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arXiv:2602.11769v1 Announce Type: new Abstract: Recent advances in diffusion-based generative models have established a new paradigm for image and video relighting. However, extending these capabilities...
arXiv:2602.11767v1 Announce Type: new Abstract: Advances in large language models (LLMs) are driving a shift toward using reinforcement learning (RL) to train agents from iterative,...
arXiv:2602.11764v1 Announce Type: new Abstract: Shared, dynamic network infrastructures, such as dual-use LEO satellite constellations, pose critical threats to metadata privacy, particularly for state actors...
arXiv:2602.11759v1 Announce Type: new Abstract: Traffic forecasting based network operation optimization and management offers enormous promise but also presents significant challenges from traffic forecasting perspective....
arXiv:2602.11758v1 Announce Type: new Abstract: Humanoid robots show promise for complex whole-body tasks in unstructured environments. Although Human-Object Interaction (HOI) has advanced, most methods focus...
arXiv:2602.11757v1 Announce Type: new Abstract: Achieving spatial intelligence requires moving beyond visual plausibility to build world simulators grounded in physical laws. While coding LLMs have...
arXiv:2602.11756v1 Announce Type: new Abstract: Data integration is the primary use case for knowledge graphs. However, integrated data are not typically graphs but come in...
arXiv:2602.11754v1 Announce Type: new Abstract: LLM-based multi-agent systems (LLM-MAS), in which autonomous AI agents cooperate to solve tasks, are gaining increasing attention. For such systems...
arXiv:2602.11753v1 Announce Type: new Abstract: Aligning AI systems with human values fundamentally relies on effective human feedback. While significant research has addressed training algorithms, the...
arXiv:2602.11750v1 Announce Type: new Abstract: Benchmarks are paramount for gauging progress in the domain of Mobile GUI Agents. In practical scenarios, users frequently fail to...
arXiv:2602.11749v1 Announce Type: new Abstract: Large Language Model (LLM) agents are increasingly deployed in practice across a wide range of autonomous applications. Yet current safety...
arXiv:2602.11748v1 Announce Type: new Abstract: Achieving effective test-time scaling requires models to engage in In-Context Exploration -- the intrinsic ability to generate, verify, and refine...
arXiv:2602.11746v1 Announce Type: new Abstract: When successful, Open Source Software (OSS) projects create enormous value, but most never reach a sustainable state. Recent work has...
arXiv:2602.11745v1 Announce Type: new Abstract: Graph models are fundamental to data analysis in domains rich with complex relationships. Text-to-Graph-Query-Language (Text-to-GQL) systems act as a translator,...
arXiv:2602.11743v1 Announce Type: new Abstract: Mainstream Test-Time Adaptation (TTA) methods for adapting vision-language models, e.g., CLIP, typically rely on Shannon Entropy (SE) at test time...
arXiv:2602.11741v1 Announce Type: new Abstract: Designing a rate limiter that is simultaneously accurate, available, and scalable presents a fundamental challenge in distributed systems, primarily due...
arXiv:2602.11740v1 Announce Type: new Abstract: Efficient exploration is critical for multiagent systems to discover coordinated strategies, particularly in open-ended domains such as search and rescue...
arXiv:2602.11738v1 Announce Type: new Abstract: Probabilistic forecasting of irregularly sampled time series is crucial in domains such as healthcare and finance, yet it remains a...
arXiv:2602.11737v1 Announce Type: new Abstract: We study object hallucination in Multimodal Large Language Models (MLLMs) and improve visual contrastive decoding (VCD) by constructing an object-aligned...
arXiv:2602.11735v1 Announce Type: new Abstract: Cooperative path planning for heterogeneous UAV swarms poses significant challenges for Multi-Agent Reinforcement Learning (MARL), particularly in handling asymmetric inter-agent...
arXiv:2602.11733v1 Announce Type: new Abstract: E-commerce product understanding demands by nature, strong multimodal comprehension from text, images, and structured attributes. General-purpose Vision-Language Models (VLMs) enable...
arXiv:2602.11732v1 Announce Type: new Abstract: We study the fundamental problem of fairly dividing a set of indivisible goods among agents with additive valuations. Here, envy-freeness...
arXiv:2602.11731v1 Announce Type: new Abstract: Existing multimodal large language models have achieved high-fidelity visual perception and exploratory visual generation. However, a precision paradox persists in...
arXiv:2602.11730v1 Announce Type: new Abstract: In vision-language models (VLMs), misalignment between textual descriptions and visual coordinates often induces hallucinations. This issue becomes particularly severe in...