Search: “models”

Energy-Driven Adaptive Visual Token Pruning for Efficient Vision-Language Models

arXiv:2603.05950v2 Announce Type: replace Abstract: Visual token reduction is critical for accelerating Vision-Language Models (VLMs), since visual inputs are represented as token sequences that intro

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Hardware

FARI: Robust One-Step Inversion for Watermarking in Diffusion Models

DGX agent

arXiv:2607.26723v1 Announce Type: cross Abstract: Inversion-based watermarking is a promising approach to authenticate diffusion-generated images, yet practical use is bottlenecked by inversion that i

hardwarearxiv-cs-ai

31 Jul 2026

LEEPS: Latent-Guided Explore-Exploit Prompt Sampling for Efficient RLVR in Large Language Models

arXiv:2607.28077v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) improves the reasoning capabilities of large language models, but prompt groups with identical rol

Hardware

ReToken: One Token to Improve Vision-Language Models for Visual Retrieval

arXiv:2607.28627v1 Announce Type: new Abstract: Long visual context poses a challenge for vision-language models: performance degrades as the number of distractors grows, and processing all tokens at

hardwarearxiv-cs-cv

Test-Time Backdoor Detection for Object Detection Models

arXiv:2503.15293v2 Announce Type: replace Abstract: Object detection models are vulnerable to backdoor attacks, where attackers poison a small subset of training samples by embedding a predefined trig

Where and When to Commit: Candidate-Aware Decoding for Diffusion Language Models

arXiv:2607.28166v1 Announce Type: new Abstract: Diffusion language models (DLMs) expose a provisional prediction at every denoising step, creating an opportunity for generation-time early exit that st

Applications

Automated Multilabel Mpox Research Classification with Explainable Transformer Models

arXiv:2607.26700v1 Announce Type: new Abstract: The Mpox outbreak remains a serious public health issue, with the WHO (World Health Organization) reporting increasing cases in some regions. Research o

applicationsarxiv-cs-cl

Tutorials

CalTwin: Towards Calibrated, Shift-Robust Medical World Models via Fisher-Information Regularisation

arXiv:2607.26752v1 Announce Type: new Abstract: Medical world models aim to learn a latent state of patient or organ physiology and a transition function that forecasts how that state evolves under in

tutorialsarxiv-cs-lg

CG-World: A Large-Scale World-State Dataset and Protocol for World Models

arXiv:2607.26452v1 Announce Type: cross Abstract: World models must learn the joint dynamics of states, actions, events, and observations, yet existing video, robotics, and simulation datasets usually

safetyarxiv-cs-cv

Classification of Disease from Lungs X-ray Images using VGG16, VGG19 and ResNet50 Models

arXiv:2607.26580v1 Announce Type: new Abstract: With the increase in the number of cases related to respiratory diseases, there is an urgent need to detect them early and diagnose them accurately. Con

From Keypoints to Predictive Distributions: Post-Hoc Uncertainty for YOLO-Pose Models

arXiv:2607.26921v1 Announce Type: new Abstract: YOLO-Pose models provide efficient keypoint localization, but do not quantify the associated spatial uncertainty. We introduce a lightweight post-hoc pr

local-aiarxiv-cs-cv

Graph Signal Diffusion Models for Wireless Resource Allocation

arXiv:2604.05175v2 Announce Type: replace-cross Abstract: We consider constrained ergodic resource optimization in wireless networks with graph-structured interference. We train a diffusion model poli

safetyarxiv-cs-lg

Sky sphere representation in language models

arXiv:2607.27092v1 Announce Type: new Abstract: We analyze whether language models of size ~100B have a representation of the night sky map that is decodable from their residual stream. We find that m

researcharxiv-cs-lg

Tutorials

A Causality-aware Infer-diagnose-refine Framework for Test-time Modality Adaptation in VLA Models

arXiv:2607.25516v1 Announce Type: new Abstract: Vision-language-action (VLA) models predict sequential actions to execute tasks specified by language instructions, conditioned on visual observations a

tutorialsarxiv-cs-ro

CADENCE: A Cardiac Atom Dictionary for Interpretable Neural Concept Extraction from ECG Foundation Models

arXiv:2607.25244v1 Announce Type: new Abstract: Foundation models for 12-lead electrocardiograms (ECGs) transfer well across clinical tasks, but the physiological knowledge encoded in their representa

Tutorials

Diffusion Disambiguation Models for Partial Label Learning

arXiv:2507.00411v2 Announce Type: replace Abstract: Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of partial label learning (PLL) is

tutorialsarxiv-cs-lg

Foundation Models for EEG Are Blind to Long-Range Temporal Correlations: A Spectral-Temporal Dissociation Behind Their Cross-Population Fragility

arXiv:2607.24834v1 Announce Type: cross Abstract: Objective. Electroencephalography (EEG) foundation models (FMs) are trained to reconstruct or contrastively align short patches, then pooled into a fi

