Knowledge catalogue
arXiv:2608.18767v1 Announce Type: new Abstract: Gradient matching attacks (GMAs) in LLM split learning (SL) rely on a critical yet underexplored assumption: the gradient exposed at the split interface
arXiv:2608.19009v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly paired with verifiers (step checkers, self-consistency filters, tool-based fact checkers, formal proof ass
arXiv:2608.18887v1 Announce Type: new Abstract: The epileptogenic zone (EZ) is the brain region that generates seizures in an individual, and is the target of epilepsy surgery. Localizing the EZ from
arXiv:2608.18936v1 Announce Type: cross Abstract: Designing, implementing, and comparing interpretable architectures requires a formal language to represent them. The most common representations fall
arXiv:2608.18777v1 Announce Type: new Abstract: Graph generation models have advanced significantly with deep learning, yet they remain limited in scalability, flexibility, and ability to model underl
arXiv:2608.18752v1 Announce Type: cross Abstract: Statutory retrieval is necessary for citation-grounded legal question answering, but remains underexplored for Greek. We introduce GreekBarRetrieval,
arXiv:2608.19140v1 Announce Type: new Abstract: Frontier language models are compared, marketed, and benchmarked on capability -- what their best or average output can achieve. I argue this measures t
arXiv:2608.19066v1 Announce Type: cross Abstract: This paper proposes a lightweight, plug-and-play framework that improves robustness to viewpoint shifts in Vision-Language-Action (VLA) policies witho
arXiv:2608.18292v1 Announce Type: cross Abstract: Consider a robot guide dog escorting a blind user to an available seat while a second assistive robot dog concurrently retrieves a cup of coffee and d
arXiv:2608.18185v1 Announce Type: cross Abstract: Fine-grained recognition often involves hierarchical label spaces, where a model may be confident about a coarse semantic concept while remaining unce
arXiv:2607.10476v2 Announce Type: replace Abstract: Large language models (LLMs) have emerged as a powerful tool for retrieving knowledge through seamless, human-like interactions. Despite their advan
arXiv:2608.19013v1 Announce Type: cross Abstract: Continual learning has largely been model-centric, treating model parameters as the state that changes with sequential experience. Modern agents can a
arXiv:2608.18446v1 Announce Type: new Abstract: End-to-end imitation learning avoids hand-made robot motion for approaching and grasping, but the policy must still decide which fruit to pick and where
arXiv:2608.18306v1 Announce Type: new Abstract: Single-photon cameras based on single-photon avalanche diode (SPAD) technology are gaining popularity for 3D sensing, thanks to their extreme sensitivit
arXiv:2608.18265v1 Announce Type: cross Abstract: We introduce a general, easy-to-implement AI-based method for studying the structure and complexity of human behavior. We assign a large language mode
arXiv:2607.16094v2 Announce Type: replace Abstract: Compositional visual question answering requires Vision-Language Models (VLMs) to execute multiple reasoning operations like object selection, spati
arXiv:2608.18155v1 Announce Type: cross Abstract: Quantum machine learning (QML) for network intrusion detection (NIDS) is routinely reported to reach near-perfect accuracy, yet the most rigorous stud
arXiv:2608.18184v1 Announce Type: new Abstract: Human-centric intelligence is evolving in the foundation-model era, with growing emphasis on scale, transferability, and general-purpose modeling. Yet i
arXiv:2606.20151v2 Announce Type: replace-cross Abstract: This work proposes a hybrid ANN-SNN pipeline that effectively leverages the rich embeddings of pretrained artificial neural networks (ANNs) to
arXiv:2509.04100v3 Announce Type: replace Abstract: This paper explores the combination of Reinforcement Learning (RL) and search-based path planners to speed up the optimization of flight paths for a
arXiv:2608.18821v1 Announce Type: cross Abstract: The logical reconstruction of argument graphs from natural language text is challenging because of the prevalence of enthymemes (i.e., arguments with
arXiv:2608.19177v1 Announce Type: new Abstract: Ground Penetrating Radar (GPR) is a widely adopted non-destructive sensing technology for subsurface inspection in civil and transportation engineering.
arXiv:2607.18197v2 Announce Type: replace Abstract: Seamless human-robot interaction (HRI) requires a number of perceptual and motor abilities from the robot, one of them being the imitation of human
arXiv:2608.18696v1 Announce Type: cross Abstract: Digitizing the text from handwritten historical manuscripts is required to make them easily accessible, preservable, and to enable historical scholars
arXiv:2608.18409v1 Announce Type: new Abstract: Combinatorial scheduling poses a significant challenge for language models, requiring them to identify feasible solutions within exponentially large sea
arXiv:2608.18135v1 Announce Type: new Abstract: Introduction: Medication errors (MEs) represent a significant threat to global healthcare systems, contributing to patient harm. Introducing artificial
arXiv:2608.18374v1 Announce Type: cross Abstract: We address two open questions in streaming PCA via Oja's algorithm: sharp operator-norm convergence for general rank under sub-Gaussian data, and dist
arXiv:2608.18592v1 Announce Type: new Abstract: Whether distinct neural architectures develop common collective dynamics remains an open question. Recent analysis of Transformer language models reveal
arXiv:2608.19026v1 Announce Type: new Abstract: Released in 2025, Institutional Books: Harvard Library (IB-HL) is a collection of 983,004 volumes (242B o200k_base tokens), originally digitized through
arXiv:2608.18957v1 Announce Type: new Abstract: Historical book collections contain rich visual elements - such as illustrations, photographs, engravings, and decorative art - that are frequently unde
arXiv:2608.18972v1 Announce Type: new Abstract: Historical newspapers are an abundant record of public life, but their dense, irregular and sometimes noisy layouts make computational access to these m
arXiv:2608.18107v1 Announce Type: cross Abstract: We investigate whether large language models (LLMs) systematically discriminate in candidate evaluations based on applicant name ethnicity and/or inst
arXiv:2502.02891v2 Announce Type: replace-cross Abstract: Recent advances in implicit neural representations have made them a popular choice for modeling 3D geometry. However, directly editing these r
arXiv:2608.19124v1 Announce Type: cross Abstract: Astrolinguistics -- communication with minds that categorize reality differently from ours -- has been purely speculative since Freudenthal's Lincos (
arXiv:2608.19163v1 Announce Type: cross Abstract: Seasonal precipitation anomalies are largely regulated by atmospheric circulation, which dynamical models predict with greater reliability than precip
arXiv:2608.19006v1 Announce Type: new Abstract: Hate speech is a real and timely threat that affects a large portion of online users, especially youth and minority groups. While building reliable and
arXiv:2502.16445v4 Announce Type: replace-cross Abstract: Generative models for image generation are now commonly used for a wide variety of applications, ranging from guided image generation for ente
arXiv:2511.04707v2 Announce Type: replace-cross Abstract: Recent advances in long-context language models (LMs) have enabled million-token inputs, expanding their capabilities across complex tasks lik
arXiv:2608.18412v1 Announce Type: new Abstract: Effective sign language (SL) acquisition is crucial for deaf children, yet 95% are born to hearing parents who often lack proficiency in SL. SL recognit
arXiv:2603.17566v2 Announce Type: replace Abstract: Fine-tuning large language models (LLMs) with high-quality knowledge has been shown to enhance their performance effectively. However, there is a pa
arXiv:2608.18618v1 Announce Type: new Abstract: Autonomous laboratories hold great promise for accelerating scientific discovery. To achieve this vision, robots are supposed to dexterously manipulate
arXiv:2601.01684v2 Announce Type: replace-cross Abstract: While dense retrieval models have been the standard for state-of-the-art information retrieval, their deployment is often constrained by high
arXiv:2608.18138v1 Announce Type: cross Abstract: In recent years, the rapid development of language models has transformed the field of Natural Language Processing through a wide range of application
arXiv:2512.00020v3 Announce Type: replace-cross Abstract: Code generation has emerged as a critical research area at the intersection of Software Engineering (SE) and Artificial Intelligence (AI), att
arXiv:2608.18080v1 Announce Type: new Abstract: We present a review on the applications of large language models (LLMs) in health, e.g., social media analysis, clinical conversational agents, therapy
arXiv:2608.18089v1 Announce Type: cross Abstract: Instruction-tuned models often refuse harmful requests in English but comply with the same requests in Yoruba, Igbo, Igala, and Hausa. This suggests t
arXiv:2608.19127v1 Announce Type: cross Abstract: A gradient-boosted ensemble predicts by summing one leaf value per tree. Read those values as coordinates rather than as intermediate results, and eve
arXiv:2608.19168v1 Announce Type: new Abstract: A single training example's contribution to a finished model is normally estimated rather than measured, because measuring it takes two expensive full p
arXiv:2608.18765v1 Announce Type: new Abstract: Register automata are finite automata equipped with memory that recognize data languages over infinite alphabets. In this work, we investigate active le
arXiv:2608.19082v1 Announce Type: cross Abstract: We present a new data-driven learning of a Random Geometric Graph (RGG) of a multivariate dataset, where the graph is drawn in a probabilistic metric
arXiv:2608.18907v1 Announce Type: cross Abstract: Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative
arXiv:2608.18570v1 Announce Type: cross Abstract: Machine learning and data analysis techniques have recently emerged as powerful tools for identifying patterns and formulating conjectures in mathemat
arXiv:2608.18681v1 Announce Type: new Abstract: We introduce a failure-aware adversarial retrieval-augmented framework for improving robustness in natural language understanding. Rather than selecting
arXiv:2608.18398v1 Announce Type: cross Abstract: Large language model (LLM) agents can now carry out long-horizon technical workflows involving complex tool use, code execution, file edits, and gener
arXiv:2608.19171v1 Announce Type: new Abstract: Deep models for irregularly-sampled time series answer queries at arbitrary continuous timestamps, yet report nothing about how far each answer should b
arXiv:2510.03115v2 Announce Type: replace Abstract: Speech-to-Text Translation (S2TT) systems built from Automatic Speech Recognition (ASR) and Text-to-Text Translation (T2TT) modules face two major l
arXiv:2608.18503v1 Announce Type: new Abstract: The growing demand for AI-driven workloads, particularly from Large Language Models (LLMs), has raised concerns about the significant energy and resourc
arXiv:2608.18082v1 Announce Type: new Abstract: Although context windows have expanded significantly in recent years, hallucinations in long-context summarization remain a challenge. Long novels are b
arXiv:2607.16841v2 Announce Type: replace Abstract: Large vision-language models (LVLMs) have demonstrated remarkable capabilities in multimodal understanding. However, they remain prone to hallucinat
arXiv:2608.18171v1 Announce Type: new Abstract: Looped language models have shown promising results on reasoning benchmarks, yet their potential for agentic tool use remains largely unexplored. We stu