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Do Understanding and Generation Fight? A Diagnostic Study of DPO for Unified Multimodal Models

arXiv:2603.17044v2 Announce Type: replace-cross Abstract: Unified multimodal models share a language model backbone for both understanding and generating images. Can DPO align both capabilities simult

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Document Classification Pattern Recognition via Information Fusion: A Systematic Review of Multimodal and Multiview Representation Approaches

DGX agent

arXiv:2605.23910v1 Announce Type: cross Abstract: Information fusion is used widely to improve document classification by the integration of multiple data sources (multimodal) or representations (mult

safetyarxiv-cs-ai

26 May 2026

Does Continued Pretraining on a Learner Corpus Improve Automated Essay Scoring on English Proficiency Tests? Evidence from EFCAMDAT

arXiv:2605.25924v1 Announce Type: new Abstract: Recent automated essay scoring (AES) studies increasingly use pretrained transformer models, but these models are usually pretrained on general-domain E

researcharxiv-cs-cl

Don't Retrain, Just Reuse: Recovering Dual-Target Molecules from Single-Target Diffusion Models

arXiv:2605.25681v1 Announce Type: cross Abstract: Designing a single molecule that modulates two targets is a promising strategy for polypharmacology, but it remains substantially harder than standard

researcharxiv-cs-ai

Applications

Double Self-weighted Multi-view Clustering via Adaptive View Fusion

arXiv:2011.10396v3 Announce Type: replace-cross Abstract: Multi-view clustering has been applied in many real-world applications where original data often contain noises. Some graph-based multi-view c

applicationsarxiv-cs-ai

Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation

model-releasesarxiv-cs-cl

arXiv:2605.25781v1 Announce Type: new Abstract: Evaluating structured-information extraction from historical documents at scale requires high-precision ground-truth annotations, yet traditional manual

Drift-Resistant Navigation World Model with Anchored Epipolar Guidance

arXiv:2605.24761v1 Announce Type: cross Abstract: We propose Drift-Resistant Navigation World Model, a generative model that mitigates both perceptual drift and geometric drift in conventional rollout

agentsarxiv-cs-ro

DriftingMol: Decoder-Coupled Drift for One-Pass Property-Conditional Molecular Generation

arXiv:2605.24841v1 Announce Type: new Abstract: Property-conditional molecular generation should produce valid, diverse molecules while responding to continuous target values at low sampling cost. We

researcharxiv-cs-lg

DRInQ: Evaluating Conversational Implicature with Controlled Context Variation

model-releasesarxiv-cs-cl

arXiv:2605.24267v1 Announce Type: new Abstract: Human conversation relies heavily on conversational implicature, in which speakers convey meanings that are suggested rather than explicitly stated. Alt

DRIVE: Modeling Skills at the Reasoning and Interaction Levels for Web Agents under Continual Learning

arXiv:2605.23939v1 Announce Type: new Abstract: Web agents require both high-level reasoning (for task decomposition) and low-level interactions (for page elements manipulation) to conduct different t

researcharxiv-cs-ai

DropoutTS: Sample-Adaptive Dropout for Robust Time Series Forecasting

arXiv:2601.21726v2 Announce Type: replace Abstract: Deep time series models are vulnerable to noisy data ubiquitous in real-world applications. Existing robustness strategies either prune data or rely

DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models

arXiv:2605.26038v1 Announce Type: cross Abstract: Lightweight vision-language models perform competitively on standard benchmarks yet fail systematically in dense-scene reasoning, where multiple objec

Local Ai

DTO: a Differentiable Training Objective for Effective Counterfactual Story Rewriting

arXiv:2605.24885v1 Announce Type: new Abstract: Counterfactual story rewriting is a natural language processing task that requires updating an existing story to reflect a chosen alternative event, yet

local-aiarxiv-cs-cl

DUEL: Adversarial Self-Play for Multimodal Reasoning

arXiv:2605.24794v1 Announce Type: cross Abstract: Reinforcement learning (RL) has emerged as an effective paradigm for improving the reasoning capability of vision-language models (VLMs). However, RL-

researcharxiv-cs-cl

DVAO: Dynamic Variance-adaptive Advantage Optimization for Multi-reward Reinforcement Learning

arXiv:2605.25604v1 Announce Type: new Abstract: Reinforcement Learning has become a standard paradigm for aligning Large Language Models with human intent and task requirements. While Group Relative P

safetyarxiv-cs-cl

Dynamic Dual-Granularity Skill Bank for Agentic RL

arXiv:2603.28716v2 Announce Type: replace Abstract: Agentic RL can benefit substantially from reusable experience, yet existing skill-based methods mainly extract trajectory-level guidance and often l