Linear-LLM-SCM: Benchmarking LLMs for Coefficient Elicitation in Linear-Gaussian Causal Models

arXiv:2602.10282v2 Announce Type: replace Abstract: Large language models (LLMs) have shown potential in identifying qualitative causal relations, but their ability to perform quantitative causal reas

local-aiarxiv-cs-lg

Neurai-VN Benchmark: Standardized Machine Learning Models for Multimodal Digital Phenotyping in Mental Health Classification

model-releasesarxiv-cs-lg

arXiv:2607.25232v1 Announce Type: new Abstract: Digital phenotyping (DP) using smartphones and wearable devices has shown considerable potential for mental health monitoring. However, progress remains

Applications

Operational evaluation of data-driven forest fire forecasting models

arXiv:2603.25469v2 Announce Type: replace Abstract: A growing body of literature has focused on predicting wildfire occurrence using machine learning methods, capitalizing on high-resolution data and

applicationsarxiv-cs-lg

Agents

KG2Code: Bridging Knowledge Graphs and Large Language Models via Executable Code for Question Answering

arXiv:2607.22652v1 Announce Type: new Abstract: Recent research has explored the integration of knowledge graphs (KGs) with large language models (LLMs) to enhance their performance on downstream know

agentsarxiv-cs-ai

LOCUS: Local Visual Cue Search for Enhancing Fine-Grained Perception in Multimodal Large Language Models

arXiv:2606.16586v2 Announce Type: replace Abstract: Multimodal Large Language Models (MLLMs) remain unreliable on fine-grained visual perception, even when high-resolution inputs preserve the necessar

local-aiarxiv-cs-cv

MM-ShiftKV: Decode-Aware Prefill-Stage KV Selection for Multimodal Large Language Models

arXiv:2607.22586v1 Announce Type: new Abstract: Key-Value (KV) caching is essential for efficient inference in multimodal large language models (MLLMs), yet its memory footprint grows linearly with co

Operational Proto-Introspection in Looped Language Models: Process-Quality Taps, Executable Branching, and the Readout-Control Boundary

arXiv:2607.18553v2 Announce Type: replace-cross Abstract: Can a language model read the quality of its ongoing computation, and can an external intervention turn that readout into better outcomes? We

Parallel Tokenizers: Rethinking Encoder Models' Vocabulary Design in Cross-Lingual Transfer of Low-Resource Languages

arXiv:2510.06128v2 Announce Type: replace Abstract: Tokenization forms the basis of multilingual language models, yet existing methods often limit cross-lingual transfer by mapping semantically equiva

safetyarxiv-cs-cl

RP-OPSD: Resolution-Privileged On-Policy Self-Distillation for Multimodal Large Language Models

arXiv:2607.24447v1 Announce Type: new Abstract: On-Policy Self-Distillation (OPSD) uses privileged information available only to the teacher to provide dense token-level supervision on trajectories ge

local-aiarxiv-cs-cv

SARATR-X-v2: Scale-Aware Structural Pre-Training for SAR Foundation Models

arXiv:2607.23238v1 Announce Type: new Abstract: Masked image modeling has become a dominant paradigm for SAR pre-training, yet the design of the reconstruction target remains fundamentally unsettled.

Attention Degradation, Function Token Anchoring, and the Limits of Attention-Based Intervention in Large Language Models

model-releasesarxiv-cs-ai

arXiv:2607.20524v1 Announce Type: new Abstract: Mean cross-positional attention degradation is widely reported in transformer interpretability, yet whether it causally limits contextual retrieval rema

From a Word-Level Dictionary to Sentence-Level Semantics: Multilingual Grievance Labelling with Contextual Models

model-releasesarxiv-cs-cl

arXiv:2607.20946v1 Announce Type: new Abstract: Grievance is one of the warning signs analysts look for when assessing threats of violence. It is increasingly measured at scale from online text, most

Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances

arXiv:2511.03354v2 Announce Type: replace-cross Abstract: Generative artificial intelligence (GenAI) is transforming bioinformatics by advancing genomics, proteomics, transcriptomics, structural biolo

Geo3R: Mitigating Spatial Reasoning Hallucination in Multimodal Large Language Models

arXiv:2607.21085v1 Announce Type: new Abstract: Despite remarkable progress in visual understanding, Multimodal Large Language Models (MLLMs) remain prone to hallucinations when reasoning about spatia

