs-cv

A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models

arXiv:2512.03915v3 Announce Type: replace-cross Abstract: In large-scale AI training, Sparse Mixture-of-Experts (s-MoE) layers enable scaling by activating only a small subset of experts per token. An

Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation

arXiv:2604.24361v1 Announce Type: new Abstract: Large language models (LLMs) have achieved strong performance in general machine translation, yet their ability in culture-aware scenarios remains poorl

Hardware

FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost

arXiv:2604.24073v1 Announce Type: cross Abstract: Modern industrial Deep Learning Recommendation Models typically extract user preferences through the analysis of sequential interaction histories, sub

hardwarearxiv-cs-ai

Tutorials

LunarDepthNet: Generation of Digital Elevation Models using Deep Learning and Monocular Satellite Images

arXiv:2604.22848v1 Announce Type: new Abstract: Recent times have seen an increase in demand of high quality Digital Elevation Models (DEMs) for the lunar surface, because they are highly important fo

tutorialsarxiv-cs-cv

Applications

Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models

arXiv:2604.23272v1 Announce Type: new Abstract: Humans understand and interact with the real world by relying on diverse physical feedback beyond visual perception. Motivated by this, recent approache

applicationsarxiv-cs-ro

OntoLogX: Ontology-Guided Knowledge Graph Extraction from Cybersecurity Logs with Large Language Models

arXiv:2510.01409v2 Announce Type: replace Abstract: System logs represent a valuable source of Cyber Threat Intelligence (CTI), capturing attacker behaviors, exploited vulnerabilities, and traces of m

RL Token: Bootstrapping Online RL with Vision-Language-Action Models

arXiv:2604.23073v1 Announce Type: new Abstract: Vision-language-action (VLA) models can learn to perform diverse manipulation skills 'out of the box,' but achieving the precision and speed that real-w

safetyarxiv-cs-lg

Agents

SCRIBE: Structured Mid-Level Supervision for Tool-Using Language Models

arXiv:2601.03555v2 Announce Type: replace Abstract: Training reliable tool-augmented agents remains a significant challenge, largely due to the difficulty of credit assignment in multi-step reasoning.

agentsarxiv-cs-ai

Secure On-Premise Deployment of Open-Weights Large Language Models in Radiology: An Isolation-First Architecture with Prospective Pilot Evaluation

arXiv:2604.22768v1 Announce Type: cross Abstract: Purpose: To design, implement, evaluate, and report on the regulatory requirements of a self-hosted LLM infrastructure for radiology adhering to the p

Seer: Language Instructed Video Prediction with Latent Diffusion Models

arXiv:2303.14897v4 Announce Type: replace Abstract: Imagining the future trajectory is the key for robots to make sound planning and successfully reach their goals. Therefore, text-conditioned video p

Stress-Testing Emotional Support Models: Moving from Homogeneous to Diverse Help Seekers

arXiv:2601.07698v2 Announce Type: replace Abstract: As emotional support chatbots have recently gained significant traction across both research and industry, a common evaluation strategy has emerged:

TexOCR: Advancing Document OCR Models for Compilable Page-to-LaTeX Reconstruction

arXiv:2604.22880v1 Announce Type: new Abstract: Existing document OCR largely targets plain text or Markdown, discarding the structural and executable properties that make LaTeX essential for scientif

Agents

The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models

arXiv:2604.24698v1 Announce Type: new Abstract: Applications based on large language models (LLMs), such as multi-agent simulations, require population diversity among agents. We identify a pervasive

agentsarxiv-cs-cl

Applications

Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption

arXiv:2604.23245v1 Announce Type: cross Abstract: The use of Machine Learning (ML) for data-driven decision-making often relies on access to sensitive datasets, which introduces privacy challenges. Tr

applicationsarxiv-cs-ai

Weakly Supervised Multicenter Nancy Index Scoring in Ulcerative Colitis Using Foundation Models

arXiv:2604.23706v1 Announce Type: new Abstract: Histologic assessment of ulcerative colitis (UC) activity is an important endpoint in clinical trials and routine care, but manual grading with indices

researcharxiv-cs-cv

Applications

When Silence Matters: The Impact of Irrelevant Audio on Text Reasoning in Large Audio-Language Models

arXiv:2510.00626v3 Announce Type: replace-cross Abstract: Large audio-language models (LALMs) unify speech and text processing, but their robustness in noisy real-world settings remains underexplored.

