Knowledge catalogue
arXiv:2602.07570v2 Announce Type: replace-cross Abstract: Understanding how humans and artificial intelligence systems process complex narrative videos is a fundamental challenge at the intersection o
arXiv:2503.08633v2 Announce Type: replace Abstract: Machine unlearning is the task of updating a trained model to forget specific training data without retraining from scratch. In this paper, we inves
arXiv:2605.18814v1 Announce Type: new Abstract: Trajectory-based data attribution methods estimate the influence of training samples on model predictions by unrolling the training trajectory. They are
arXiv:2605.19156v1 Announce Type: new Abstract: Recent auto-research systems can produce complete papers, but feasibility is not the same as quality, and the field still lacks a systematic study of ho
arXiv:2603.03140v3 Announce Type: replace-cross Abstract: AI agents are increasingly active on social media platforms, generating content and interacting with one another at scale. Yet the behavioral
arXiv:2605.18822v1 Announce Type: cross Abstract: Post-training has become essential for adapting large language models (LLMs) to complex downstream behaviors, including instruction following, prefere
arXiv:2510.00600v2 Announce Type: replace-cross Abstract: Using Large Language Models to produce intermediate thoughts, a.k.a. Chain-of-thought (CoT), before providing an answer has been a successful
arXiv:2605.18932v1 Announce Type: cross Abstract: In this work, we propose HypergraphFormer, a novel and efficient approach to floor plan generation based on learning hypergraph representations with a
arXiv:2605.18959v1 Announce Type: cross Abstract: The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-
arXiv:2605.19135v1 Announce Type: new Abstract: Causal representation learning (CRL) seeks to uncover meaningful latent variables and their corresponding causal structure from high-dimensional observa
arXiv:2605.19522v1 Announce Type: new Abstract: Pairwise image quality assessment (IQA) in professional photography requires a model not only to identify the preferred image between two candidates, bu
arXiv:2605.19301v1 Announce Type: new Abstract: Vision-Language Models require efficient adaptation to continually emerging downstream tasks. While Parameter-Efficient Fine-Tuning mitigates catastroph
arXiv:2605.19346v1 Announce Type: cross Abstract: We present IMLJD, an open dataset of 3,613 Indian court judgments covering matrimonial disputes under IPC Section 498A, the Protection of Women from D
arXiv:2603.29183v2 Announce Type: replace-cross Abstract: Open-set anomaly detection (OSAD) is an emerging paradigm designed to utilize limited labeled data from anomaly classes seen in training to id
arXiv:2605.19592v1 Announce Type: cross Abstract: Reinforcement learning often produces high-frequency oscillatory control signals that undermine the safety and stability required for physical deploym
arXiv:2605.19458v1 Announce Type: new Abstract: We study the max-margin solutions reached by mirror flow in deep neural networks with homogeneous activation functions. Extending classical results on g
arXiv:2504.03758v4 Announce Type: replace-cross Abstract: Crowd movement simulation is crucial for pedestrian safety management and facility design. Data-driven models offer the potential to improve r
arXiv:2605.18772v1 Announce Type: cross Abstract: Retrieval-Augmented Generation (RAG) improves the factual accuracy of large language model (LLM) outputs by grounding generation in external knowledge
arXiv:2605.18830v1 Announce Type: new Abstract: Regression and Bayesian accounts of in-context learning (ICL) explain how demonstrations can induce predictors, while mechanistic analyses often identif
arXiv:2605.18853v1 Announce Type: cross Abstract: Edge deployment of Vision-Language Models (VLMs) faces a tradeoff between latency and accuracy: cloud execution provides high-quality predictions but
arXiv:2605.19641v1 Announce Type: cross Abstract: Stochastic gradient methods are central to modern large-scale learning, but their use with incomplete covariates remains delicate since imputation sch
arXiv:2605.19317v1 Announce Type: cross Abstract: Inference-time scaling has emerged as a major approach for improving reasoning capabilities, and has been increasingly applied to diffusion models. Ho
arXiv:2605.19644v1 Announce Type: cross Abstract: Knowledge Graphs (KGs) are a powerful representation of linked data, offering flexibility, semantic richness, and support for knowledge enrichment and
arXiv:2605.19152v1 Announce Type: cross Abstract: Physical computing systems provide a promising route toward hardware-native machine learning, but their computational capabilities remain difficult to
arXiv:2605.20088v1 Announce Type: cross Abstract: Discovering shapelets -- i.e., discriminative temporal patterns within time series -- has been widely studied to address the inherent complexity of ti
arXiv:2605.18849v1 Announce Type: cross Abstract: Explainability methods have progressed rapidly, but global explanations for time-series models remain underdeveloped, with most approaches focusing on
arXiv:2605.19042v1 Announce Type: new Abstract: Machine unlearning aims to remove the contribution of designated training data from a trained model while preserving performance on the remaining data.