Naver-News-KO: A Korean News Summarization Dataset for Open-Source Fine-Tuning of Summarization Models

model-releasesarxiv-cs-cl

arXiv:2607.20442v1 Announce Type: new Abstract: We release Naver-News-KO, a Korean news summarization dataset of 27,400 (document, summary) pairs collected from Naver News over a ten-day window in Jul

SuperFlow: Training Flow Matching Models with RL on the Fly

arXiv:2512.17951v3 Announce Type: replace Abstract: Recent progress in flow-based generative models and reinforcement learning (RL) has improved text-image alignment and visual quality. However, curre

safetyarxiv-cs-cv

D3VL: Understanding Driving Scenes from 3D Time Series Data and Video with Language Models

arXiv:2607.19528v1 Announce Type: cross Abstract: Recent advances in Multimodal Large Language Models (MLLMs) have triggered the development of end-to-end MLLMs for autonomous driving. However, the ma

Agents

Dreamer-CPC: Message Learning with World Models for Decentralized Multi-agent Reinforcement Learning

arXiv:2607.19809v1 Announce Type: cross Abstract: In multi-agent reinforcement learning (MARL), inter-agent communication is effective for improving performance under partial observability. Representa

agentsarxiv-cs-lg

Efficient Chain-of-Modality Reasoning via Progressive Compression for Spoken Language Models

arXiv:2607.19932v1 Announce Type: new Abstract: Spoken language models (SLMs) enable natural human-computer interaction, but their reasoning ability still lags behind that of text-based large language

JAGG: Jacobian-Aggregated Group Gradient for Efficient GRPO Training of Diffusion Models

arXiv:2607.17572v2 Announce Type: replace Abstract: Group Relative Policy Optimization (GRPO) is a powerful reinforcement learning algorithm for aligning generative models with human preferences. Whil

safetyarxiv-cs-lg

Leveraging Offline Supervision for Efficient and Generalizable Reinforcement Learning in Large-Scale Vision-Language-Action Models

model-releasesarxiv-cs-ai

arXiv:2607.19399v1 Announce Type: cross Abstract: It is commonly observed that online reinforcement learning (RL) produces better-performing strategies than offline methods across a broad range of per

Multi-Mask Diffusion Language Models for Few-Step Generation

arXiv:2607.19686v1 Announce Type: new Abstract: Masked diffusion models (MDMs) are a promising family of language generators, but achieving high-quality few-step generation remains challenging. In MDM

Multimodal Large Language Models for Remote Sensing Image Understanding: Domain-Specific or General-Purpose?

arXiv:2607.20284v1 Announce Type: new Abstract: The rapid development of multimodal large language models (MLLMs) has introduced a flexible paradigm for remote sensing image scene understanding (RSISU

Predictive Extrema, Unprofitable Policies: An AI-Assisted Audit of Candle-Based Binance Spot Timing Models

arXiv:2607.19453v1 Announce Type: cross Abstract: We audit whether candle-based machine-learning models can turn predictions of cryptocurrency extrema or short-horizon outcomes into positive Binance S

Prompt Programming for Cultural Bias and Alignment of Large Language Models

arXiv:2603.16827v2 Announce Type: replace Abstract: Culture shapes reasoning, values, prioritization, and strategic decision-making, yet large language models (LLMs) often exhibit cultural biases that

A Comparative Evaluation of Large Vision-Language Models for 2D Object Detection under SOTIF Conditions

model-releasesarxiv-cs-cv

arXiv:2601.22830v2 Announce Type: replace Abstract: Reliable environmental perception remains one of the main obstacles for safe operation of automated vehicles. Safety of the Intended Functionality (

Beyond Color Geometry: Evaluating Human-Like Color Representations in Vision Models

arXiv:2607.13647v1 Announce Type: cross Abstract: Do vision models see colors the way humans do? Existing evaluations of color representations usually compare them with geometric spaces such as CIELAB

The SIGReg Objective as Variational Free Energy: A Theoretical Active-Inference Account of JEPA World Models

arXiv:2607.13612v1 Announce Type: cross Abstract: Joint-Embedding Predictive Architectures (JEPAs) are the dominant design for latent world models, yet they are usually justified by empirical performa

Towards Enhancing 3D Spatial Reasoning in Medical Multimodal Large Language Models

arXiv:2607.13860v1 Announce Type: new Abstract: While Multimodal Large Language Models (MLLMs) have demonstrated remarkable success in 2D medical image understanding, their extension to 3D volumetric

Entropy-Preserving Supervised Fine-Tuning via Adaptive Self-Distillation for Large Reasoning Models

arXiv:2602.02244v3 Announce Type: replace-cross Abstract: The standard post-training recipe for large reasoning models, supervised fine-tuning followed by reinforcement learning (SFT-then-RL), may lim

15 Jul 2026

Agents

SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning