Knowledge catalogue
arXiv:2502.14541v3 Announce Type: replace Abstract: The rapid advancement of Large Language Models (LLMs) has opened new opportunities in recommender systems by enabling zero-shot recommendation witho
arXiv:2604.26960v1 Announce Type: cross Abstract: Transformer-based agentic AI is rapidly being deployed on major platforms to help users shop, watch, and navigate content with less effort. While thes
arXiv:2604.27032v1 Announce Type: cross Abstract: Large Language Models (LLMs) have become an integral part of many real-world workflows. However, LLMs consume a lot of energy, which becomes a large c
arXiv:2604.27960v1 Announce Type: new Abstract: Recent large language models (LLMs) have achieved impressive reasoning milestones but continue to struggle with high computational costs, logical incons
arXiv:2604.27345v1 Announce Type: new Abstract: Human annotators frequently disagree on emotion labels, yet most evaluations of Large Language Model (LLM) emotion annotation collapse these judgments i
arXiv:2512.03449v3 Announce Type: replace Abstract: Background and Objective: Radiomics of knee MRI requires robust, anatomically meaningful regions of interest (ROIs) that jointly capture cartilage a
arXiv:2601.22228v2 Announce Type: replace-cross Abstract: We study whether vision-language models (VLMs) can solve relative camera pose estimation (RCPE) from image pairs, a direct test of multi-view
arXiv:2604.27487v1 Announce Type: new Abstract: Low-Rank Adaptation (LoRA), which leverages the insight that model updates typically reside in a low-dimensional space, has significantly improved the t
arXiv:2512.07808v2 Announce Type: replace-cross Abstract: Qubit readout is a critical operation in quantum computing systems, which maps the analog response of qubits into discrete classical states. D
arXiv:2509.15549v2 Announce Type: replace Abstract: Multilingual instruction fine-tuning (IFT) empowers large language models to generalize across diverse linguistic and cultural contexts; however, hi
arXiv:2604.27297v1 Announce Type: new Abstract: Deriving governing equations from empirical observations is a longstanding challenge in science. Although artificial intelligence (AI) has demonstrated
arXiv:2604.27804v1 Announce Type: new Abstract: The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and us
arXiv:2604.26973v1 Announce Type: cross Abstract: Multiobjective optimization remains challenging for many scientific and engineering problems due to the need to balance convergence, diversity, and co
arXiv:2602.19483v2 Announce Type: replace-cross Abstract: Quantifying uncertainty in clinical predictions is critical for high-stakes diagnosis tasks. Conformal prediction offers a principled approach
arXiv:2506.23964v3 Announce Type: replace-cross Abstract: Generative ML models are increasingly popular in networking for tasks such as telemetry imputation, prediction, and synthetic trace generation
arXiv:2604.27052v1 Announce Type: cross Abstract: Nonlinear models and optimization methods have successfully tackled a rapidly growing set of problems in recent years. Indeed, a relatively small tool
arXiv:2604.27624v1 Announce Type: cross Abstract: Large Language Models (LLMs) can strongly shape social discourse, yet datasets investigating how LLM outputs vary across controlled social and context
arXiv:2604.28098v1 Announce Type: new Abstract: Research on classroom interaction has long been divided between large-scale observation and in-depth ethnographic work. We propose a framework mapping t
arXiv:2604.28167v1 Announce Type: cross Abstract: In this study, we use machine learning to classify and interpolate the phase structure of the Vicsek flocking model across the three-dimensional param
arXiv:2604.26963v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as the execution core of autonomous agents rather than as standalone text generators. Agentic w
arXiv:2604.27618v1 Announce Type: new Abstract: To enhance LLMs' impact on math education, we need data on their mathematical prowess and biases across prompts. To fill this gap, we introduce MEDS (Ma
arXiv:2604.27819v1 Announce Type: new Abstract: Multi-server MCP agents create an information-flow control problem: faithful tool composition can turn individually benign read/write permissions into c
arXiv:2604.27374v1 Announce Type: new Abstract: As LLMs become credible readers of earnings calls, investor-relations Q&A, guidance, and disclosure language, supervised financial NLP benchmarks increa
arXiv:2604.28061v1 Announce Type: cross Abstract: Numerous metascience studies and other initiatives have begun to monitor the prevalence of open science practices when it is more important to underst
arXiv:2604.27289v1 Announce Type: new Abstract: We present five results in the theory of structural governance for cognitive workflow systems. Three are mechanized in Coq 8.19 using the Interaction Tr
arXiv:2604.27300v1 Announce Type: new Abstract: Metamaterial discovery seeks microstructured materials whose geometry induces targeted mechanical behavior. Existing inverse-design methods can efficien
arXiv:2604.28030v1 Announce Type: cross Abstract: Fairness in machine learning remains challenging due to its ethical complexity, the absence of a universal definition, and the need for context-specif
arXiv:2604.27733v1 Announce Type: new Abstract: Preference learning has become the foundation of aligning Large Language Models (LLMs) with human intent. Popular methods, such as Direct Preference Opt
arXiv:2604.27393v1 Announce Type: new Abstract: Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming inter
arXiv:2603.21016v2 Announce Type: replace-cross Abstract: Large language models (LLMs) used for multiple-choice and pairwise evaluation tasks often exhibit selection bias due to non-semantic factors l
