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DualDiT: A Conditional Dual-Output Diffusion Transformer for Joint OCT Image and Segmentation Mask Generation

arXiv:2607.29337v1 Announce Type: cross Abstract: Background and Objective: Generating realistic medical images with anatomically accurate segmentation masks helps address the shortage of annotated da

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Fast Feature Field (ext{F}^3): A Predictive Representation of Events

DGX agent

arXiv:2509.25146v2 Announce Type: replace-cross Abstract: This paper develops a mathematical argument and algorithms for building representations of data from event-based cameras, that we call Fast Fe

tutorialsarxiv-cs-ai

3 Aug 2026

FBFM: A Training-Free Asynchronous Feedback Mechanism for Flow-Matching in World-Action Models Execution

arXiv:2607.29235v1 Announce Type: cross Abstract: Although world-action models (WAMs) enhance long-horizon robot control by predicting visual evolution before acting, long-horizon reliability demands

Group-wise Supervision with Focal-Dice Loss for Long-Tailed Indoor Semantic Occupancy Prediction

arXiv:2607.28935v1 Announce Type: new Abstract: Recently, 3D semantic occupancy prediction has garnered increasing attention for understanding the indoor scene. However, unlike structured outdoor envi

Maximum Entropy Behavior Exploration for Sim2Real Zero-Shot Reinforcement Learning

arXiv:2603.25464v2 Announce Type: replace-cross Abstract: Zero-shot reinforcement learning (RL) algorithms aim to learn a family of policies from a reward-free dataset, and recover optimal policies fo

MoRAE: Flow-Friendly Self-Supervised Latents for Text-to-Motion Generation

arXiv:2607.29180v1 Announce Type: cross Abstract: Text-to-motion generation must produce motions that are semantically correct, temporally coherent, and physically plausible. A natural approach is to

MOSAIC: Masked Outsourcing of Secure AI Computations

arXiv:2607.29221v1 Announce Type: cross Abstract: We address the challenge of securely and efficiently outsourcing AI computations from a trusted but computationally weak client to an untrusted but po

PaletteID: Prototype-Composed Semantic Identifiers for Multimodal CTR Prediction

arXiv:2607.29000v1 Announce Type: cross Abstract: Multimodal information can improve the accuracy of click-through rate (CTR) prediction and effectively alleviate item cold-start and long-tail problem

Tipping Point Forecasting in Non-Stationary Dynamics on Function Spaces

arXiv:2308.08794v4 Announce Type: replace Abstract: Tipping points are abrupt, drastic, and often irreversible changes in the evolution of non-stationary and chaotic dynamical systems. For instance, i

To Facilitate or not to Facilitate: Human and LLM Facilitator Tendencies in Online Discussions

arXiv:2607.28643v1 Announce Type: cross Abstract: Automating facilitation in online discussions is a long-standing social concern given the increasing time we spend on online spaces and the failure of

TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners

arXiv:2607.29592v1 Announce Type: new Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks. A similarly impor

TransMem: Transforming Hidden States into Memory for Large Language Models

arXiv:2607.29032v1 Announce Type: cross Abstract: Large language model (LLM) agents increasingly operate over long interaction histories, where effective reasoning requires identifying and exploiting

UniPolymer: A Unified Framework for Property Prediction, Structure Recommendation, and Evaluation in Polyimide Design

arXiv:2607.29256v1 Announce Type: new Abstract: Designing polyimide structures with specific glass transition temperatures (Tg) is highly challenging. Existing methods primarily focus on target-condit

AuricularWorld: Hierarchical Action-Guided World Modeling for Fine-Grained Auricular Structure Segmentation from CT Scans

arXiv:2607.28487v1 Announce Type: new Abstract: Fine-grained segmentation of auricular structures in CT is challenging because the ear occupies a small image region, cartilage boundaries are highly ir

AutoPref: Automatic Discovery of Task-Specific Preference Objectives for Neural Combinatorial Optimization

arXiv:2607.27953v1 Announce Type: new Abstract: Combinatorial optimization problems (COPs) underpin many real-world decisions, but their exponentially large search spaces make high-quality solutions c

Backbone-Agnostic Stochastic Perturbation Learning for End-to-End Real-World Image Dehazing

arXiv:2607.11623v3 Announce Type: replace Abstract: Real-world paired image dehazing remains challenging because haze degradation is spatially non-uniform, illumination-dependent, and physically ambig

