Search: “models”

Do Medical Vision Models Reason About Anatomy? Probing the Spatial Inductive Biases of Learned Visual Representations

arXiv:2608.28092v1 Announce Type: cross Abstract: Interpreting a CT scan means comparing structures on either side, judging how far apart organs sit, and knowing where each one belongs. Medical vision

31 Aug 2026

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Research

Dual-Stream Semantic Guidance with Prototype Anchor Calibration for Source-Fully-Free Adaptation of Vision-Language Models

DGX agent

arXiv:2608.28145v1 Announce Type: new Abstract: Source-Fully-Free Domain Adaptation (SFF-DA) has emerged as a strategic paradigm to adapt Vision-Language Models (VLMs) without any access to source dat

researcharxiv-cs-cv

31 Aug 2026

Dynamic Alignment Compensation for Hallucination Mitigation in Large Vision-Language Models

arXiv:2608.28058v1 Announce Type: new Abstract: Large Vision-Language Models (LVLMs) remain prone to hallucinations, producing responses that are irrelevant or inconsistent with the multimodal input.

31 Aug 2026

EXPOSE: Explainable and Domain-Robust Embeddings from Pathology Vision Foundation Models using Sparse Autoencoders

arXiv:2608.28191v1 Announce Type: new Abstract: Vision Foundation Models (VFMs) are widely used in computational pathology but remain sensitive to domain shifts arising from variations in staining, ti

31 Aug 2026

EEG-to-Report: An Annotation and Feature-Text Framework for Training Language Models on Clinical EEG

arXiv:2608.26153v1 Announce Type: new Abstract: Clinical electroencephalography (EEG) reporting remains largely manual and time-consuming, and current EEG software ecosystems do not produce the struct

FOCUS & RePAIR: Mitigating Text Degeneration via Token-Level Guidance for Pruned Large Language Models

arXiv:2608.26676v1 Announce Type: cross Abstract: Pruning is a practical approach to compress large language models (LLMs), but it can amplify text degeneration, especially repetition loops, even when

Mutual Debiasing via Dual-Seed Comparison for Probabilistic Sampling in Large Language Models

arXiv:2608.26161v1 Announce Type: cross Abstract: Although Large Language Models (LLMs) demonstrate remarkable capabilities in reasoning and decision-making, high-fidelity probabilistic sampling remai

Provable one-poison backdoor attacks on linear models and ReLU neural networks

arXiv:2508.05600v3 Announce Type: replace Abstract: Backdoor poisoning attacks are a threat to machine learning models that are trained on data collected from untrusted sources; these attacks enable a

Reconstructing Humans and Objects in Interaction using Large Reconstruction Models

arXiv:2608.27407v1 Announce Type: new Abstract: Estimation of Human-Object Interactions in 3D (3D HOI) is a fundamental problem in 3D computer vision with applications in AR/VR, robotics, and embodied

Refine-POI: Reinforcement Fine-Tuned Large Language Models for Next Point-of-Interest Recommendation

arXiv:2506.21599v5 Announce Type: replace-cross Abstract: Advancing large language models (LLMs) for the next point-of-interest (POI) recommendation task faces two fundamental challenges: (i) although

Residual Reward Models: Leveraging Prior Knowledge for Efficient Preference-based Reinforcement Learning in Robotics

arXiv:2507.00611v2 Announce Type: replace-cross Abstract: Preference-based Reinforcement Learning (PbRL) provides a promising alternative to heuristic reward design in complex robotic environments. Ho

SPAR-K: Scheduled Periodic Alternating Early Exit for Spoken Language Models

arXiv:2603.09215v2 Announce Type: replace Abstract: Interleaved spoken language models (SLMs) alternately generate text and speech tokens, but decoding at full transformer depth for every step becomes

Applications

Layer-Order Inversion: Rethinking Latent Multi-Hop Reasoning in Large Language Models

arXiv:2601.03542v2 Announce Type: replace Abstract: Large language models (LLMs) perform well on multi-hop reasoning, yet how they internally compose multiple facts remains unclear. Recent work propos

applicationsarxiv-cs-cl

Learning to Accelerate Vision-Language-Action Models through Adaptive Visual Token Caching

arXiv:2602.00686v2 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have demonstrated remarkable generalization capabilities in robotic manipulation tasks, yet their substantial co

safetyarxiv-cs-ro

Learning to Detect Unseen Jailbreak Attacks in Large Vision-Language Models

arXiv:2508.09201v5 Announce Type: replace-cross Abstract: Despite extensive alignment efforts, Large Vision-Language Models (LVLMs) remain vulnerable to jailbreak attacks. To mitigate these risks, exi