arXiv:2510.23498v2 Announce Type: replace-cross Abstract: Exploiting low-precision computations has become a standard strategy in deep learning to address the growing computational costs imposed by ev
arXiv:2509.20491v2 Announce Type: replace-cross Abstract: The rapid adoption of Artificial Intelligence (AI) is increasingly realised through Machine Learning (ML) pipelines that integrate data prepro
arXiv:2604.27934v1 Announce Type: new Abstract: Multimodal Stance Detection (MSD) is crucial for understanding public discourse, yet effectively fusing text and image, especially with conflicting sign
arXiv:2512.10881v2 Announce Type: replace Abstract: Motion capture now underpins content creation far beyond digital humans, yet most existing pipelines remain species- or template-specific. We formal
arXiv:2604.28130v1 Announce Type: new Abstract: Recent methods for arbitrary-skeleton motion capture from monocular video follow a factorized pipeline, where a Video-to-Pose network predicts joint pos
arXiv:2604.27872v1 Announce Type: new Abstract: Large language model (LLM) agents deployed in clinical settings often exhibit abrupt, threshold-driven behavior, offering little visibility into accumul
arXiv:2407.08668v3 Announce Type: replace-cross Abstract: In this work, we propose a simulation-based estimation approach using generative neural networks to determine dependencies of precipitation ma
arXiv:2604.28031v1 Announce Type: new Abstract: When researchers iteratively refine ideas with large language models, do the models preserve fidelity to the original objective? We introduce DriftBench
arXiv:2604.26984v1 Announce Type: new Abstract: Representational collapse, where embeddings become anisotropic and lose multi-scale structure, can erode downstream performance long before performance
arXiv:2604.27792v1 Announce Type: new Abstract: Vision-Language-Action (VLA) models achieve strong semantic generalization but often lack fine-grained modeling of world dynamics. Recent work explores
arXiv:2604.27654v1 Announce Type: new Abstract: Accurate CT-MRI registration of the cervical spine is essential for preoperative planning because this region is anatomically complex,highly variable,an
arXiv:2512.10941v2 Announce Type: replace-cross Abstract: Reasoning goes beyond language; the real world requires reasoning about space, time, affordances, and much more that words alone cannot convey
arXiv:2604.27846v1 Announce Type: new Abstract: How people narrate their experiences offers a window into how the mind organizes them. Computational approaches to therapeutic writing have evolved from
arXiv:2604.26979v1 Announce Type: cross Abstract: In-memory computing (IMC) enables energy-efficient neural network inference by computing analog matrix-vector multiplications (MVM) in memory crossbar
arXiv:2504.02768v4 Announce Type: replace Abstract: We introduce MultiBLiMP 1.0, a massively multilingual benchmark of linguistic minimal pairs, covering 101 languages and 2 types of subject-verb agre
arXiv:2602.20122v2 Announce Type: replace-cross Abstract: How do large language models (LLMs) know what they know? Answering this question has been difficult because pre-training data is often a 'blac
arXiv:2510.18183v2 Announce Type: replace Abstract: Finding Nash equilibria in two-player zero-sum imperfect-information games remains a central challenge in multi-agent reinforcement learning. Recent
arXiv:2604.27852v1 Announce Type: cross Abstract: Although precise recall is a core objective in Retrieval-Augmented Generation (RAG), a critical oversight persists in the field: improvements in retri
arXiv:2604.28107v1 Announce Type: new Abstract: Accurate state estimation of nonlinear dynamical systems is fundamental to modern aerospace operations across air, sea, and space domains. Online tracki
arXiv:2604.27889v1 Announce Type: new Abstract: Semantic segmentation and change detection are two fundamental challenges in remote sensing, requiring models to capture either spatial semantics or tem
arXiv:2604.27031v1 Announce Type: cross Abstract: In a continual learning setting, we require a model to be plastic enough to learn a new task and stable enough to not disturb previously learned capab
arXiv:2604.28125v1 Announce Type: new Abstract: Sign languages, of any geographical or accentual variation, understandably face continuous scrutiny under the ever present popularity of verbal dictatio
arXiv:2604.26970v1 Announce Type: cross Abstract: Knowledge graphs used for retrieval treat all facts as equally current. Existing temporal approaches apply uniform decay, using a single forgetting cu
arXiv:2604.27820v1 Announce Type: new Abstract: Every document format in existence was designed for a human reader moving linearly through text. Autonomous LLM agents do not read - they retrieve. This
arXiv:2512.10955v2 Announce Type: replace Abstract: Visual concept personalization aims to transfer only specific image attributes, such as identity, expression, lighting, and style, into unseen conte
arXiv:2512.14044v3 Announce Type: replace-cross Abstract: The deployment of Vision-Language Models (VLMs) in safety-critical domains like autonomous driving (AD) is critically hindered by reliability
arXiv:2604.28197v1 Announce Type: cross Abstract: Human-robot collaboration has been studied primarily in dyadic or sequential settings. However, real homes require multiadic collaboration, where mult
arXiv:2604.27786v1 Announce Type: new Abstract: Semidefinite programs (SDPs) are a powerful framework for convex optimization and for constructing strong relaxations of hard combinatorial problems. Ho
arXiv:2511.17176v2 Announce Type: replace-cross Abstract: Accurate prediction of extreme weather events remains a major challenge for artificial intelligence-based weather prediction systems. While de
arXiv:2604.28147v1 Announce Type: new Abstract: Surprisal theory links human processing effort to the predictability of an upcoming linguistic unit, but empirical work often leaves the notion of a uni