applicationsarxiv-cs-cl

Clutter-Robust Vision-Language-Action Models through Object-Centric and Geometry Grounding

arXiv:2512.22519v2 Announce Type: replace Abstract: Recent Vision-Language-Action (VLA) models have made impressive progress toward general-purpose robotic manipulation by post-training large Vision-L

safetyarxiv-cs-ro

LLMPhy: Parameter-Identifiable Physical Reasoning Combining Large Language Models and Physics Engines

arXiv:2411.08027v3 Announce Type: replace-cross Abstract: Most learning-based approaches to complex physical reasoning sidestep the crucial problem of parameter identification (e.g., mass, friction) t

Preference Heads in Large Language Models: A Mechanistic Framework for Interpretable Personalization

arXiv:2604.22345v1 Announce Type: new Abstract: Large Language Models (LLMs) exhibit strong implicit personalization ability, yet most existing approaches treat this behavior as a black box, relying o

Agents

Source-Modality Monitoring in Vision-Language Models

arXiv:2604.22038v1 Announce Type: new Abstract: We define and investigate source-modality monitoring -- the ability of multimodal models to track and communicate the input source from which pieces of

agentsarxiv-cs-cl

Sum-of-Checks: Structured Reasoning for Surgical Safety with Large Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2604.22156v1 Announce Type: cross Abstract: Purpose: Accurate assessment of the Critical View of Safety (CVS) during laparoscopic cholecystectomy is essential to prevent bile duct injury, a comp

Survey Response Generation: Generating Closed-Ended Survey Responses In-Silico with Large Language Models

arXiv:2510.11586v2 Announce Type: replace Abstract: Many in-silico simulations of human survey responses with large language models (LLMs) focus on generating closed-ended survey responses, whereas LL

safetyarxiv-cs-cl

Transferable Physical-World Adversarial Patches Against Pedestrian Detection Models

arXiv:2604.22552v1 Announce Type: new Abstract: Physical adversarial patch attacks critically threaten pedestrian detection, causing surveillance and autonomous driving systems to miss pedestrians and

Evaluation of Automatic Speech Recognition Using Generative Large Language Models

arXiv:2604.21928v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) is traditionally evaluated using Word Error Rate (WER), a metric that is insensitive to meaning. Embedding-based sema

How Much Is One Recurrence Worth? Iso-Depth Scaling Laws for Looped Language Models

arXiv:2604.21106v1 Announce Type: cross Abstract: We measure how much one extra recurrence is worth to a looped (depth-recurrent) language model, in equivalent unique parameters. From an iso-depth swe

Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection

arXiv:2604.21728v1 Announce Type: new Abstract: Pretrained vision-language models such as CLIP exhibit strong zero-shot generalization but remain sensitive to distribution shifts. Test-time adaptation

Revisiting Non-Verbatim Memorization in Large Language Models: The Role of Entity Surface Forms

arXiv:2604.21882v1 Announce Type: new Abstract: Understanding what kinds of factual knowledge large language models (LLMs) memorize is essential for evaluating their reliability and limitations. Entit

Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models

arXiv:2604.21031v1 Announce Type: cross Abstract: Synthetic data generation offers promise for addressing data scarcity and privacy concerns in educational technology, yet practitioners lack empirical

Cognitive Kernel-Pro: A Framework for Deep Research Agents and Agent Foundation Models Training

arXiv:2508.00414v3 Announce Type: replace Abstract: General AI Agents are increasingly recognized as foundational frameworks for the next generation of artificial intelligence, enabling complex reason

Improving End-to-End Training of Retrieval-Augmented Generation Models via Joint Stochastic Approximation

arXiv:2508.18168v3 Announce Type: replace Abstract: Retrieval-augmented generation (RAG) has become a widely recognized paradigm to combine parametric memory with non-parametric memories. An RAG model

Large Language Models Meet Biomedical Knowledge Graphs for Mechanistically Grounded Therapeutic Prioritization

arXiv:2604.19815v1 Announce Type: new Abstract: Drug repurposing is often framed as a candidate identification task, but existing approaches provide limited guidance for distinguishing biologically pl

Large language models perceive cities through a culturally uneven baseline

arXiv:2604.20048v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to describe, evaluate and interpret places, yet it remains unclear whether they do so from a cultural

safetyarxiv-cs-cl

Saying More Than They Know: A Framework for Quantifying Epistemic-Rhetorical Miscalibration in Large Language Models

arXiv:2604.19768v1 Announce Type: cross Abstract: Large language models (LLMs) exhibit systematic miscalibration with rhetorical intensity not proportionate to epistemic grounding. This study tests th

researcharxiv-cs-ai

Temporal Difference Calibration in Sequential Tasks: Application to Vision-Language-Action Models

arXiv:2604.20472v1 Announce Type: cross Abstract: Recent advances in vision-language-action (VLA) models for robotics have highlighted the importance of reliable uncertainty quantification in sequenti

safetyarxiv-cs-lg