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Decomposing Factual Sycophancy in Language Models: How Size and Instruction Tuning Shape Robustness

arXiv:2606.06306v1 Announce Type: new Abstract: Factual sycophancy occurs when a language model abandons a correct, verifiable answer under social pressure. Because a flip occurs only when pressure to

researcharxiv-cs-cl

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Deep Learning-assisted AMD Staging based on OCT and OCT Angiography

DGX agent

arXiv:2606.05379v1 Announce Type: new Abstract: To develop and evaluate deep learning models for automated grading of age-related macular degeneration (AMD) severity using optical coherence tomography

researcharxiv-cs-cv

5 Jun 2026

Deep Learning-based 3D Oral Cavity Reconstruction Using 2D Intraoral Images

arXiv:2606.05998v1 Announce Type: new Abstract: Oral 3D modelling is one of the most essential stages in dentistry, and many different approaches, such as impression taking and intraoral scanning, are

Dense Contexts Are Hard Contexts: Lexical Density Limits Effective Context in LLMs

arXiv:2606.06203v1 Announce Type: new Abstract: Input length and the position of relevant information are widely cited as the primary causes of degraded LLM long-context performance. Here, we study le

DexFuture: Hierarchical Future-State Visuomotor Targeting for Bimanual Dexterous Tool Use

arXiv:2606.05699v1 Announce Type: new Abstract: Bimanual dexterous tool use remains challenging for robots due to high-dimensional hand configurations and complex hand-tool-object dynamics and contact

Tutorials

Diff-CA: Separating Common and Salient Factors with Diffusion Models

arXiv:2606.06120v1 Announce Type: new Abstract: Contrastive Analysis aims to separate factors that are common between two data distributions from those that are salient to only one of them. Existing c

tutorialsarxiv-cs-cv

DiG-Plan: Mitigating Early Commitment for Tool-Graph Planning via Diffusion Guidance

arXiv:2606.05728v1 Announce Type: cross Abstract: Generating executable tool plans requires selecting appropriate subsets from tool libraries, a combinatorial search problem with an exponentially larg

researcharxiv-cs-cl

DisasterBench: A Multimodal Benchmark for UAV-Based Disaster Response in Complex Environments

arXiv:2606.06217v1 Announce Type: new Abstract: When a disaster unfolds, responders must answer not only what is happening, but also why it is happening, what will happen next, and what to do now, oft

Discrete-WAM: Unified Discrete Vision-Action Token Editing for World-Policy Learning

arXiv:2606.05645v1 Announce Type: new Abstract: Autonomous driving requires reasoning about how ego actions shape the evolution of the surrounding world. However, most end-to-end methods rely on direc

Disentangled Fine-Grained Prototype Learning for Incomplete Image-Tabular Classification

arXiv:2606.05455v1 Announce Type: new Abstract: The missing-modality problem poses a significant challenge in image-tabular multimodal learning across a wide range of multimedia applications, includin

safetyarxiv-cs-cv

Do MLLMs Capture How Interfaces Guide User Behavior? A Benchmark for Multimodal UI/UX Design Understanding

arXiv:2505.05026v5 Announce Type: replace Abstract: User interface (UI) design goes beyond visuals to shape user experience (UX), underscoring the shift toward UI/UX as a unified concept. While recent

Do Models Share Safety Representations? Cross-Model Steering for Safe Visual Generation

arXiv:2606.05290v1 Announce Type: new Abstract: Recent progress in generative modeling has made safety control a central challenge, yet existing approaches remain largely model-specific, requiring ret

safetyarxiv-cs-cv

DocHop-QA: Towards Multi-Hop Reasoning over Multimodal Document Collections

arXiv:2508.15851v2 Announce Type: replace Abstract: Despite rapid progress in large language models (LLMs), current QA benchmarks still overlook the core challenge of real-world scientific information

Domain-Aware Mispronunciation Detection and Diagnosis Using Language-Specific Statistical Graphs

arXiv:2606.05569v1 Announce Type: new Abstract: Mispronunciation Detection and Diagnosis (MDD) has gained increasing importance in computer-assisted language learning and speech technology in recent y

Domain-Conditioned Safety in Frontier Computer-Using Agents: A 793-Episode Browser Benchmark, a Coding-Domain Cross-Reference, and a Reproducibility Audit of Recent Red-Teaming

arXiv:2606.05233v1 Announce Type: cross Abstract: Recent computer-using-agent (CUA) red-teaming papers report prompt-injection attack success rates (ASR) of 42-98%, but these headline numbers cluster

DRIFT: A Residual Flow Adapter for Decoding Continuous Outputs in Vision-Language Models

arXiv:2606.05758v1 Announce Type: new Abstract: Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretra

Drishti AI-Event Guardian: An Intelligent Real-Time Crowd Monitoring and Emergency Response System for Mass Gathering Events

arXiv:2606.05185v1 Announce Type: cross Abstract: Mass gathering events are associated with critical safety incidents caused by insufficient crowd monitoring and inadequate emergency response coordina

safetyarxiv-cs-cv

Drive-KD: Multi-Teacher Distillation for VLMs in Autonomous Driving

arXiv:2601.21288v2 Announce Type: replace-cross Abstract: Autonomous driving is an important and safety-critical task, and recent advances in LLMs/VLMs have opened new possibilities for reasoning and

Dual Feature Decoupling for Fine-Grained OOD Detection

arXiv:2606.05536v1 Announce Type: new Abstract: Out-of-distribution detection (OOD) is an indispensable technique when applying machine learning models to real-world scenarios. Most existing OOD detec

applicationsarxiv-cs-cv

Dynamic Multi-Agent Pickup and Delivery in Robotic Cellular Warehousing Systems

arXiv:2606.05669v1 Announce Type: new Abstract: Robotic Cellular Warehousing Systems (RCWS) give rise to multi-agent pickup and delivery (MAPD) processes in which robots sequentially collect multiple

agentsarxiv-cs-ro

Dynamic Thinking-Token Selection for Efficient Reasoning in Large Reasoning Models

arXiv:2601.18383v2 Announce Type: replace-cross Abstract: Large Reasoning Models (LRMs) excel at solving complex problems by explicitly generating a reasoning trace before deriving the final answer. H

researcharxiv-cs-cl

Local Ai

EasyLens: A Training-Free Plug-and-Play Subtle-Lesion Representation Amplifier for Medical Vision-Language Models

arXiv:2606.06379v1 Announce Type: new Abstract: Medical vision-language models (VLMs) have shown increasing potential for clinical image interpretation, including lesion detection and report generatio

local-aiarxiv-cs-cv

EDIT: Evidence-Diagnosed Intervention Training for Rule-Faithful LLM Grading

arXiv:2606.06350v1 Announce Type: new Abstract: Reliable rubric grading requires more than accurate score prediction. Each judgement must be grounded in the mark scheme and evidence from the student a

applicationsarxiv-cs-cl

Efficient Computation of Distance Functions for Navigation Vector Fields in Lie Groups

arXiv:2606.05372v1 Announce Type: new Abstract: Vector-field-based methods are widely used for robot control and are often applied to the path-tracking problem. Some vector field approaches require re

researcharxiv-cs-ro

Efficient Mean Curvature Computation on High-Dimensional Data Manifolds

arXiv:2606.06329v1 Announce Type: cross Abstract: Estimating local mean curvature at each point of a high-dimensional dataset is a key ingredient of geometry-aware machine learning algorithms, such as

applicationsarxiv-cs-cv

Efficient Punctuation Restoration via Weighted Lookahead Scoring Method for Streaming ASR Systems

arXiv:2606.05179v1 Announce Type: new Abstract: Punctuation restoration improves ASR (Automatic Speech Recognition) readability. However streaming ASR requires online decisions with limited future con

Local Ai

EgoAction: Egocentric Action Composition with Reliability-Aware Temporal Fusion for the EPIC-KITCHENS Action Detection Challenge at CVPR 2026

arXiv:2605.24496v2 Announce Type: replace Abstract: The EPIC-KITCHENS-100 Action Detection challenge evaluates whether a model can localize the start and end of each action in long untrimmed egocentri

local-aiarxiv-cs-cv

EgoAdapt: A Multi-Scene Egocentric Adaptation Method for CVPR 2026 HD-EPIC VQA Challenge

arXiv:2605.24500v2 Announce Type: replace Abstract: This technical report presents our solution, EgoAdapt (Egocentric Adaptation via Category, Calibration, and Consistency), to the CVPR 2026 HD-EPIC V

EgoHumanoid: Unlocking In-the-Wild Loco-Manipulation with Robot-Free Egocentric Demonstration

arXiv:2602.10106v2 Announce Type: replace Abstract: Human demonstrations offer rich environmental diversity and scale naturally, making them an appealing alternative to robot teleoperation. While this

EGTR-Review: Efficient Evidence-Grounded Scientific Peer Review Generation via Multi-Agent Teacher Distillation

arXiv:2606.06025v1 Announce Type: new Abstract: Scientific peer review generation has attracted increasing attention for reducing reviewing burdens and providing timely feedback. However, existing Lar

EMBER: Efficient Memory via Budgeted Evidence Retention for Long-Horizon Agents

arXiv:2606.05894v1 Announce Type: new Abstract: Long-horizon agents can archive large histories, but future answers still incur retrieval, rereading, and context costs. When retained memory misses ans

safetyarxiv-cs-cl

Emergent Language as an Approach to Conscious AI

arXiv:2606.06380v1 Announce Type: new Abstract: The question of whether artificial systems can be conscious remains open, in part because existing approaches either evaluate systems against theory-der

