Knowledge catalogue
arXiv:2603.03959v4 Announce Type: replace-cross Abstract: Code comment classification is a critical task for automated software documentation and analysis. In the context of the NLBSE'26 Tool Competit
arXiv:2604.13066v1 Announce Type: new Abstract: In-context learning has established itself as an important learning paradigm for Large Language Models (LLMs). In this paper, we demonstrate that LLMs c
arXiv:2604.13432v1 Announce Type: new Abstract: Token compression is crucial for mitigating the quadratic complexity of self-attention mechanisms in Vision Transformers (ViTs), which often involve num
arXiv:2604.14016v1 Announce Type: new Abstract: Multimodal Continual Instruction Tuning (MCIT) is essential for sequential task adaptation of Multimodal Large Language Models (MLLMs) but is severely r
arXiv:2604.13970v1 Announce Type: new Abstract: In diagnostic reports, experts encode complex imaging data into clinically actionable information. They describe subtle pathological findings that are m
arXiv:2604.13062v1 Announce Type: new Abstract: Recent advances in large language models (LLMs) have demonstrated strong capabilities in code generation and text synthesis, yet their potential for sym
arXiv:2505.03280v3 Announce Type: replace Abstract: In many practical sequential decision-making problems, tracking the state of the environment incurs a sensing/communication/computation cost. In the
arXiv:2604.13695v1 Announce Type: new Abstract: Reliable and interpretable decision-making is essential in medical imaging, where diagnostic outcomes directly influence patient care. Despite advances
arXiv:2604.13756v1 Announce Type: new Abstract: The potential of Multimodal Large Language Models (MLLMs) in domain of medical imaging raise the demands of systematic and rigorous evaluation framework
arXiv:2604.14004v1 Announce Type: cross Abstract: Memory-based self-evolution has emerged as a promising paradigm for coding agents. However, existing approaches typically restrict memory utilization
arXiv:2508.06433v4 Announce Type: replace Abstract: Large Language Models (LLMs) based agents excel at diverse tasks, yet they suffer from brittle procedural memory that is manually engineered or enta
arXiv:2604.13418v1 Announce Type: new Abstract: Motivated by the underspecified, multi-hop nature of search queries and the multimodal, heterogeneous, and often conflicting nature of real-world web re
arXiv:2604.13414v1 Announce Type: new Abstract: Majority-vote ensembles achieve variance reduction by averaging over diverse, approximately independent base learners. When training data exhibits Marko
arXiv:2512.06695v2 Announce Type: replace Abstract: Quantum generative models exploit quantum superposition and entanglement to enhance learning efficiency for both classical and quantum data. Recentl
arXiv:2512.04844v2 Announce Type: replace Abstract: Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the relianc
arXiv:2506.09207v4 Announce Type: replace Abstract: Accurately solving partial differential equations (PDEs) is essential across many scientific disciplines. However, high-fidelity solvers can be comp
arXiv:2604.13579v1 Announce Type: new Abstract: Conventional Retrieval-Augmented Generation (RAG) systems often struggle with complex multi-hop queries over long documents due to their single-pass ret
arXiv:2510.05786v3 Announce Type: replace-cross Abstract: Mobius inversion and Shapley values are two mathematical tools for characterizing and decomposing higher-order structure in complex systems. T
arXiv:2510.05056v2 Announce Type: replace Abstract: As programmers write code, they often edit and retry multiple times, creating rich 'interaction traces' that reveal how they approach coding tasks a
arXiv:2604.13897v1 Announce Type: new Abstract: We present an open Molecular Crystal (MC) database of Machine-Learned Interatomic Potentials (MLIP) called MolCryst-MLIPs. The first release comprises f
arXiv:2604.14108v1 Announce Type: new Abstract: Recent work suggests that (stochastic) gradient descent self-organizes near an instability boundary, shaping both optimization and the solutions found.