Bridging the Gap in Ophthalmic AI: MM-Retinal-Reason Dataset and OphthaReason Model toward Dynamic Multimodal Reasoning

arXiv:2508.16129v3 Announce Type: replace Abstract: Multimodal large language models (MLLMs) have recently demonstrated remarkable reasoning abilities with reinforcement learning paradigm. Although se

Can Large Language Models Execute Parent Orders?

arXiv:2607.28410v1 Announce Type: cross Abstract: Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution

CDAE: Enhancing Perturbation Robustness in Pretrained Language Models with Contrastive Denoising

arXiv:2607.28236v1 Announce Type: cross Abstract: Pre-trained language models have significantly improved sentence representation learning, yet their embedding remain sensitive to semantic preserving

Deep R Programming

arXiv:2301.01188v5 Announce Type: replace-cross Abstract: Deep R Programming is a comprehensive and in-depth introductory course on one of the most popular languages for data science. It equips ambiti

Dynamically Scaled Activation Steering

arXiv:2512.03661v2 Announce Type: replace Abstract: Activation steering has emerged as a powerful method for guiding the behavior of generative models towards desired outcomes such as toxicity mitigat

Enhancing Scene Transition Awareness in Video Generation via Post-Training

arXiv:2507.18046v2 Announce Type: replace Abstract: Recent advances in AI-generated video have shown strong performance on text-to-video tasks, particularly for short clips depicting a single scene. H

FedOGL: Combating Catastrophic Forgetting in Federated Open-World Multimodal Graph Learning

arXiv:2607.27665v1 Announce Type: new Abstract: Federated graph learning enables collaborative training over decentralized graph data without sharing raw graph information. As such risks evolve, clien

Finding Change in Satellite Archives from Text: How to Combine Before-and-After Images Efficiently

arXiv:2607.28571v1 Announce Type: new Abstract: Operational Earth observation increasingly calls for answering queries such as ``find the image pairs where a new building appeared.'' This means search

FootprintNet: State-Transition-Guided Dynamic Footprint Learning for Multi-temporal Remote Sensing Change Detection

arXiv:2607.27969v1 Announce Type: new Abstract: Despite substantial progress in remote sensing multi-temporal change detection (MTCD), most existing MTCD methods still represent the dynamic process at

Guarding Organizations Against Malware Risk: A Novel Graph-Based Malware Detection Method

arXiv:2607.26634v1 Announce Type: cross Abstract: Organizational digitalization expands cybersecurity risks, making cybersecurity an increasingly important research area in Information Systems (IS). A

Interpreting learning dynamics of autoencoders: Transient scaling and emerging concepts of the Ising model

arXiv:2607.10285v2 Announce Type: replace Abstract: We study how unsupervised autoencoders trained on microscopic spin configurations from the Ising model learn macroscopic, theory-relevant variables

Isolating to Harness: Cross-Division Distillation for Fully Unsupervised Anomaly Detection

arXiv:2508.18007v2 Announce Type: replace Abstract: Fully Unsupervised Anomaly Detection (FUAD) addresses the practical scenario where training data is contaminated with unlabeled anomalies. This sett

Latent Matters: Learning Deep State-Space Models

arXiv:2602.23050v2 Announce Type: replace Abstract: Deep state-space models (DSSMs) enable temporal predictions by learning the underlying dynamics of observed sequence data. They are often trained by

Learning to Detect Cyber Attacks: Neural Anomaly Detection for Cybersecurity with Theoretical Insights

arXiv:2409.08521v2 Announce Type: replace-cross Abstract: In cybersecurity practice, new forms of cyberattacks continuously emerge, deliberately designed to evade defense systems that rely on previous

Learning to Understand Body Language from Flight through Robust 3D Avatar Placing

arXiv:2607.27865v1 Announce Type: new Abstract: Perceiving human motion and intent at long range is a prerequisite for socially intelligent aerial robots, yet the data to learn it barely exists. We in

MentorCollab: Selective Large-to-Small Inference-Time Guidance for Efficient Reasoning

arXiv:2602.05307v2 Announce Type: replace Abstract: Large reasoning models (LRMs) achieve strong performance by producing long chains of thought, but their inference costs are high and often generate

