arXiv:2512.08317v1 Announce Type: cross Abstract: Dataset distillation aims to synthesize a compact subset of the original data, enabling models trained on it to achieve performance comparable to those trained on the original large dataset. Existing distribution-matching methods are confined to Euclidean spaces, making them only capture linear structures and overlook the intrinsic geometry of real […]
ContextDrag: Precise Drag-Based Image Editing via Context-Preserving Token Injection and Position-Consistent Attention
arXiv:2512.08477v1 Announce Type: cross Abstract: Drag-based image editing aims to modify visual content followed by user-specified drag operations. Despite existing methods having made notable progress, they still fail to fully exploit the contextual information in the reference image, including fine-grained texture details, leading to edits with limited coherence and fidelity. To address this challenge, we […]
MARINE: Theoretical Optimization and Design for Multi-Agent Recursive IN-context Enhancement
arXiv:2512.07898v1 Announce Type: cross Abstract: Large Language Model (LLM)-based agents demonstrate advanced reasoning capabilities, yet practical constraints frequently limit outputs to single responses, leaving significant performance potential unrealized. This paper introduces MARINE (Multi-Agent Recursive IN-context Enhancement), a theoretically grounded framework that reconceptualizes test-time reasoning as iterative refinement of a persistent reference trajectory, fundamentally departing from […]
Near-real time fires detection using satellite imagery in Sudan conflict
arXiv:2512.07925v1 Announce Type: cross Abstract: The challenges of ongoing war in Sudan highlight the need for rapid moni- toring and analysis of such conflicts. Advances in deep learning and readily available satellite remote sensing imagery allow for near real-time monitor- ing. This paper uses 4-band imagery from Planet Labs with a deep learning model to […]
FRIEDA: Benchmarking Multi-Step Cartographic Reasoning in Vision-Language Models
arXiv:2512.08016v1 Announce Type: cross Abstract: Cartographic reasoning is the skill of interpreting geographic relationships by aligning legends, map scales, compass directions, map texts, and geometries across one or more map images. Although essential as a concrete cognitive capability and for critical tasks such as disaster response and urban planning, it remains largely unevaluated. Building on […]
Scalable Offline Model-Based RL with Action Chunks
arXiv:2512.08108v1 Announce Type: cross Abstract: In this paper, we study whether model-based reinforcement learning (RL), in particular model-based value expansion, can provide a scalable recipe for tackling complex, long-horizon tasks in offline RL. Model-based value expansion fits an on-policy value function using length-n imaginary rollouts generated by the current policy and a learned dynamics model. […]
Learning to Pose Problems: Reasoning-Driven and Solver-Adaptive Data Synthesis for Large Reasoning Models
arXiv:2511.09907v2 Announce Type: replace Abstract: Data synthesis for training large reasoning models offers a scalable alternative to limited, human-curated datasets, enabling the creation of high-quality data. However, existing approaches face several challenges: (i) indiscriminate generation that ignores the solver’s ability and yields low-value problems, or reliance on complex data pipelines to balance problem difficulty; and […]
Chat with UAV — Human-UAV Interaction Based on Large Language Models
arXiv:2512.08145v1 Announce Type: cross Abstract: The future of UAV interaction systems is evolving from engineer-driven to user-driven, aiming to replace traditional predefined Human-UAV Interaction designs. This shift focuses on enabling more personalized task planning and design, thereby achieving a higher quality of interaction experience and greater flexibility, which can be used in many fileds, such […]
Scalable Back-End for an AI-Based Diabetes Prediction Application
arXiv:2512.08147v1 Announce Type: new Abstract: The rising global prevalence of diabetes necessitates early detection to prevent severe complications. While AI-powered prediction applications offer a promising solution, they require a responsive and scalable back-end architecture to serve a large user base effectively. This paper details the development and evaluation of a scalable back-end system designed for […]
A Practical Framework for Evaluating Medical AI Security: Reproducible Assessment of Jailbreaking and Privacy Vulnerabilities Across Clinical Specialties
arXiv:2512.08185v1 Announce Type: cross Abstract: Medical Large Language Models (LLMs) are increasingly deployed for clinical decision support across diverse specialties, yet systematic evaluation of their robustness to adversarial misuse and privacy leakage remains inaccessible to most researchers. Existing security benchmarks require GPU clusters, commercial API access, or protected health data — barriers that limit community […]