Dynamic Neural Koopman Distillation for Real-Time Robot Control Using Diffusion Models

arXiv:2605.24924v1 Announce Type: new Abstract: Diffusion models excel at generating diverse and multimodal trajectories for robotic planning, yet their iterative denoising process introduces latency

safetyarxiv-cs-ro

Dynamic Optimization and Safety Indicator Injection for Jailbreaking Text-to-Image Models with Multimodal Safety Filters

arXiv:2505.18979v2 Announce Type: replace Abstract: Text-to-image (T2I) models can generate not-safe-for-work (NSFW) content, motivating multi-stage safety pipelines with both text and image filters.

safetyarxiv-cs-lg

Dynamic Relational Priming Improves Transformer in Multivariate Time Series

arXiv:2509.12196v2 Announce Type: replace-cross Abstract: Standard attention mechanisms in transformers employ static token representations that remain unchanged across all pair-wise computations in e

Dynamics Reveals Structure: Challenging the Linear Propagation Assumption

arXiv:2601.21601v2 Announce Type: replace-cross Abstract: Neural networks adapt through first-order parameter updates, yet it remains unclear whether such updates preserve logical coherence. We invest

DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition

arXiv:2512.11941v2 Announce Type: replace-cross Abstract: Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton feat

E = T*H/(O+B): A Dimensionless Control Parameter for Mixture-of-Experts Ecology

arXiv:2605.06415v2 Announce Type: replace-cross Abstract: We introduce E = T*H/(O+B), a dimensionless control parameter that predicts whether Mixture-of-Experts (MoE) models will develop a healthy exp

E3AD: An Emotion-Aware Vision-Language-Action Model for Human-Centric End-to-End Autonomous Driving

arXiv:2512.04733v2 Announce Type: replace-cross Abstract: End-to-end autonomous driving (AD) systems increasingly adopt vision-language-action (VLA) models, yet they typically ignore the passenger's e

ECHO: Terminal Agents Learn World Models for Free

arXiv:2605.24517v1 Announce Type: cross Abstract: CLI agents are the closest thing language models have to an embodied setting: the model emits commands, the terminal executes them, and the returned s

safetyarxiv-cs-cl

EchoDistill:Alignment Noisy-to-Clean Self-Distillation for Robust Audio LLMs

arXiv:2605.23954v1 Announce Type: cross Abstract: Audio Large Language Models (ALLMs) are highly vulnerable to real-world noise, which often induces severe semantic drift and hallucinations. Existing

EchoPilot: Training-Free Ultrasound Video Segmentation via Scale-Space Semantic Prompting and Reliability-Gated Memory

arXiv:2605.25944v1 Announce Type: cross Abstract: Ultrasound video segmentation is clinically valuable yet difficult due to speckle noise, weak boundaries, and rapid anatomical deformation. Recent pro

ECo-MoE: Embodiment-Conditioned Mixture of Experts Increases the Evolvability of Robots

arXiv:2605.24225v1 Announce Type: new Abstract: In this paper, we introduce a model of evolution and learning in robots that co-optimizes a distribution of latent design vectors (genotypes) and a mixt

safetyarxiv-cs-ro

Efficient and Scalable Neural Symbolic Search for Knowledge Graph Complex Query Answering

arXiv:2505.08155v4 Announce Type: replace Abstract: Complex Query Answering (CQA) is a crucial reasoning task over Knowledge Graphs (KGs), which aims to answer first-order logical queries from incompl

researcharxiv-cs-ai

Efficient Benchmarking Is Just Feature Selection and Multiple Regression

arXiv:2605.25773v1 Announce Type: cross Abstract: Efficient benchmarking techniques aim to lower the computational cost of evaluating LLMs by predicting full benchmark scores using only a subset of a

Efficient DP-SGD for LLMs with Randomized Clipping

model-releasesarxiv-cs-lg

arXiv:2605.24879v1 Announce Type: new Abstract: Large language models (LLMs) are trained on vast datasets that may contain sensitive information. Differential privacy (DP), the de facto standard for f

Efficient Imputation for Patch-based Missing Single-cell Data via Cluster-regularized Optimal Transport

arXiv:2601.14653v2 Announce Type: replace Abstract: Missing data in single-cell sequencing datasets poses significant challenges for extracting meaningful biological insights. However, existing imputa

researcharxiv-cs-lg

Efficient Long-Horizon Vision-Language-Action Models via Static-Dynamic Disentanglement

model-releasesarxiv-cs-ro

arXiv:2602.03983v3 Announce Type: replace Abstract: Vision-Language-Action (VLA) models have recently emerged as a promising paradigm for generalist robotic control. Built upon vision-language model (

