Search: “models”

DiffusionOPD: A Unified Perspective of On-Policy Distillation in Diffusion Models

arXiv:2605.15055v1 Announce Type: cross Abstract: Reinforcement learning has emerged as a powerful tool for improving diffusion-based text-to-image models, but existing methods are largely limited to

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Dimension-Level Intent Fidelity Evaluation for Large Language Models: Evidence from Structured Prompt Ablation

DGX agent

arXiv:2605.14517v1 Announce Type: cross Abstract: Holistic evaluation scores capture overall output quality but do not distinguish whether a model reproduced the structural form of a user's request fr

safetyarxiv-cs-ai

15 May 2026

Distribution Corrected Offline Data Distillation for Large Language Models

arXiv:2605.14071v1 Announce Type: new Abstract: Distilling reasoning traces from strong large language models into smaller ones is a promising route to improve intelligence in resource-constrained set

Factorization-Error-Free Discrete Diffusion Language Model via Speculative Decoding

arXiv:2605.14305v1 Announce Type: new Abstract: Discrete diffusion language models improve generation efficiency through parallel token prediction, but standard X_0 prediction methods introduce factor

Geometry-Aware Decoding with Wasserstein-Regularized Truncation and Mass Penalties for Large Language Models

arXiv:2602.10346v2 Announce Type: replace Abstract: Large language models (LLMs) must balance diversity and creativity against logical coherence in open-ended generation. Existing truncation-based sam

IntentVLA: Short-Horizon Intent Modeling for Aliased Robot Manipulation

model-releasesarxiv-cs-ai

arXiv:2605.14712v1 Announce Type: cross Abstract: Robot imitation data are often multimodal: similar visual-language observations may be followed by different action chunks because human demonstrators

Tutorials

Large Language Models for Web Accessibility: A Systematic Literature Review

arXiv:2605.13873v1 Announce Type: cross Abstract: Web accessibility aims to ensure that web content and services are usable by people with diverse abilities. In recent years, Large Language Models (LL

tutorialsarxiv-cs-ai

Learning Cross-Coupled and Regime Dependent Dynamics for Aerial Manipulation

model-releasesarxiv-cs-ro

arXiv:2605.14805v1 Announce Type: new Abstract: Accurate dynamics models are critical for aerial manipulators operating under complex tasks such as payload transport. However, modeling these systems r

LLMs Should Express Uncertainty Explicitly

model-releasesarxiv-cs-ai

arXiv:2604.05306v2 Announce Type: replace-cross Abstract: Large language models (LLMs) often produce confident yet incorrect answers, which can lead to risky failures in real-world applications. We st

MPU: Towards Secure and Privacy-Preserving Knowledge Unlearning for Large Language Models

arXiv:2602.23798v2 Announce Type: replace-cross Abstract: Machine unlearning for large language models often faces a privacy dilemma in which strict constraints prohibit sharing either the server's pa

Non-linear Interventions on Large Language Models

arXiv:2605.14749v1 Announce Type: cross Abstract: Intervention is one of the most representative and widely used methods for understanding the internal representations of large language models (LLMs).

Persian MusicGen: A Large-Scale Dataset and Culturally-Aware Generative Model for Persian Music

arXiv:2605.14765v1 Announce Type: cross Abstract: Persian music, with its unique tonalities, modal systems (Dastgah), and rhythmic structures, presents significant challenges for music generation mode

ReasonCache: Accelerating Large Reasoning Model Serving through KV Cache Sharing

arXiv:2507.21433v3 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) are becoming integral to many AI inference systems, enhancing their capabilities with advanced reasoning. Howeve

applicationsarxiv-cs-ai

Reasoning Model Is Superior LLM-Judge, Yet Suffers from Biases

arXiv:2601.03630v2 Announce Type: replace Abstract: This paper presents the first systematic comparison investigating whether Large Reasoning Models (LRMs) are superior judges to non-reasoning LLMs. O

Scalable Subset Selection in Linear Mixed Models

arXiv:2506.20425v3 Announce Type: replace-cross Abstract: Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalize

Tutorials

SeesawNet: Towards Non-stationary Time Series Forecasting with Balanced Modeling of Common and Specific Dependencies

arXiv:2605.14551v1 Announce Type: new Abstract: Instance normalization (IN) is widely used in non-stationary multivariate time series forecasting to reduce distribution shifts and highlight common pat

tutorialsarxiv-cs-lg

Separating Intrinsic Ambiguity from Estimation Uncertainty in Deep Generative Models for Linear Inverse Problems

arXiv:2605.15050v1 Announce Type: new Abstract: Recently, deep generative models have been used for posterior inference in inverse problems, including high-stakes applications in medical imaging and s

Thinking Ahead: Prospection-Guided Retrieval of Memory with Language Models

model-releasesarxiv-cs-ai

arXiv:2605.14177v1 Announce Type: cross Abstract: Long-horizon personalization requires dialogue assistants to retrieve user-specific facts from extended interaction histories. In practice, many relev

Training ML Models with Predictable Failures

arXiv:2605.15134v1 Announce Type: new Abstract: Estimating how often an ML model will fail at deployment scale is central to pre-deployment safety assessment, but a feasible evaluation set is rarely l

safetyarxiv-cs-lg

Where Should Diffusion Enter a Language Model? Geometry-Guided Hidden-State Replacement

arXiv:2605.14368v1 Announce Type: cross Abstract: Continuous diffusion language models lag behind autoregressive transformers, partly because diffusion is applied in spaces poorly suited to language d

AnyFlow: Any-Step Video Diffusion Model with On-Policy Flow Map Distillation

arXiv:2605.13724v1 Announce Type: cross Abstract: Few-step video generation has been significantly advanced by consistency distillation. However, the performance of consistency-distilled models often

safetyarxiv-cs-ai

Bridging the Missing-Modality Gap: Improving Text-Only Calibration of Vision Language Models

arXiv:2605.12517v1 Announce Type: cross Abstract: Vision-language models (VLMs) are often deployed on text-only inputs, although they are trained with images. We find that removing the vision modality

Debunking Grad-ECLIP: A Comprehensive Study on Its Incorrectness and Fundamental Principles for Model Interpretation

arXiv:2605.12952v1 Announce Type: new Abstract: Grad-ECLIP is published at ICML 2024 and represents a new Transformer interpretation technical route (intermediate features-based). First, this paper de

Few-Shot Physics-Informed Neural Network for Shape Reconstruction of Concentric-Tube Robots

model-releasesarxiv-cs-ro

arXiv:2605.12790v1 Announce Type: new Abstract: Modeling concentric tube robots (CTRs) involves complex nonlinear continuum mechanics, and despite recent progress, physics-based models often lack an a

SubspaceAD: Training-Free Few-Shot Anomaly Detection via Subspace Modeling

arXiv:2602.23013v3 Announce Type: replace Abstract: Detecting visual anomalies in industrial inspection often requires training with only a few normal images per category. Recent few-shot methods achi

Unifying Physically-Informed Weather Priors in A Single Model for Image Restoration Across Multiple Adverse Weather Conditions

arXiv:2605.13158v1 Announce Type: new Abstract: Image restoration under multiple adverse weather conditions aims to develop a single model to recover the underlying scene with high visibility. Weather

A Comparative Study of Model Selection Criteria for Symbolic Regression

arXiv:2605.11233v1 Announce Type: new Abstract: Effective model selection is critical in symbolic regression (SR) to identify mathematical expressions that balance accuracy and complexity, and have lo

Action Hallucination in Generative Vision-Language-Action Models

arXiv:2602.06339v2 Announce Type: replace Abstract: Robot Foundation Models, such as VLAs, promise end-to-end generative robot policies with broad generalization. Yet it remains unclear whether they f

researcharxiv-cs-ro

Local Ai

ADMM-Q: An Improved Hessian-based Weight Quantizer for Post-Training Quantization of Large Language Models

arXiv:2605.11222v1 Announce Type: new Abstract: Quantization is an effective strategy to reduce the storage and computation footprint of large language models (LLMs). Post-training quantization (PTQ)

local-aiarxiv-cs-lg

Couple to Control: Joint Initial Noise Design in Diffusion Models

arXiv:2605.11311v1 Announce Type: cross Abstract: Diffusion models typically generate image batches from independent Gaussian initial noises. We argue that this independence assumption is only one cho

safetyarxiv-cs-cv

Design Your Ad: Personalized Advertising Image and Text Generation with Unified Autoregressive Models

arXiv:2605.12138v1 Announce Type: cross Abstract: Generating realistic and user-preferred advertisements is a key challenge in e-commerce. Existing approaches utilize multiple independent models drive

ECHO: Continuous Hierarchical Memory for Vision-Language-Action Models

arXiv:2605.10993v1 Announce Type: new Abstract: Memory capacity is a critical factor determining the performance of Vision-Language-Action (VLA) models in long-horizon manipulation tasks. Existing mem

applicationsarxiv-cs-ro

Efficient Adjoint Matching for Fine-tuning Diffusion Models

arXiv:2605.11480v1 Announce Type: new Abstract: Reward fine-tuning has become a common approach for aligning pretrained diffusion and flow models with human preferences in text-to-image generation. Am

Tutorials

FERMI: Exploiting Relations for Membership Inference Against Tabular Diffusion Models

arXiv:2605.11527v1 Announce Type: new Abstract: Diffusion models are the leading approach for tabular data synthesis and are increasingly used to share sensitive records. Whether they actually protect

tutorialsarxiv-cs-lg

G^2TR: Generation-Guided Visual Token Reduction for Separate-Encoder Unified Multimodal Models

arXiv:2605.12309v1 Announce Type: new Abstract: The development of separate-encoder Unified multimodal models (UMMs) comes with a rapidly growing inference cost due to dense visual token processing. I

HorizonDrive: Self-Corrective Autoregressive World Model for Long-horizon Driving Simulation

arXiv:2605.11596v1 Announce Type: new Abstract: Closed-loop driving simulation requires real-time interaction beyond short offline clips, pushing current driving world models toward autoregressive (AR

Local Ai

Instruction Lens Score: Your Instruction Contributes a Powerful Object Hallucination Detector for Multimodal Large Language Models

arXiv:2605.12258v1 Announce Type: new Abstract: Multimodal large language models (MLLMs) have achieved remarkable progress, yet the object hallucination remains a critical challenge for reliable deplo

local-aiarxiv-cs-lg

Interpretable rainfall modelling reveals rapid reorganisation of Amazonian rainfall under vegetation loss

arXiv:2605.10948v1 Announce Type: cross Abstract: Understanding how vegetation loss alters rainfall remains a major challenge in climate and hydrological science, as deforestation modifies precipitati

Local Ai

Local and Mixing-Based Algorithms for Gaussian Graphical Model Selection from Glauber Dynamics

arXiv:2412.18594v3 Announce Type: replace Abstract: Gaussian graphical model selection is usually studied under independent sampling, but in many applications observations arise from dependent dynamic

local-aiarxiv-cs-lg

Make It Long, Keep It Fast: End-to-End 10k-Sequence Modeling at Billion Scale on Douyin Recommendation

arXiv:2511.06077v2 Announce Type: replace Abstract: Short-video recommenders such as Douyin must exploit extremely long user histories without breaking latency or cost budgets. We present an end-to-en

applicationsarxiv-cs-lg

Measuring Accuracy and Energy-to-Solution of Quantum Fine-Tuning of Foundational AI Models

arXiv:2605.02798v1 Announce Type: cross Abstract: We present an experimental study of energy-to-solution (ETS) of hybrid quantum-classical applications, enabled by direct instrumentation of power cons

applicationsarxiv-cs-lg

Patterns behind Chaos: Forecasting Data Movement for Efficient Large-Scale MoE LLM Inference

model-releasesarxiv-cs-lg

arXiv:2510.05497v5 Announce Type: replace-cross Abstract: Large-scale Mixture of Experts (MoE) Large Language Models (LLMs) have recently become the frontier open-weight models, achieving remarkable m

Procedural-skill SFT across capacity tiers: A W-Shaped pre-SFT Trajectory and Regime-Asymmetric Mechanism on 0.8B-4B Qwen3.5 Models

model-releasesarxiv-cs-lg

arXiv:2605.11907v1 Announce Type: new Abstract: We measure procedural-skill SFT contribution across three Qwen3.5 dense scales (0.8B, 2B, 4B) on a 200-task / 40-skill holdout, with Claude Haiku 4.5 as

Question Difficulty Estimation for Large Language Models via Answer Plausibility Scoring

arXiv:2605.12398v1 Announce Type: new Abstract: Estimating question difficulty is a critical component in evaluating and improving large language models (LLMs) for question answering (QA). Existing ap

Reconstruction of Personally Identifiable Information from Supervised Finetuned Models

arXiv:2605.12264v1 Announce Type: cross Abstract: Supervised Finetuning (SFT) has become one of the primary methods for adapting a large language model (LLM) with extensive pre-trained knowledge to do

applicationsarxiv-cs-cl

The DAWN of World-Action Interactive Models

arXiv:2605.11550v1 Announce Type: new Abstract: A plausible scene evolution depends on the maneuver being considered, while a good maneuver depends on how the scene may evolve. Existing World Action M

safetyarxiv-cs-cv

A Breast Vision Pathology Foundation Model for Real-world Clinical Utility

arXiv:2605.08207v1 Announce Type: new Abstract: Pathology foundation models have shown strong retrospective performance, but whether such systems can support clinically relevant use remains unclear. T

applicationsarxiv-cs-cv

12 May 2026

A Single Layer to Explain Them All:Understanding Massive Activations in Large Language Models

arXiv:2605.08504v1 Announce Type: new Abstract: We investigate the origins of massive activations in large language models (LLMs) and identify a specific layer named the extbf{Massive Emergence Layer