MetaRank: Task-Aware Metric Selection for Model Transferability Estimation

arXiv:2511.21007v2 Announce Type: replace Abstract: Selecting an appropriate pre-trained source model is a critical, yet computationally expensive, task in transfer learning. Model Transferability Est

Noisy Data is Destructive to Reinforcement Learning with Verifiable Rewards

arXiv:2603.16140v2 Announce Type: replace Abstract: Reinforcement learning with verifiable rewards (RLVR) has driven recent capability advances of large language models across various domains. Recent

PhiZero: A World Model Built Around Physical Language

arXiv:2607.28624v1 Announce Type: new Abstract: We introduce PhiZero, a physical world model built around physical language, a compact discrete representation of world-state transitions. Existing phys

ReDiff: Reliability-Guided Diffusion for Trustworthy Ultra-Low-Field to High-Field MRI Synthesis

arXiv:2603.11325v2 Announce Type: replace Abstract: Low-field to high-field MRI synthesis has emerged as a promising strategy to improve image quality when access to high-field scanners is limited. Ho

Relational Scene Graphs for Object Grounding of Natural Language Commands

arXiv:2602.04635v2 Announce Type: replace Abstract: Robots are finding wider adoption in human environments, increasing the need for natural human-robot interaction. However, understanding a natural l

tutorialsarxiv-cs-ro

RLPF: Reinforcement Learning from Performance Feedback for Code Generation

arXiv:2607.27271v1 Announce Type: new Abstract: Code models are increasingly trained with execution feedback, but most training signals still stop at correctness. This leaves an important gap for syst

SAFViT: Spatial Attention Fusion Gating for Vision Transformer-Based Nucleus Segmentation and Classification

arXiv:2607.27835v1 Announce Type: new Abstract: Accurate cell segmentation and classification are foundational to digital pathology, enabling quantitative tissue analysis for diagnosis and treatment p

Same Graph Cross-Task Transfer in GNNs: Protocols and Predictors

arXiv:2607.28525v1 Announce Type: new Abstract: Many real-world graphs support multiple predictive tasks over the same underlying structure, creating an opportunity to reuse supervision across node cl

SUNSET - A Sensor-fUsioN based semantic SegmEnTation exemplar for ROS-based self-adaptation

arXiv:2601.13732v2 Announce Type: replace Abstract: The fact that robots are getting deployed more often in dynamic environments, together with the increasing complexity of their software systems, rai

tutorialsarxiv-cs-ro

TARS: Timestep-Aware Data Scaling for 3D-Free Video Re-Shooting

arXiv:2607.28261v1 Announce Type: new Abstract: Video re-shooting aims to regenerate videos with controllable camera motion and viewpoint. Existing methods rely on explicit 3D priors, which are limite

The Social Cost of an AI Teammate: How an Artificial Teammate Reshapes Human-Human Communication in Small-Team Decision-Making

arXiv:2607.27179v1 Announce Type: cross Abstract: Conversational AI is increasingly positioned as a teammate rather than a tool, yet we know little about how its presence reshapes communication among

Towards Robust Monocular Depth Estimation in Non-Lambertian Surfaces

arXiv:2408.06083v2 Announce Type: replace Abstract: In the field of monocular depth estimation (MDE), many models with excellent zero-shot performance in general scenes emerge recently. However, these

What Makes Graph Unified? Principles and Generative Sliding-Window Transformer for Graph Foundation Models

arXiv:2607.27966v1 Announce Type: new Abstract: Graph Foundation Models (GFMs) have recently emerged as a promising paradigm for general-purpose graph learning, aiming to learn reusable knowledge that

Breaking the Stealth-Potency Trade-off in Clean-Image Backdoors with Generative Trigger Optimization

arXiv:2511.07210v3 Announce Type: replace Abstract: Clean-image backdoor attacks, which use only label manipulation in training datasets to compromise deep neural networks, pose a significant threat t

30 Jul 2026

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

30 Jul 2026

Challenges and proposed solutions in modeling multimodal medical data: A systematic review

arXiv:2505.06945v5 Announce Type: replace Abstract: Multimodal data modeling has emerged as a powerful approach in clinical research, enabling the integration of diverse data types such as imaging, ge