arXiv:2604.13481v1 Announce Type: new Abstract: Here, we describe Monthly Diffusion at 1.5-degree grid spacing (MD-1.5 version 0.9), a climate emulator that leverages a spherical Fourier neural operat
arXiv:2604.13287v1 Announce Type: new Abstract: Weight pruning is a common technique for compressing large neural networks. We focus on the challenging post-training one-shot setting, where a pre-trai
arXiv:2604.13853v1 Announce Type: new Abstract: Safe and explainable motion planning remains a central challenge in autonomous driving. While rule-based planners offer predictable and explainable beha
arXiv:2604.13340v1 Announce Type: new Abstract: We present a multispectral extension to 3D Gaussian Splatting (3DGS) for wavelength-aware view synthesis. Each Gaussian is augmented with spectral radia
arXiv:2505.07591v2 Announce Type: replace Abstract: Instruction following refers to the ability of large language models (LLMs) to generate outputs that satisfy all specified constraints. Existing res
arXiv:2604.13345v1 Announce Type: new Abstract: The paper presents design and prototype implementation of an edge based object detection system within the new paradigm of AI agents orchestration. It g
arXiv:2601.15170v2 Announce Type: replace Abstract: The rapid expansion of research across machine learning, vision, and language has produced a volume of publications that is increasingly difficult t
arXiv:2604.13142v1 Announce Type: cross Abstract: We present two multi-modal panoramic 3D outdoor (MPO) datasets for semantic place categorization with six categories: forest, coast, residential area,
arXiv:2604.13328v1 Announce Type: new Abstract: Pathology reports serve as the definitive record for breast cancer staging, yet their unstructured format impedes large-scale data curation. While Large
arXiv:2604.14075v1 Announce Type: cross Abstract: We introduce Multistage Conditional Compositional Optimization (MCCO) as a new paradigm for decision-making under uncertainty that combines aspects of
arXiv:2604.13217v1 Announce Type: new Abstract: Reliable evaluation of blastocyst quality is critical for the success of in vitro fertilization (IVF) treatments. Current embryo grading practices prima
arXiv:2604.13828v1 Announce Type: new Abstract: User simulators are essential for the scalable training and evaluation of interactive AI systems. However, existing approaches often rely on shallow use
arXiv:2604.13456v1 Announce Type: cross Abstract: Woody Breast (WB) and Spaghetti Meat (SM) myopathies significantly impact poultry meat quality, yet current detection methods rely either on subjectiv
arXiv:2510.07019v3 Announce Type: replace Abstract: Transformers excel at sequence modeling but face quadratic complexity, while linear attention offers improved efficiency but often compromises recal
arXiv:2604.13919v1 Announce Type: cross Abstract: Computational fluid dynamics (CFD) has become an essential tool for predicting fire behavior, yet maintaining both efficiency and accuracy remains cha
arXiv:2604.13235v1 Announce Type: new Abstract: Digital elevation modeling of planetary surfaces is essential for studying past and ongoing geological processes. Wide-angle imagery acquired during spa
arXiv:2604.14073v1 Announce Type: new Abstract: Resolving and rewriting references is fundamental in programming languages. Motivated by a real-world decompilation task, we abstract reference rewritin
arXiv:2601.11340v2 Announce Type: replace Abstract: Chain-of-Thought reasoning has significantly enhanced the problem-solving capabilities of Large Language Models. Unfortunately, current models gener
arXiv:2504.13228v4 Announce Type: replace Abstract: Mean-field game theory relies on approximating games that are intractable to model due to a very large to infinite population of players. While thes
arXiv:2604.14021v1 Announce Type: new Abstract: Maintaining stable internal representations of continuous variables is fundamental for effective robotic control. Continuous attractor networks provide
arXiv:2604.13681v1 Announce Type: cross Abstract: The node2vec random walk is a non-Markovian random walk on the vertex set of a graph, widely used for network embedding and exploration. This random w
arXiv:2405.07432v4 Announce Type: replace-cross Abstract: The Koopman operator provides a powerful framework for representing the dynamics of general nonlinear dynamical systems. However, existing dat
arXiv:2506.08125v3 Announce Type: replace-cross Abstract: Large language models (LLMs) show strong reasoning abilities but often produce unnecessarily long explanations that reduce efficiency. Althoug
arXiv:2604.13206v1 Announce Type: cross Abstract: As Large Language Models (LLMs) are increasingly integrated into agentic workflows, their unpredictability stemming from numerical instability has eme
arXiv:2604.13121v1 Announce Type: new Abstract: Locating and intercepting a moving target from possibly delayed, intermittent sensory signals is a paradigmatic problem in decision-making under uncerta
arXiv:2604.13073v1 Announce Type: new Abstract: Modern multimodal large language models (MLLMs) generate fluent responses from interleaved text, image, audio, and video inputs. However, identifying wh
arXiv:2408.10610v5 Announce Type: replace Abstract: We propose an L^2 norm for stationary Autoregressive Moving Average (ARMA) models. We look at ARMA models within the Hilbert space of the past with
arXiv:2507.14005v2 Announce Type: replace Abstract: It was recently shown that dynamic programming (DP) methods for finding static CVaR-optimal policies in Markov Decision Processes (MDPs) can fail wh
arXiv:2604.14149v1 Announce Type: new Abstract: Long video understanding is inherently challenging for vision-language models (VLMs) because of the extensive number of frames. With each video frame ty
arXiv:2604.14062v1 Announce Type: new Abstract: Human-Object Interaction (HOI) modelling captures how humans act upon and relate to objects, typically expressed as triplets. Existing approaches split
arXiv:2604.13740v1 Announce Type: new Abstract: We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback abo
arXiv:2602.19315v2 Announce Type: replace Abstract: Underwater glider robots have become indispensable for ocean sampling, yet fully autonomous long-term operation remains rare in practice. Although s
arXiv:2603.06885v2 Announce Type: replace Abstract: Trachoma remains the leading infectious cause of blindness worldwide, with Sub-Saharan Africa bearing over 85% of the global burden and Ethiopia alo
arXiv:2604.13672v1 Announce Type: new Abstract: The `spotoptim` package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential
arXiv:2602.07924v2 Announce Type: replace Abstract: Securing petroleum infrastructure requires balancing autonomous system efficiency with human judgment for threat escalation, a challenge unaddressed
arXiv:2604.13283v1 Announce Type: cross Abstract: Earth Observation (EO) satellite scheduling (deciding which imaging tasks to perform and when) is a well-studied combinatorial optimization problem. E
arXiv:2604.13656v1 Announce Type: new Abstract: The statistical essence of the Transformer architecture has long remained elusive: Is it a universal approximator, or a neural network version of known
arXiv:2604.13252v1 Announce Type: new Abstract: Anomaly detection aims to identify observations that deviate from expected behavior. Because anomalous events are inherently sparse, most frameworks are
arXiv:2604.13455v1 Announce Type: new Abstract: Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluc