Search: “models”

P-PatchDiff: Progressive Patch Diffusion Models for Low-light Image Enhancement

arXiv:2609.01123v1 Announce Type: new Abstract: Recent advancements in low-light image enhancement have leveraged diffusion models for their strong ability to generate perceptually realistic, detailed

local-aiarxiv-cs-cv

2 Sept 2026

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Research

VerTox: Verifiable Reward-Guided Corpus Poisoning Against Neural Ranking Models

DGX agent

arXiv:2609.01325v1 Announce Type: new Abstract: Neural ranking models have become core components of modern information retrieval systems and important building blocks of AI systems such as retrieval-

researcharxiv-cs-cl

2 Sept 2026

Accelerating Unified Multimodal Models with Core-Expansion Routing and Unified Computation Scheduling

arXiv:2608.29291v1 Announce Type: new Abstract: Unified multimodal models jointly support understanding and generation, but incur substantial redundant computation across tokens, layers, and generatio

safetyarxiv-cs-ai

CoCoA: Context-Conditional Cultural Alignment for Large Language Models

arXiv:2608.29492v1 Announce Type: new Abstract: Large Language Models (LLMs) often favor Western-associated entities across cultural contexts. Conventional debiasing methods aim for uniform neutrality

Detecting Hidden Chain-of-Thought in Large Language Models with Linguistic, Behavioral, and Mechanistic Indicators

arXiv:2608.29956v1 Announce Type: new Abstract: Large language models often answer complex reasoning questions without revealing intermediate steps, raising whether they reason latently or complete pa

Tutorials

Do Language Models Reason Across Languages?

arXiv:2601.06644v2 Announce Type: replace-cross Abstract: The real-world information sources are inherently multilingual, which naturally raises a question about whether language models can synthesize

tutorialsarxiv-cs-ai

Do large language models scrutinise what they review? A multimodal audit of scoring calibration, error detection, and author-identity effects

arXiv:2608.28626v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate peer reviews, prompting examination of their capacity for critical evaluation. This stu

Multimodal Large Language Models Predict Urban Safety Perception but Encode Non-Neutral Demographic Priors

arXiv:2503.00610v2 Announce Type: replace-cross Abstract: Understanding how people perceive urban environments is essential for inclusive planning, yet conventional surveys are costly and difficult to

safetyarxiv-cs-ai

On the Recoverability of Private Information Unlearning in Large Language Models

arXiv:2608.29943v1 Announce Type: cross Abstract: Large language models (LLMs) can memorize sensitive information, raising serious privacy concerns. Machine unlearning offers a potential solution to r

SpanCalib-VLM: Calibrated Hallucination Span Detection in Vision-Language Models

arXiv:2608.29974v1 Announce Type: cross Abstract: Detecting hallucinations in Large Vision-Language Models (LVLMs) requires both accurate span localization and well-calibrated confidence scores. Fine-

local-aiarxiv-cs-cl

Where Does Robustness Live? Neuron-Guided Adaptation for Retrieval-Augmented Language Models

arXiv:2604.02194v2 Announce Type: replace-cross Abstract: Retrieval-Augmented Language Models (RALMs) have shown strong potential in knowledge-intensive tasks, yet they remain vulnerable when retrieve

Which one is banana man? Evaluating vision-language models in multi-turn pragmatic interpretation

arXiv:2608.29571v1 Announce Type: new Abstract: Flexible adaptation to context and shared pragmatic intuitions contribute to smooth human conversation. Iterated reference games---in which players repe

WildSEEK: Evaluating Language Models for Information-Seeking

arXiv:2608.30683v1 Announce Type: new Abstract: Language models are increasingly mediating information access to end users, urging a systematic evaluation of their responses for a fair and reliable in

Embedding Models for Stance-Aware Argument Retrieval

arXiv:2608.28283v1 Announce Type: new Abstract: In computational argumentation, obtaining arguments that explicitly support or attack given claims is a critical precursor to downstream reasoning tasks

31 Aug 2026

CARE: Causally-Aligned Reasoning Exploration for Medical Large Language Models

arXiv:2608.26147v1 Announce Type: new Abstract: Large Language Models (LLMs) have shown strong potential for medical reasoning, yet the scarcity and cost of expert-annotated data constrain their progr

Agents

SPT: Skills as Pre-Training Data for Agentic Language Models

arXiv:2608.26563v1 Announce Type: new Abstract: Agentic (tool-using) language models are mainly trained on tool-call traces and agent trajectories during post-training. These data provide direct behav

agentsarxiv-cs-cl

Surgical Alignment in Knowledge Graph Training for Clinical Diagnosis with Large Language Models

arXiv:2608.26587v1 Announce Type: new Abstract: Biomedical knowledge graphs (KGs) offer structured medical knowledge that can ground large language model (LLM) reasoning in clinical diagnosis applicat

local-aiarxiv-cs-cl

Survival-Guided Length Control for Efficient Diffusion Language Models

arXiv:2608.26374v1 Announce Type: new Abstract: Diffusion language models (DLMs) generate text by iteratively denoising masked sequences, but standard decoding either fixes the sequence length or reli

Tutorials

Systematic Literature Review of Machine Learning Models and Applications for Text Recognition

arXiv:2608.26500v1 Announce Type: new Abstract: Optical Character Recognition (OCR) for text recognition using machine vision has significantly improved, particularly when handling heterogeneous textu

tutorialsarxiv-cs-cv

TrapVLA: Trapping Vision-Language-Action Models in Configured Failure Modes

arXiv:2608.26578v1 Announce Type: cross Abstract: This work introduces Configured Failure Trapping, a novel backdoor attack task against Vision-Language-Action (VLA) models, which aims to activate att

safetyarxiv-cs-cv

Applications

Addressing the Reasoning Gap: Mechanistic Circuit-Based Knowledge Editing in Large Language Models

arXiv:2604.05876v2 Announce Type: replace Abstract: Deploying Large Language Models (LLMs) in real-world dynamic environments raises the challenge of updating their pre-trained knowledge. While existi

applicationsarxiv-cs-cl

Bootstrapping a 4D LiDAR Annotation Tool from Video Foundation Models

arXiv:2608.25418v1 Announce Type: new Abstract: Progress in 4D LiDAR segmentation is bottlenecked by data. Assigning temporally consistent labels across sparse point cloud sequences is costly and hard

researcharxiv-cs-cv

Emergent Abilities in Large Language Models: A Survey

arXiv:2503.05788v3 Announce Type: replace-cross Abstract: Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial genera

Generative vs. Encoder Large Language Models for ASR Evaluation: A Comparative Study

arXiv:2608.25574v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) is typically evaluated using Word Error Rate (WER), which poorly reflects semantic similarity. While embedding-based

GGSS: Geodesic-Gated Spherical Steering for Inference-Time Debiasing of Generative Vision-Language Models

arXiv:2608.25375v1 Announce Type: cross Abstract: Generative vision-language models (VLMs) are increasingly used in human-centered settings, yet they can produce demographically biased outputs even wh

Towards Purified Multi-Label Test-Time Adaptation of Vision-Language Models

arXiv:2608.25653v1 Announce Type: new Abstract: Test-time adaptation (TTA) has been widely explored in single-label recognition, effectively mitigating distribution shifts, especially when combined wi

safetyarxiv-cs-cv

Why ML-based cough models do not generalize: a systematic cross-dataset evaluation for tuberculosis screening

arXiv:2608.25846v1 Announce Type: cross Abstract: Cough acoustics are promising for non-invasive tuberculosis (TB) screening, yet whether machine learning (ML) models capture disease-related acoustics

researcharxiv-cs-lg

Model Releases

Constraint-Guided Enterprise Data Mapping with Large Language Models

model-releasesarxiv-cs-ai

arXiv:2608.24218v1 Announce Type: new Abstract: Enterprise entity alignment must handle semi-structured records, implicit attributes, and unit or granularity mismatches. Manual matching is still commo

Evaluating Deep Multivariate Imputation Models on Wearable Device Data

arXiv:2608.24436v1 Announce Type: cross Abstract: Wearable device data enables continuous health monitoring, but suffers from structured missingness: features sharing a physical sensor drop out togeth

On-Policy Self-Distillation in Diffusion Models

arXiv:2608.24646v1 Announce Type: new Abstract: Reinforcement learning can align diffusion models with human preferences and task-specific objectives, but endpoint rewards do not specify how an interm

safetyarxiv-cs-cv

What Does Prompt Learning Change? -A Natural-Language Concept Analysis of Vision-Language Models

arXiv:2608.24142v1 Announce Type: new Abstract: Prompt learning adapts vision-language models such as CLIP by optimizing continuous prompt vectors, but the learned prompts are difficult to interpret i

researcharxiv-cs-cv

Agents

Agentic Scaffolding Amplifies Sycophantic Behavior in Large Language Models

arXiv:2608.21377v1 Announce Type: cross Abstract: Sycophancy in large language models, the tendency to prioritize user agreement over truthful responses, has been documented extensively but studied pr