ReFIne: A Framework for Trustworthy Large Reasoning Models with Reliability, Faithfulness, and Interpretability

arXiv:2510.09062v2 Announce Type: replace Abstract: Recent advances in long chain-of-thought (CoT) reasoning have largely prioritized answer accuracy and token efficiency, while overlooking aspects cr

Regularized Latent Dynamics Prediction is a Strong Baseline For Behavioral Foundation Models

arXiv:2603.15857v2 Announce Type: replace-cross Abstract: Behavioral Foundation Models (BFMs) produce agents with the capability to adapt to any unknown reward or task. These methods, however, are onl

Segmentation-Based Attention Entropy: Detecting and Mitigating Object Hallucinations in Large Vision-Language Models

arXiv:2603.16558v2 Announce Type: replace Abstract: Large Vision-Language Models (LVLMs) achieve strong performance on many multimodal tasks, but object hallucinations severely undermine their reliabi

Unsupervised Post-Training of Foundation Models: A Survey

arXiv:2608.24982v1 Announce Type: new Abstract: Foundation-model post-training usually relies on human labels, preference data, stronger teachers, or executable verifiers. We study Unsupervised Post-T

Applications

V-Link: Recovering Lost Visual Representations in Action DiT for Vision-Language-Action Models

arXiv:2608.25308v1 Announce Type: new Abstract: Vision-language-action (VLA) models provide a scalable path toward generalist robotic manipulation by integrating visual perception, language understand

applicationsarxiv-cs-cv

What Do Medical Vision-Language Models Learn in Radiology? Transfer, Alignment, and Source-Proxy Leakage Under Distribution Shift

arXiv:2608.25251v1 Announce Type: new Abstract: Medical vision-language models (VLMs) can appear reliable in-domain while failing when acquisition domain, paired supervision, or evaluation protocol ch

A Theory of Speciation in Generative Diffusion Models on Compact Riemannian Manifolds

arXiv:2608.23798v1 Announce Type: new Abstract: Speciation in generative diffusion models denotes the emergence of distinct stable branches during denoising, through which initially undifferentiated t

26 Aug 2026

GaussianWAM: Distilling Geometry and Semantics from 3D Gaussian Fields into World-Action Models

arXiv:2608.24714v1 Announce Type: new Abstract: World-Action Models (WAMs) jointly learn future visual prediction and action generation, using video dynamics as a representation-learning signal for ro

local-aiarxiv-cs-ro

26 Aug 2026

Adaptive Multilevel Twisted Sequential Monte Carlo for Rare Events Estimation in Language Models

arXiv:2608.21736v1 Announce Type: new Abstract: Rare unsafe behaviors in large language models can remain practically significant even when their probability is extremely small, particularly at deploy

safetyarxiv-cs-lg

Adaptive Test-Time Compute Allocation for Block Diffusion Language Models in Complex Reasoning

arXiv:2602.09555v3 Announce Type: replace Abstract: Recent advances in block diffusion language models have demonstrated competitive performance and strong scalability on reasoning tasks. However, the

E2S-Pruner: Progressive Two-Stage Evidence Fusion for Visual Token Pruning in Vision-Language Models

arXiv:2608.23253v1 Announce Type: cross Abstract: Vision-language models typically encode an image into hundreds of visual tokens, incurring substantial inference latency and GPU memory overhead. Exis

local-aiarxiv-cs-ai

Tutorials

EchoWM: Open and Enterable Omnimodal World Models

arXiv:2608.23189v1 Announce Type: new Abstract: We present EchoWM, an omnimodal world model for enterable generative media that responds to continuous navigation while jointly generating 720p video, e

tutorialsarxiv-cs-cv

Model Releases

Evaluating Large Language Models for automatic analysis of teacher simulations

model-releasesarxiv-cs-ai

arXiv:2407.20360v2 Announce Type: replace Abstract: Digital Simulations (DS) provide safe environments where users interact with an agent through conversational prompts, providing engaging learning ex

FourierSampler: Unlocking Non-Autoregressive Potential in Diffusion Language Models via Frequency-Guided Generation

arXiv:2601.23182v2 Announce Type: replace Abstract: Despite the non-autoregressive potential of diffusion language models (dLLMs), existing decoding strategies demonstrate positional bias, failing to

local-aiarxiv-cs-cl

Image-Conditioned Diffusion Models for Quality Assurance of Organ-at-Risk Segmentations in Radiotherapy

arXiv:2608.23432v1 Announce Type: new Abstract: Accurate organ-at-risk segmentation is essential for radiotherapy planning, but reviewing segmentations is time-consuming and subjective. We investigate