EfficientGraph-RAG: Structured Retrieval-State Management for Cross-Task Retrieval-Augmented Generation

arXiv:2605.25379v1 Announce Type: new Abstract: Retrieval-augmented generation (RAG) has become the standard way to ground large language models in external knowledge, but many systems still organize

agentsarxiv-cs-cl

Electricity Consumption Forecasting: An Approach Using Cooperative Ensemble Learning with SHapley Additive exPlanations

arXiv:2605.25305v1 Announce Type: new Abstract: Electricity expense management presents significant challenges, as this resource is susceptible to various influencing factors. In universities, the dem

agentsarxiv-cs-lg

Local Ai

Elevator-LIO: Robust LiDAR-Inertial Odometry for Multi-Floor Navigation under Elevator-Induced Non-Inertial Motion

arXiv:2605.24495v1 Announce Type: new Abstract: This paper presents Elevator-LIO, a LiDAR-inertial odometry framework designed to achieve continuous robot localization during elevator travel, thereby

local-aiarxiv-cs-ro

EMA: Effort Metric Attention for Anatomical Effort-Guided Human Motion Diffusion

arXiv:2605.24566v1 Announce Type: cross Abstract: Human motion diffusion models can synthesize action sequences from text, but controlling motion intensity remains challenging. Existing approaches rel

safetyarxiv-cs-lg

EMA-Nesterov: Stabilizing Nesterov's Lookahead for Accelerated Deep Learning Optimization

model-releasesarxiv-cs-lg

arXiv:2605.25395v1 Announce Type: new Abstract: Lookahead-based acceleration methods, such as Nesterov's momentum, are widely used in optimization, but they often become unreliable in deep learning tr

Emergent Analogical Reasoning in Transformers

arXiv:2602.01992v4 Announce Type: replace Abstract: Analogy is a central faculty of human intelligence, enabling abstract patterns discovered in one domain to be applied to another. Despite its centra

Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration

arXiv:2605.24543v1 Announce Type: new Abstract: The rapid growth of Electric Vehicle (EV) adoption challenges power distribution networks through peak load spikes, voltage instability, and transformer

Emotional intelligence in large language models is fragmented across perception, cognition, and interaction

arXiv:2605.24686v1 Announce Type: new Abstract: As large language models (LLMs) are increasingly integrated into emotionally sensitive domains, the structural integrity of their emotional intelligence

Empirical Analysis and Detection of Hallucinations in LLM-Generated Bug Report Summaries

arXiv:2605.24137v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used to generate summaries of software bug reports, including sections such as Steps-to-Reproduce (S2R),

End-to-End Intracortical Speech Decoding from Neural Activity

arXiv:2605.24313v1 Announce Type: new Abstract: Current high-performing intracortical speech neuroprostheses achieve low word error rates but typically rely on external language models during inferenc

researcharxiv-cs-cl

Energy Shields for Fairness

arXiv:2605.24926v1 Announce Type: new Abstract: Runtime fairness is not a one-time constraint but a dynamic property evaluated over a sequence of decisions. To ensure fairness at runtime, it is necess

Enhanced INS/GNSS State Estimation using GNSS-Based Acceleration Measurements

arXiv:2605.24767v1 Announce Type: new Abstract: Accurate and reliable navigation is essential for autonomous ground vehicle operations. Standard INS/GNSS fusion relies on GNSS position updates, which

agentsarxiv-cs-ro

Enhancing Reliability in LLM-Based Secure Code Generation

arXiv:2605.24300v1 Announce Type: cross Abstract: Large language models (LLMs) are widely used for code generation, but their security reliability remains inconsistent across languages and prompting s

EPPC-OASIS: Ontology-Aware Adaptation and Structured Inference Refinement for Electronic Patient-Provider Communication Mining in Secure Messages

arXiv:2605.24172v1 Announce Type: new Abstract: Secure patient-provider messages contain clinically important communication behaviors that are difficult to characterize manually at scale. The Electron

Equation-Free Coarse Control of Distributed Parameter Systems via Local Neural Operators

model-releasesarxiv-cs-lg

arXiv:2509.23975v2 Announce Type: replace-cross Abstract: The control of high-dimensional distributed parameter systems (DPS) remains a challenge when explicit coarse-grained equations are unavailable

Applications

Equip Pre-ranking with Target Attention by Residual Quantization

arXiv:2509.16931v3 Announce Type: replace-cross Abstract: The pre-ranking stage in industrial recommendation systems faces a fundamental conflict between efficiency and effectiveness. While powerful m

applicationsarxiv-cs-ai