Search: “models”

Cohort-Anchored Foundation Models for Electronic Health Records: From Risk Scores to Auditable Peer Cohorts

arXiv:2606.21885v1 Announce Type: new Abstract: Foundation models have achieved remarkable performance across medical question answering, imaging, and electronic health record (EHR) tasks, yet reliabl

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Evaluation of Medical Vision Language Models HuluMed and MedGemma, and general purpose chatbots Gemma 3, ChatGPT Plus, and Claude Pro on real previously unseen wound images

DGX agent

arXiv:2606.20723v1 Announce Type: new Abstract: Chronic wound assessment remains a clinically challenging task that requires accurate interpretation of wound morphology, tissue composition, vascular c

model-releasesarxiv-cs-cv

23 Jun 2026

FCR: Investigating Generative AI models for Forensic Craniofacial Reconstruction

arXiv:2508.18031v2 Announce Type: replace Abstract: Craniofacial reconstruction in forensics is one of the processes to identify victims of crime and natural disasters. Identifying an individual from

Improving Reasoning in Vision-Language Models via Perception Verified Self-Training

arXiv:2606.22158v1 Announce Type: new Abstract: Achieving human-like reasoning in Vision-Language Models (VLMs) remains a long-standing challenge. Recent approaches leverage Chain-of-Thought (CoT) rat

safetyarxiv-cs-cv

Inverse Problem for Partial Differential Equations with Jump Discontinuities in Coefficients by Two-stage Physics-Informed Deep Learning and Statistical Mixture Models

arXiv:2510.14656v2 Announce Type: replace-cross Abstract: This work proposes a two-stage physics-informed deep learning framework that combines neural-network-based sampling with statistical inference

Local Ai

Local Flow Matching Generative Models

arXiv:2410.02548v4 Announce Type: replace-cross Abstract: Flow Matching (FM) is a simulation-free method for learning a continuous, invertible flow that interpolates between two distributions, and in

local-aiarxiv-cs-lg

muMatch: Foundation Models for Semi-supervised Learning and Domain Adaptation in EM

arXiv:2606.21605v1 Announce Type: new Abstract: Vision foundation models have substantially advanced computer vision, enabling state-of-the-art performance in zero- and few-shot settings. They have be

OmniNWM: Omniscient Driving Navigation World Models

arXiv:2510.18313v5 Announce Type: replace Abstract: Autonomous driving world models are expected to work effectively across three core dimensions: state, action, and reward. However, existing methods

safetyarxiv-cs-cv

PG-MAP: Joint MAP Optimization for Inference-Time Alignment of Diffusion and Flow-Matching Models

arXiv:2606.22958v1 Announce Type: cross Abstract: Inference-time alignment of pretrained text-to-image models is typically performed along a single control axis, such as classifier-free guidance, atte

safetyarxiv-cs-cv

SamatNext v0.2-B: An Exploratory Study of RMS-Normalized Hybrid Decoders for Curriculum Retention in Small Code Models

arXiv:2606.22248v1 Announce Type: new Abstract: Standard autoregressive Transformer decoders can often exhibit substantial forgetting under sequential fine-tuning on shifting curriculum distributions.

Skeleton-to-Image Encoding: Enabling Skeleton Representation Learning via Vision-Pretrained Models

arXiv:2603.05963v2 Announce Type: replace Abstract: Recent advances in large-scale pretrained vision models have demonstrated impressive capabilities across a wide range of downstream tasks, including

Steer, Don't Solve: Training Small Critic Models for Large Code Agents

arXiv:2606.21811v1 Announce Type: cross Abstract: End-to-end code agent training is resource-intensive and plateaus on the strategy-level reasoning needed to resolve code issues, since jointly optimiz

Understanding Latent Flow Models for Tabular Data Synthesis: Targets, Paths, and Sampling

arXiv:2606.20878v1 Announce Type: new Abstract: Synthetic tabular data enables microdata sharing in regulated domains, yet deploying continuous-time generative models requires balancing analytical uti

UniFS: Unified Fast-to-Slow Hierarchical Architecture for Vision-Language-Action Models

arXiv:2606.22794v1 Announce Type: new Abstract: Mainstream Fast-Slow dual system vision-language-action models decouple a high-frequency action expert from a low-frequency vision-language model for ef

researcharxiv-cs-ro

Unlocking In-Context Learning in Audio-Language Models from Decentralized Medical Audio

arXiv:2606.23243v1 Announce Type: new Abstract: Clinical audio diagnosis in low-resource settings requires models that identify conditions from minimal examples without large annotated corpora. We pro