arXiv:2605.19982v1 Announce Type: new Abstract: Low-Light Image Enhancement (LLIE) has long been a challenging problem in low-level vision, as insufficient illumination often leads to low contrast, de
arXiv:2605.18759v1 Announce Type: cross Abstract: Artificial intelligence (AI), exemplified by large language models (LLMs), is rapidly approaching and in some cases surpassing human performance acros
arXiv:2605.20159v1 Announce Type: new Abstract: Non-destructive testing of aerospace SiC/SiC composites via X-ray computed tomography (XCT) relies on expert visual assessment, with current workflows o
arXiv:2605.19611v1 Announce Type: new Abstract: Inverse design of metasurfaces for specific electromagnetic responses requires generating geometries that satisfy stringent spectral constraints while m
arXiv:2605.19523v1 Announce Type: cross Abstract: Vision-Language Models (VLMs) have demonstrated remarkable proficiency in general multi-modal understanding; yet they struggle to efficiently acquire
arXiv:2508.16112v2 Announce Type: replace Abstract: Spectral analysis provides crucial clues for the elucidation of unknown materials. Among various techniques, infrared spectroscopy (IR) plays an imp
arXiv:2509.22292v2 Announce Type: replace-cross Abstract: Along with the rapid advancement of numerous Text-to-Video (T2V) models, growing concerns have emerged regarding their safety risks. While rec
arXiv:2605.19926v1 Announce Type: new Abstract: The progression of reinforcement learning algorithms have been driven by challenging benchmarks. The rate in which a researcher can iterate on a problem
arXiv:2605.19840v1 Announce Type: new Abstract: For most of human history, we have not thought systematically about how and why we incorporate aspects of the natural world into our designs. The lack o
arXiv:2605.20108v1 Announce Type: cross Abstract: While conventional (k=1) discrete-time barrier certificate conditions impose strict safety constraints by requiring the function to be non-increasing
arXiv:2605.19645v1 Announce Type: new Abstract: Recent advancements in large language models (LLMs) have shown their remarkable capacities in many NLP tasks. However, their substantial size often pres
arXiv:2605.18850v1 Announce Type: cross Abstract: We introduce KadiAssistant, a privacy-by-design AI assistant integrated into the Kadi research data ecosystem, enabling researchers to efficiently acc
arXiv:2605.19031v1 Announce Type: new Abstract: Kolmogorov-Arnold Networks (KANs) have demonstrated an exceptional ability to learn complex functions on clean, low-dimensional data but struggle to mai
arXiv:2605.19435v1 Announce Type: cross Abstract: Visual Place Recognition (VPR) is critical for autonomous navigation, yet state-of-the-art methods lack well-calibrated uncertainty estimation. Standa
arXiv:2605.18895v1 Announce Type: cross Abstract: Safety validation of autonomous driving systems requires high-risk scenario coverage, clear collision semantics, executable trajectories, and attribut
arXiv:2605.19703v1 Announce Type: new Abstract: Autonomous UAV flight in confined, wall-dense environments requires low-latency and reliable motion planning under strict safety constraints. Traditiona
arXiv:2605.19133v1 Announce Type: cross Abstract: Self-supervised learning (SSL) is now a standard way to pretrain medical image models, but performance is still mostly judged by downstream accuracy.
arXiv:2605.20170v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) have shown impressive performances in user-facing tasks such as question answering, as well as consistent improvemen
arXiv:2605.19049v1 Announce Type: cross Abstract: Linear attention has recently gained significant attention for long-context inference due to its constant decoding cost with respect to context length
arXiv:2605.19821v1 Announce Type: new Abstract: Facial Expression Recognition (FER) in the wild is still challenging due to uncontrolled variations in pose, occlusion, and illumination. Most existing
arXiv:2605.19416v1 Announce Type: new Abstract: Group Relative Policy Optimization(GRPO) has become a cornerstone of modern reinforcement learning alignment, prized for its efficacy in foregoing an ex
arXiv:2605.19865v1 Announce Type: new Abstract: Accurate camera viewpoint estimation under sparse-view conditions remains challenging, particularly in two-view scenarios. Recent approaches leverage di
arXiv:2507.01123v2 Announce Type: replace Abstract: Landslides pose severe threats to infrastructure, economies, and human lives, necessitating accurate detection and predictive mapping across diverse
arXiv:2602.13466v2 Announce Type: replace-cross Abstract: The ability of machine learning models to store input information in hidden layer vector embeddings, analogous to the concept of `memory', is
arXiv:2605.19284v1 Announce Type: new Abstract: In the training data used by large language models (LLMs), the same latent concept is often presented in multiple distinct ways: the same facts appear i
arXiv:2605.20050v1 Announce Type: new Abstract: This study investigates how language mutations affect the persistent diffusion of conspiracy theories on social media. Drawing on a three-year dataset o
arXiv:2605.19805v1 Announce Type: cross Abstract: Irregular multivariate time series impose a trade-off for long-horizon forecasting: discrete methods can distort temporal structure via re-gridding, w
arXiv:2605.19008v1 Announce Type: new Abstract: Modern language-model training is increasingly exposed to instability, degraded runs, and wasted compute, especially under aggressive learning-rate, sca
arXiv:2408.06843v5 Announce Type: replace Abstract: We present an efficient task and motion replanning approach for sequential multi-object manipulation in dynamic environments. Conventional Task And
arXiv:2510.27588v3 Announce Type: replace-cross Abstract: We consider the task of constructing a data structure for associating a static set of keys with values, while allowing arbitrary output values
arXiv:2506.01529v2 Announce Type: replace Abstract: Learning meaningful abstract models of Markov Decision Processes (MDPs) is crucial for improving generalization from limited data. In this work, we
arXiv:2605.19562v1 Announce Type: cross Abstract: This paper presents a learning-augmented trajectory planning framework for cooperative unmanned aerial vehicle (UAV) and unmanned ground vehicle (UGV)
arXiv:2510.03485v2 Announce Type: replace Abstract: Autonomous web agents are increasingly deployed for long-horizon tasks, yet their ability to adhere to real-world policies remains critically undere