Knowledge catalogue
arXiv:2606.01670v1 Announce Type: cross Abstract: Recently, Generative Recommenders (GRs) have emerged as a transformative recommendation paradigm by replacing traditional item IDs with semantic indic
arXiv:2606.01169v1 Announce Type: cross Abstract: Spacecraft collision avoidance for low-thrust satellites often requires determining not only how to maneuver, but also how late a maneuver can begin w
arXiv:2606.02142v1 Announce Type: new Abstract: The ongoing digitization has led to a proliferation of time-series data streams that monitor a variety of processes, from which valuable insights may be
arXiv:2606.01498v1 Announce Type: cross Abstract: Time series data inform critical decisions across many real-world domains. While large language model (LLM) agents can analyze data through natural la
arXiv:2606.00895v1 Announce Type: cross Abstract: This paper presents a fast, recursive neural solver for the J2-perturbed Lambert problem based on Tiny Recursive Models (TRM), termed the TRM-Perturbe
arXiv:2606.01591v1 Announce Type: new Abstract: The TimeLogic Challenge evaluates formal temporal-logic reasoning over video - 16 operators (before, after, until, since, always, co-occur, ordering, ..
arXiv:2606.01540v1 Announce Type: cross Abstract: Shapley values are a widely used tool for attributing importance and interactions among input variables in black-box models, but their computation inv
arXiv:2606.01810v1 Announce Type: new Abstract: Current benchmarks for embodied vision-language planning often favor linguistic next-token prediction over physically grounded next-state reasoning. Thi
arXiv:2505.22961v3 Announce Type: replace Abstract: Large language models (LLMs) have shown promising potential in persuasion, but existing works on training LLM persuaders are still preliminary. Nota
arXiv:2606.02518v1 Announce Type: new Abstract: Fine-grained image classification (FGIC) has broad applications and has attracted significant research attention. In this paper, we explore a novel para
arXiv:2602.07883v3 Announce Type: replace Abstract: LLM-powered agentic systems excel at complex long-horizon tasks, but remain constrained by static configurations fixed before execution. Such rigidi
arXiv:2606.01184v1 Announce Type: cross Abstract: Many interventions alter the structure of an outcome distribution rather than its mean: they can split a population into disconnected regimes, create
arXiv:2606.02048v1 Announce Type: new Abstract: We propose a novel computational toolbox that integrates Topological Data Analysis (TDA), Differential Box Counting (DBC), Multifractal Partition (MFP),
arXiv:2606.00427v1 Announce Type: new Abstract: State abstraction in reinforcement learning is usually formulated as a partition of states based on reward and transition similarity. This excludes a co
arXiv:2606.00496v1 Announce Type: new Abstract: Oscillatory neural signals such as electroencephalography (EEG) and local field potentials (LFPs) show phase relationships that coordinate communication
arXiv:2507.09766v2 Announce Type: replace-cross Abstract: Accurate estimation of Remaining Useful Life (RUL) and State of Health (SoH) is essential for reliable Prognostics and Health Management (PHM)
arXiv:2606.00596v1 Announce Type: new Abstract: Large language models have rapidly evolved in multilingual competence and reasoning capacity, enabling their integration into Social Sciences and Humani
arXiv:2606.00014v1 Announce Type: cross Abstract: Although studies have demonstrated that Large Language Models (LLMs) can perform well on Out-of-Distribution (OOD) tasks, their advantage tends to dim
arXiv:2606.02000v1 Announce Type: cross Abstract: Diffusion models have shown remarkable success in video generation. However, whether such models are truly aware of the 3D structure underlying visual
arXiv:2605.22759v2 Announce Type: replace Abstract: While ubiquitous wearable sensors capture a wealth of behavioral and physiological information, effectively transforming these signals into personal
arXiv:2509.13805v4 Announce Type: replace-cross Abstract: Foundation models have revolutionized natural language processing through a ``train once, deploy anywhere'' paradigm, where a single pre-train
arXiv:2509.16635v2 Announce Type: replace Abstract: In real applications, person re-identification (ReID) is expected to retrieve the target person at any time, including both daytime and nighttime, r
arXiv:2606.02507v1 Announce Type: cross Abstract: Inverse materials design is shifting materials discovery from forward prediction to targeted proposal of candidates that satisfy objectives under phys
arXiv:2606.01164v1 Announce Type: new Abstract: With rapid development of large language models and diffusion-based content generation, world modeling has attracted increasing research attention, bene
arXiv:2606.00919v1 Announce Type: new Abstract: Large language models (LLMs) have seen widespread adoption across various domains, yet their reliability is frequently undermined by hallucinations - re
arXiv:2606.02487v1 Announce Type: new Abstract: Effective 'all-team' summarization in high-complexity settings like the Neonatal Intensive Care Unit (NICU) requires aggregating insights from diverse d
arXiv:2606.01342v1 Announce Type: cross Abstract: Learning-augmented paging has been extensively studied in recent years. A key advantage over naive ML-based approaches is bounded robustness, which gu
arXiv:2606.02313v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models offer a promising end-to-end paradigm for unmanned aerial vehicles (UAVs) to accomplish complex tasks specified by f
arXiv:2606.02242v1 Announce Type: cross Abstract: The joint optimization of image-based (I2I) and text-based (T2I) person re-identification (ReID) is hindered by modality discrepancies and conflicting
arXiv:2412.19444v2 Announce Type: replace Abstract: Optimization algorithms such as AdaGrad and Adam have significantly advanced the training of deep models by dynamically adjusting the learning rate
arXiv:2602.13602v2 Announce Type: replace Abstract: We present revise (nderline{Re}asoning with nderline{Vi}deo nderline{S}parsity), a multi-round agent for video question answering (VQA). Instead of
arXiv:2410.09737v2 Announce Type: replace Abstract: A popular way to improve the expressive power of graph neural networks (GNNs) is to use Laplacian eigenvectors as additional node features, since th
arXiv:2606.00959v1 Announce Type: new Abstract: Understanding modality interaction in multimodal large language models (MLLMs) is central to reliable deployment. We introduce Partial Information Decom
arXiv:2606.00611v1 Announce Type: new Abstract: Long-horizon LLM agents produce safety evidence across long trajectories, where sparse, delayed, and compositional risk signals often escape local moder
arXiv:2606.00040v1 Announce Type: cross Abstract: As Generative AI (GenAI) becomes integral to education, fostering GenAI literacy is critical. However, current assessments largely rely on self-report
arXiv:2606.02536v1 Announce Type: new Abstract: Text files such as skill files, memory files, and behavioral configuration files play a central role in defining how modern agents act. Through edits by
arXiv:2510.22138v3 Announce Type: replace Abstract: We show how to replace the O(2^n) coalition enumeration over n features behind Shapley values and Shapley-style interaction indices with a few-evalu
arXiv:2604.17456v2 Announce Type: replace Abstract: Large language model (LLM) agents have shown strong capabilities in long-horizon reasoning, tool use, and decision-making in digital environments, y
arXiv:2606.01737v1 Announce Type: new Abstract: Traffic accident liability analysis is a critical yet challenging task in intelligent transportation and legal assistance. Existing methods often suffer
arXiv:2606.01896v1 Announce Type: cross Abstract: Generated (or synthetic) image data is increasingly used to augment or replace real training datasets when target imagery is scarce, expensive, or bia
arXiv:2606.02321v1 Announce Type: new Abstract: Recent advances in large vision-language models have expanded video retrieval from simple text-based search to more flexible scenarios, where users may
arXiv:2606.00158v1 Announce Type: cross Abstract: Continuous variable-rate compression is highly demanded in real-world applications, but remains underexplored in scalable image coding for humans and
arXiv:2603.09390v2 Announce Type: replace Abstract: Coverless Image Steganography (CIS) hides information without explicitly modifying a cover image, providing strong imperceptibility and inherent rob
arXiv:2606.01380v1 Announce Type: new Abstract: In this work, we introduce a novel training-free inversion (TFinv) framework for one-step diffusion models,addressing key challenges in real image inver
arXiv:2606.01238v1 Announce Type: cross Abstract: While diffusion-based policies have impressive performance and expressivity, their long offline training slows down the data collection and policy dep
arXiv:2606.00321v1 Announce Type: new Abstract: In fulfillment centers, diverse objects move continuously from inbound to outbound operations and can become jammed due to excessive conveyor friction,
arXiv:2606.01967v1 Announce Type: new Abstract: While prompt engineering is instrumental in maximizing the capabilities of Large Language Models (LLMs) during inference, the role of prompts during tra
arXiv:2510.03494v2 Announce Type: replace Abstract: We study finite-horizon offline reinforcement learning (RL) with function approximation for both policy evaluation and policy optimization. Prior wo
arXiv:2602.12724v3 Announce Type: replace Abstract: This study introduces TRANS: Terrain-aware Reinforcement learning for Agile Navigation under Social interactions, a deep reinforcement learning (DRL
arXiv:2606.01777v1 Announce Type: new Abstract: Transparent objects remain challenging for robotic perception due to unreliable depth sensing caused by refraction and reflection. While prior approache
arXiv:2606.02545v1 Announce Type: new Abstract: Self-harm is a major public health concern, but current surveillance relying on hospital presentations is inadequate due to the low sensitivity of diagn
arXiv:2606.01457v1 Announce Type: new Abstract: Bayesian optimization is a popular way to optimize expensive systems, where every experiment, simulation, or intervention costs time or money. In its st
arXiv:2606.01046v1 Announce Type: new Abstract: The development of Large Language Models (LLMs) has significantly improved travel planning applications, yet evaluating such models is limited by existi
arXiv:2606.01799v1 Announce Type: new Abstract: We study N-armed stochastic dueling bandits under the Condorcet-winner assumption, where three widely adopted objectives are considered: best-arm identi
arXiv:2304.11127v5 Announce Type: replace-cross Abstract: Recent scientific advances require complex experiment design, necessitating the meticulous tuning of many experiment parameters. Tree-structur
arXiv:2606.01755v1 Announce Type: new Abstract: Personalized large language models adapt responses to users' preferences and social attributes, but can introduce substantial universal truth inconsiste
arXiv:2606.01033v1 Announce Type: new Abstract: When a language model hallucinates, the final answer is wrong, but the mistake is not necessarily invisible inside the model. Different internal pathway
arXiv:2606.01599v1 Announce Type: new Abstract: Reinforcement learning (RL) for visual reasoning needs scalable, verifiable, and controllable training signals. Existing visual RL post-training trains
arXiv:2606.02350v1 Announce Type: new Abstract: Reconstructing humans and their surrounding environments in a globally consistent 4D space is essential for comprehensive perception. However, prior wor
arXiv:2606.01000v1 Announce Type: cross Abstract: Weak-to-strong generalization studies how to improve a strong student using supervision from a weaker teacher when reliable labels are scarce. We view