Emotion-Aware Image Generation from Korean Diary Text via LLM-based Prompt Translation and LoRA Fine-Tuning

arXiv:2606.05816v1 Announce Type: new Abstract: T2I models cannot effectively capture sentiment from various types of text, including diaries, as they primarily focus on visual object-related patterns

English-to-Prakrit Machine Translation via Multilingual Transfer Learning

arXiv:2606.06038v1 Announce Type: new Abstract: We study English-to-Prakrit machine translation in a low-resource setting where the target language is unsupported by IndicTrans2. We adapt the multilin

Enhancing Multi-Robot Exploration Using Probabilistic Frontier Prioritization with Dirichlet Process Gaussian Mixtures

arXiv:2604.03042v2 Announce Type: replace Abstract: Multi-agent autonomous exploration is essential for applications such as environmental monitoring, search and rescue, and industrial-scale surveilla

agentsarxiv-cs-ro

Ensuring Interaction Safety in Multitask Exoskeleton Control: A Simulation-Trained Variable Impedance Framework

arXiv:2606.06370v1 Announce Type: new Abstract: Wearable exoskeletons can augment human phys ical capabilities during complex activities. However, ensuring adaptation across diverse tasks while guaran

Entropy-Based Evaluation of AI Agents: A Lightweight Framework for Measuring Behavioral Patterns

arXiv:2606.05872v1 Announce Type: cross Abstract: AI agents are commonly evaluated using task success, reward, latency, and cost. These metrics are useful, but they often miss important aspects of age

agentsarxiv-cs-cv

Epidemiology of Model Collapse: Modeling Synthetic Data Contamination via Bilayer SIR Dynamics

arXiv:2606.05168v1 Announce Type: new Abstract: Training on synthetic data causes model collapse, but existing analyses treat this as single-chain degradation. In reality, the AI ecosystem involves cr

EpiEvolve: Self-Evolving Agents for Streaming Pandemic Forecasting under Regime Shifts

arXiv:2606.05513v1 Announce Type: cross Abstract: Epidemic LLM forecasters are usually trained and evaluated as static supervised models, whereas operational pandemic forecasting is a streaming proces

Epistemic Injustice in Language Models: An Audit of Pretraining Filters and Guardrails

arXiv:2606.05936v1 Announce Type: new Abstract: Modern language models rely on pretraining filters to remove undesirable content from training corpora and inference-time guardrails to suppress undesir

safetyarxiv-cs-cl

Evaluating Stochastic Collapse and Implicit Bias in Multimodal Large Language Models

arXiv:2606.05874v1 Announce Type: new Abstract: Current evaluations for Multimodal Large Language Models (MLLMs) overwhelmingly focus on utility-driven objectives, leaving model behavior under logic-n

EVE: A Generator-Verifier System for Generative Policies

arXiv:2512.21430v2 Announce Type: replace Abstract: Visuomotor policies based on generative such as diffusion and flow-matching have shown strong performance for robotics applications but degrade unde

Executable Schema Contracts: From Automatic Ingestion to Multi-Source Retrieval

arXiv:2606.05415v1 Announce Type: new Abstract: Real-world data spans tables, documents, and semi-structured files with implicit semantics. Querying this data requires integrating evidence across inco

Local Ai

Explainability of Large Language Models: Opportunities and Challenges toward Generating Trustworthy Explanations

arXiv:2510.17256v2 Announce Type: replace Abstract: Large language models have exhibited impressive performance across a broad range of downstream tasks in natural language processing. However, how a

local-aiarxiv-cs-cl

Explainable Action Form Assessment by Exploiting Multimodal Chain-of-Thoughts Reasoning

arXiv:2512.15153v2 Announce Type: replace Abstract: Evaluating whether human action is standard or not and providing reasonable feedback to improve action standardization is very crucial but challengi

applicationsarxiv-cs-cv

ExpSpeech-Net: Multimodal Fusion of Expression and Speech for Deepfake Detection

arXiv:2606.05760v1 Announce Type: new Abstract: Deepfake videos are increasingly challenging the credibility of online content. Many existing detection methodology relies on complex, resource-intensiv

Facial-R1: Aligning Reasoning and Recognition for Facial Emotion Analysis

arXiv:2511.10254v2 Announce Type: replace Abstract: Facial Emotion Analysis (FEA) extends traditional facial emotion recognition by incorporating explainable, fine-grained reasoning. The task integrat

Faithful, Enriched, and Precise: Benchmarking Natural-Science Illustration Generation by T2I models

arXiv:2606.05949v1 Announce Type: new Abstract: Scientific illustrations are essential tools for communicating research findings, especially in natural science, where they visualize complex concepts a