LD4WAM: Learning Latent Dynamics from Human Videos for World Action Models

arXiv:2608.22403v1 Announce Type: new Abstract: Human video is playing an increasingly central role in training World Action Models (WAMs), owing to its diversity and low collection cost relative to t

safetyarxiv-cs-ro

Tutorials

LISM: Long-range Integrative State space Models via Input-Latent State Interactions

arXiv:2509.04226v2 Announce Type: replace Abstract: State space models (SSMs) are an emerging paradigm that achieves linear-time scaling, however, they intrinsically suffer from 'curse of memory'. The

tutorialsarxiv-cs-lg

Mitigating Bias in Large Vision-Language Models via Counterfactual Ensemble Decoding

arXiv:2608.21415v1 Announce Type: cross Abstract: Large Vision-Language Models (LVLMs) have achieved remarkable performance across a wide range of tasks; however, they often inherit social biases from

MOSH-WM: Mask-Grounded Soft-Hamiltonian Dynamics for Object-Centric World Models

arXiv:2608.22750v1 Announce Type: new Abstract: Object-centric world models forecast future videos by evolving a set of entity slots, but the variables receiving dynamics supervision are often unconst

safetyarxiv-cs-lg

PatchGate: Narrowing the Verbalization Gap with Intrinsic Object Inventories in Frozen Vision-Language Models

arXiv:2608.21819v1 Announce Type: cross Abstract: Reliable image captioning in Vision-Language Models (VLMs) requires captions to be both precise and complete, avoiding unsupported object mentions whi

Model Releases

RetroDFM-R: Reasoning-Driven Retrosynthesis Prediction with Large Language Models via Reinforcement Learning

model-releasesarxiv-cs-ai

arXiv:2507.17448v2 Announce Type: replace-cross Abstract: Retrosynthetic planning is a cornerstone of organic synthesis and drug discovery. Yet existing AI methods often rely on pattern matching rathe

Robust Lightweight Deep Learning Models for Oral Cancer Screening

arXiv:2608.21583v1 Announce Type: new Abstract: Oral cancer is a leading cause of mortality in low-to-middle-income countries, where a shortage of specialists delays diagnosis. While point-of-care scr

Robustness of IR Models to Collection Growth

arXiv:2608.23419v1 Announce Type: cross Abstract: Information Retrieval (IR) systems seek to identify relevant documents within a collection. In practical applications, collections are dynamic, with d

Scalable quantum simulation of continuous-time generative models via tensor networks

arXiv:2608.21700v1 Announce Type: cross Abstract: Continuous-time flow and diffusion models are widely used across many application domains, from large-scale deployment in computer vision and protein

Tabular foundation models for non-tabular tasks

arXiv:2608.22594v1 Announce Type: new Abstract: Tabular foundation models (TFMs) have recently emerged as a promising paradigm for machine learning on tabular data, offering the ability to generalize

Training Large Language Models to Reason in a Continuous Latent Space

arXiv:2412.06769v4 Announce Type: replace Abstract: Large language models (LLMs) are typically constrained to reason in the language space, where they express the reasoning process through a chain-of-

UniMem: Unifying Multimodal Memory and Control for Vision-Language-Action Models

arXiv:2608.22869v1 Announce Type: cross Abstract: While Vision-Language-Action (VLA) models have leveraged internet-scale pretraining and task-focused finetuning to achieve strong performance on long-

VibrantLeaves: A principled parametric image generator for training deep restoration models

arXiv:2504.10201v3 Announce Type: replace Abstract: In this paper, we introduce a synthetic image generator relying on a few simple principles, specifically focusing on geometric modeling, textures, a

Model Releases

WADE: A Reasoning-Annotated Benchmark for Multi-Instance Floating-Waste Grounding with Compact Vision-Language Models

model-releasesarxiv-cs-cv

arXiv:2608.22950v1 Announce Type: new Abstract: Floating waste in inland waterways threatens aquatic ecosystems and requires timely monitoring under cluttered, multi-object conditions. Existing aquati

What Models Know, How Well They Know It: Knowledge-Weighted Fine-Tuning for Learning When to Say 'I Don't Know'

arXiv:2604.05779v2 Announce Type: replace-cross Abstract: While large language models (LLMs) demonstrate strong capabilities across diverse user queries, they still suffer from hallucinations, often a

Consistency Models for Fast MRI Reconstruction Using Regularization by Denoising

arXiv:2608.20561v1 Announce Type: cross Abstract: Diffusion models (DMs) have emerged as powerful generative priors for MRI reconstruction with promising results. Yet DM-based methods require extensiv

24 Aug 2026

Stream4D: 4D-Consistency for Streaming Autoregressive Diffusion Video Models

arXiv:2608.19556v1 Announce Type: cross Abstract: Streaming autoregressive diffusion models enable real-time, long-horizon video generation, but their training objectives optimize local frame predicti

local-aiarxiv-cs-ai

21 Aug 2026

Applications

When Machines Speak: A Unified Generative Framework for Integrating Machine-Native Symbols into Pretrained Large Language Models