agentsarxiv-cs-ai

Backdoor Sentinel: Detecting and Detoxifying Backdoors in Diffusion Models via Temporal Noise Consistency

arXiv:2602.01765v2 Announce Type: replace-cross Abstract: Diffusion models have been widely deployed in AIGC services, but their reliance on opaque training data exposes them to backdoor attacks. In p

local-aiarxiv-cs-ai

Model Releases

Budget-Constrained Embodied Perception: Four Resource Walls and a Pre-Registered Evaluation of Access-Structured Perception on Open Models at less than 31B

model-releasesarxiv-cs-ai

arXiv:2608.22975v1 Announce Type: new Abstract: Embodied multimodal agents must answer from growing observation streams under a fixed per-decision token budget. We formalize this constraint through fo

Do Time-Series Foundation Models Pay Off for Industrial Monitoring? A Cost-Aware Empirical Study

arXiv:2608.22968v1 Announce Type: new Abstract: Industrial monitoring models must detect operationally relevant deviations while satisfying target-specific data, calibration, and resource constraints.

local-aiarxiv-cs-lg

HiViS: Hiding Visual Tokens from the Drafter for Speculative Decoding in Vision-Language Models

arXiv:2509.23928v3 Announce Type: replace-cross Abstract: Speculative decoding has proven effective for accelerating inference in Large Language Models (LLMs), yet its extension to Vision-Language Mod

safetyarxiv-cs-ai

Posterior Information Dynamics of Diffusion Models for Linear Inverse Problems

arXiv:2608.21709v1 Announce Type: new Abstract: Diffusion models are widely used as priors for linear inverse problems, yet endpoint quality does not reveal when measurement information enters reverse

safetyarxiv-cs-lg

Model Releases

Robustness of Anomaly Detection Models for Industrial Control Systems under Training-Time Data Contamination

model-releasesarxiv-cs-lg

arXiv:2608.23547v1 Announce Type: cross Abstract: Machine-learning-based anomaly detection is increasingly used in industrial control systems (ICS), yet most studies assume that detector training data

Symbolic Neural ODEs: Learning interpretable models from time-series data

arXiv:2608.22112v1 Announce Type: new Abstract: We present a machine learning framework for identifying sparse, interpretable models of dynamical systems directly from time-series data. Our approach p

researcharxiv-cs-lg

Syntax-Guided Diffusion Language Models with User-Integrated Personalization

arXiv:2510.01028v2 Announce Type: replace Abstract: Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insuffi

Understanding Diffusion Models via Ratio-Based Function Approximation with SignReLU Networks

arXiv:2601.21242v2 Announce Type: replace-cross Abstract: Motivated by challenges in conditional generative modeling, where the target conditional density takes the form of a ratio f1 over f2, this pa

Deep Learning Models Also Recall Features

arXiv:2608.20970v1 Announce Type: new Abstract: Recent work in mechanistic interpretability has studied how large language models recall facts stored in their weights. This paper argues that factual r

24 Aug 2026

Efficient Self-Evaluation for Diffusion Language Models via Sequence Regeneration

arXiv:2603.02760v2 Announce Type: replace-cross Abstract: Diffusion large language models (dLLMs) have recently attracted significant attention for their ability to enhance diversity, controllability,

24 Aug 2026

Foundation Models for Partial Causal Identification

arXiv:2608.20841v1 Announce Type: new Abstract: This paper investigates the development of causal foundation models for bounding the effect of interventions and counterfactuals from observational data

24 Aug 2026

Applications

Causal Reasoning with Bipartite Graphical Causal Models

arXiv:2608.19831v1 Announce Type: new Abstract: Causal Bayesian networks (CBNs) and structural causal models (SCMs) are the dominant frameworks for graphical causal reasoning, but they cannot adequate

applicationsarxiv-cs-ai

Empirical Characterization of Learning Geometry in Hybrid Quantum Forecasting Models

arXiv:2608.19497v1 Announce Type: new Abstract: We characterize the learning dynamics of a compact hybrid quantum forecasting model through comparison with a structurally aligned classical baseline. U

safetyarxiv-cs-lg

Applications

Improved Confidence Estimates for Black-Box Large Language Models

arXiv:2608.19323v1 Announce Type: cross Abstract: Uncertainty quantification (UQ) is essential for the safe deployment of large language models (LLMs). Existing methods, from verbalized confidence to

applicationsarxiv-cs-ai

Towards Audio Token Compression in Large Audio Language Models

arXiv:2511.20973v2 Announce Type: replace-cross Abstract: Large Audio Language Models (LALMs) deliver strong performance across speech and audio tasks, but their audio encoders generate high-rate toke