VeriBound: PAC-Bayesian Generalization Bounds for Process Reward Models Trained with Formal Verification Tools

arXiv:2606.20740v1 Announce Type: cross Abstract: Process Reward Models (PRMs) provide step-level verification for Large Language Model (LLM) reasoning, yet their training data acquisition remains a b

Tutorials

Words as Difference Makers: How Large Language Models Determine Causal Structure in Text

arXiv:2606.22430v1 Announce Type: cross Abstract: Because large language models (LLMs) are impressively successful in predicting text, it appears that they must have access to a 'world model' represen

tutorialsarxiv-cs-lg

A Judge-Aware Ranking Framework for Evaluating Large Language Models without Ground Truth

arXiv:2601.21817v3 Announce Type: replace-cross Abstract: Evaluating large language models (LLMs) on open-ended tasks without ground-truth labels is increasingly done via the LLM-as-a-judge paradigm.

Afrispeech Semantics: Evaluating Audio Semantic Reasoning in Spoken Language Models Across Domains and Accents

arXiv:2606.11219v1 Announce Type: cross Abstract: Audio language models (ALMs) are increasingly used for speech-based understanding, yet their ability to perform semantic reasoning beyond transcriptio

Beyond Fully Random Masking: Attention-Guided Denoising and Optimization for Diffusion Language Models

arXiv:2606.12273v1 Announce Type: new Abstract: Diffusion large language models (dLLMs) offer an efficient alternative to autoregressive models through parallel decoding, yet existing post-training me

researcharxiv-cs-cl

Cross-Layer Discrete Concept Discovery for Interpreting Language Models

arXiv:2506.20040v3 Announce Type: replace-cross Abstract: Interpreting language models remains challenging due to the existence of residual stream, which linearly mixes and duplicates features across

Agents

Physics-informed generative AI for semiconductor manufacturing: Enforcing hard physical constraints in generative models by construction

arXiv:2606.11247v1 Announce Type: cross Abstract: Generative models are increasingly used to propose designs, data, and control actions for physical systems, yet many such systems are governed by hard

agentsarxiv-cs-ai

Pretrained self-supervised speech models can recognize unseen consonants

arXiv:2606.11542v1 Announce Type: cross Abstract: Modern pretrained self-supervised automatic speech recognition models are trained on large-scale audio data to encode speech into contextualized repre

Re-evaluating Confidence Remasking in Masked Diffusion Language Models

arXiv:2606.12232v1 Announce Type: new Abstract: Masked diffusion language models (dLLMs) have recently emerged as a competitive alternative to autoregressive language models, with the promise of faste

Towards Fully Automated Exam Grading: Fairness-Aware Recognition of Handwritten Answers with Foundation Models

arXiv:2606.11477v1 Announce Type: cross Abstract: Correcting handwritten exams by hand is time-consuming and error-prone, particularly for large cohorts, while fully digital exams tend to force a dida

Tutorials

Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions

arXiv:2409.02426v5 Announce Type: replace-cross Abstract: Despite their empirical success across a wide range of generative tasks, the fundamental principles underlying the ability of diffusion models

tutorialsarxiv-cs-cv

Flow-DPPO: Divergence Proximal Policy Optimization for Flow Matching Models

arXiv:2606.11025v1 Announce Type: new Abstract: Recent work has demonstrated that online reinforcement learning (RL) can substantially improve the quality and alignment of flow matching models for ima

MMD Guidance: Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance

arXiv:2601.08379v2 Announce Type: replace-cross Abstract: Pre-trained diffusion models have emerged as powerful generative priors for both unconditional and conditional sample generation, yet their ou

safetyarxiv-cs-ai

NOVA: Symbolic Regression Discovery of Interpretable Car-Following and Lane-Change Models with Driver Heterogeneity

arXiv:2606.10583v1 Announce Type: cross Abstract: We present NOVA, an autonomous symbolic regression framework that identifies interpretable car-following and lane-change structures from raw trajector

ParaBridge: Bridging Paralinguistic Perception and Dialogue Behavior in Speech Language Models

arXiv:2606.10581v1 Announce Type: new Abstract: Speech carries more information than just words: a child's voice, a fearful tone, or a noisy background should all lead a sufficiently competent spoken-

safetyarxiv-cs-cl

When Do Autoregressive Sequence Models Forecast Physical Wavefields? A Controlled Study on Synthetic Seismograms

arXiv:2606.10868v1 Announce Type: new Abstract: Long-horizon autoregressive forecasting of oscillatory physical signals, such as seismograms, gravitational-wave strain, and similar wavefields is limit

Artificial Intelligence for Mathematical Reasoning: An Integrated Survey of Language Models, Neuro-symbolic Systems, and Verified Discovery

arXiv:2606.08728v1 Announce Type: new Abstract: Mathematical reasoning has long served as a stringent test of machine intelligence; over the past decade, it has moved from a niche problem within NLP t

ConSteer-RL: Steering Reasoning Capabilities in Large Language Models via Confidence-Aware Reinforcement Learning

arXiv:2606.08088v1 Announce Type: new Abstract: Reinforcement Learning from Verifiable Rewards (RLVR) has recently become a key paradigm for improving the reasoning abilities of Large Language Models

DALE-CT: Depth-Aware Foundation Models for Computed Tomography

arXiv:2606.07775v1 Announce Type: new Abstract: Recent breakthroughs in self-supervised learning (SSL), such as the Latent-Euclidean Joint-Embedding Predictive Architecture (LeJEPA), alongside success

DeepMine-Mamba: Mitigating Information Dilution in Mamba-Based State Space Models for Document Image Binarization

model-releasesarxiv-cs-cv

arXiv:2606.08781v1 Announce Type: new Abstract: Document image binarization aims to separate foreground text from degraded backgrounds while preserving thin, broken, and low-contrast strokes. Although

DN-Hypo-Pipeline: An AI-Driven Workflow for Hypothesis Generation via Large Language Models and Scientific Explanations

arXiv:2606.08532v1 Announce Type: new Abstract: A scientific hypothesis is the first step in research and undergoes experimental validation, yet it also reflects a deep understanding of and reasoning

Evaluating the Representation Space of Diffusion Models via Self-Supervised Principles

arXiv:2606.09718v1 Announce Type: cross Abstract: Diffusion models have demonstrated remarkable generative capabilities and have also emerged as powerful self-supervised representation learners, yet t

Exposing Hidden Biases in Text-to-Image Models via Automated Prompt Search

arXiv:2512.08724v3 Announce Type: replace Abstract: Text-to-image (TTI) diffusion models have achieved remarkable visual quality, yet they have been repeatedly shown to exhibit social biases across se

Failure by Interference: Language Models Make Balanced Parentheses Errors When Faulty Mechanisms Overshadow Sound Ones

arXiv:2507.00322v2 Announce Type: replace-cross Abstract: Despite remarkable advances in coding capabilities, language models (LMs) still struggle with simple syntactic tasks such as generating balanc

Applications

FF-JEPA: Long-Horizon Planning in World Models with Latent Planners

arXiv:2606.09311v1 Announce Type: new Abstract: Joint Embedding Predictive Architectures (JEPAs) have shown promising world modeling capabilities, enabling planning in latent space by optimizing actio

applicationsarxiv-cs-ai

Applications

iMaC: Translating Actions into Motion and Contact Images for Embodied World Models

arXiv:2606.09813v1 Announce Type: cross Abstract: Embodied world models have emerged as a pivotal paradigm for visual robotic decision-making and interactive environment simulation. However, conventio

applicationsarxiv-cs-cv

Impacts of Histories and Models on LLM Grading: A Study in Advanced Software Engineering Courses

arXiv:2606.08400v1 Announce Type: cross Abstract: Graduate-level research reading report assessment creates a substantial labor burden for educators. While large language models (LLMs) hold great pote

safetyarxiv-cs-ai

omega-EVA: Envision, Verify, and Act with Latent Interactive World Models

arXiv:2606.09457v1 Announce Type: new Abstract: Embodied policies typically map current observations directly to actions, leaving candidate-action consequences implicit. World models provide predictiv

safetyarxiv-cs-ro

Quantum latent distributions in deep generative models

arXiv:2508.19857v3 Announce Type: replace Abstract: Many successful families of generative models leverage a low-dimensional latent distribution that is mapped to a data distribution. Though simple la

QuoVLA: Quotient Space for Vision-Language-Action Models

arXiv:2605.24890v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models commonly adapt pretrained Vision-Language Models (VLMs) to robot control by mapping visual observations and lang

RTL-BenchLS: A Large-Scale Benchmark for RTL Reasoning and Generation with Large Language Models

arXiv:2606.08976v1 Announce Type: new Abstract: LLM-based RTL generation and reasoning is a promising direction for hardware design automation. High-quality benchmarks are critical infrastructure for

Securing Self-supervised Data Curation for Foundation Models Robustness

arXiv:2606.09511v1 Announce Type: new Abstract: Self-supervised data curation provides a pathway to scaling and improving the generalization capabilities of machine learning models. By leveraging self

Streaming Interventions: Can Video Large Language Models Correct Mistakes as They Occur?

model-releasesarxiv-cs-cv

arXiv:2606.09547v1 Announce Type: new Abstract: Learning everyday skills, like cooking a dish, relies increasingly on instructional media such as online videos. This opens the door to the use of video