Knowledge catalogue
arXiv:2605.11011v1 Announce Type: new Abstract: Looped computation shows promise in improving the reasoning-oriented performance of LLMs by scaling test-time compute. However, existing approaches typi
arXiv:2605.12171v1 Announce Type: new Abstract: This note shows that no self-attention layer post-processed by a rational function can sign-represent the parity function unless the product of the numb
arXiv:2605.12165v1 Announce Type: cross Abstract: In light of the recent advancements in machine learning, we propose a novel approach to neutron source distribution estimation through the utilisation
arXiv:2605.11034v1 Announce Type: cross Abstract: We present MambaNetBurst, a compact tokenizer-free byte-level sequence classifier for network burst classification based on a Mamba-2 backbone. In con
arXiv:2605.12338v1 Announce Type: new Abstract: Sampling from constrained distributions has a wide range of applications, including in Bayesian optimization and robotics. Prior work establishes conver
arXiv:2605.11825v1 Announce Type: new Abstract: Affective touch in human-robot interaction is shaped not only by emotional intent, but also by robot embodiment, including touch location, physical cons
arXiv:2605.11846v1 Announce Type: new Abstract: Self-supervised learning (SSL) is often deployed under changing information, such as shorter histories, missing features, or partially observed images.
arXiv:2605.12368v1 Announce Type: new Abstract: Solving partial differential equations (PDEs) with machine learning typically requires training a new neural network for every new equation. This optimi
arXiv:2602.09587v2 Announce Type: replace Abstract: The scarcity of high-quality data remains a primary bottleneck in adapting multimodal generative models for medical image editing. Existing medical
mimalloc is an open-source, modern, scalable memory allocator that is a drop-in replacement for malloc and free. It is relatively small (~12K lines), with clear internal data structures, and is easy t
arXiv:2605.11841v1 Announce Type: cross Abstract: Tree ensembles such as random forests (RFs) and gradient boosting machines (GBMs) are among the most widely used supervised learners, yet their theore
arXiv:2605.12262v1 Announce Type: cross Abstract: We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of
arXiv:2605.11617v1 Announce Type: new Abstract: Streaming decision trees are natural candidates for open-world continual learning, as they perform local updates, enjoy bounded memory, and static decis
arXiv:2605.11900v1 Announce Type: new Abstract: Unmanned aerial vehicles (UAVs) can provide flexible traffic surveillance where fixed roadside cameras are unavailable, costly, or impractical. However,
arXiv:2605.05945v2 Announce Type: replace-cross Abstract: The recent advancement of Vision Language Action (VLA) models has driven a critical demand for large scale egocentric datasets. However, exist
arXiv:2502.12276v2 Announce Type: replace Abstract: Computational methods for analyzing prose and poetry utilize word embeddings and other abstract representations that sometimes obscure context-rich
arXiv:2602.15451v3 Announce Type: replace-cross Abstract: Deep generative modeling to stochastically design small molecules is an emerging technology for accelerating drug discovery and development. H
arXiv:2411.19093v4 Announce Type: replace Abstract: Access to drinking water and sanitation services is essential for health and well-being, yet large global disparities persist. Sustainable Developme
more demos on Interaction Models collaboratively doing system design, reading papers, fact-checking with live generative UI 1. (System design) - The Interaction Models see your screen and collaborates
arXiv:2605.11336v1 Announce Type: cross Abstract: Web search queries concern place far more often than existing labelling schemes suggest, yet the landscape of geospatial web search queries - what peo
arXiv:2605.11835v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) promise low-power event-driven computation for temporally rich tasks, but commonly used neuron models often trade off g
arXiv:2603.27358v2 Announce Type: replace Abstract: Despite a long tradition of work on extractive summarization, which by nature aims to recover the most important propositions in a text, little work
arXiv:2605.11978v1 Announce Type: new Abstract: The performance of Large Language Models (LLMs) on downstream tasks is fundamentally constrained by the capabilities acquired during pre-training. Howev
arXiv:2605.11471v1 Announce Type: new Abstract: Matrix product operator Born machines (MPO-BMs) are tractable tensor-network models for probabilistic modeling, but their efficient approximation capabi
arXiv:2605.11755v1 Announce Type: cross Abstract: Diffusion models and flow-based methods have shown impressive generative capability, especially for images, but their sampling is expensive because it
arXiv:2605.11199v1 Announce Type: cross Abstract: Trained lattice samplers are usually judged by the ensembles they generate. Here we instead analyze the trained field-space function itself: a flow-ma
arXiv:2605.11291v1 Announce Type: new Abstract: In this paper, we provide a computable characterization of the geometry of optimal representations in Contrastive Learning (CL) when the classes are imb
arXiv:2605.11172v1 Announce Type: new Abstract: We introduce SODA, a generalization of Optimistic Dual Averaging, which provides a common perspective on state-of-the-art optimizers like Muon, Lion, Ad
arXiv:2605.12419v1 Announce Type: new Abstract: Despite the rapid advancements in large language model (LLM) development, fine-tuning them for specific tasks often results in the catastrophic forgetti
arXiv:2602.12139v2 Announce Type: replace Abstract: Transformers excel at time series modelling through attention mechanisms that capture long-term temporal patterns. However, they assume uniform time
arXiv:2605.11803v1 Announce Type: new Abstract: As Video Large Language Models (Video-LLMs) scale to longer and more complex videos, their inference cost grows rapidly due to the large volume of visua
arXiv:2605.11570v1 Announce Type: new Abstract: Activation functions are what make deep networks expressive: without them, the model collapses to a linear map. Yet we still evaluate training mostly fr
arXiv:2506.23619v2 Announce Type: replace-cross Abstract: This paper investigates the impact of posterior drift on out-of-sample forecasting accuracy in overparametrized machine learning models. We do
arXiv:2605.12072v1 Announce Type: new Abstract: Dropout-based sparse-view 3D Gaussian Splatting (3DGS) methods alleviate overfitting by randomly suppressing Gaussian primitives during training. Existi
This paper investigates token superposition, a phenomenon where language models can encode multiple token representations simultaneously in a single position, enabling more efficient use of model capa
arXiv:2602.01418v2 Announce Type: replace Abstract: We propose Parabolic Position Encoding (PaPE), a parabola-based position encoding for vision modalities in attention-based architectures. Given a se
arXiv:2605.11684v1 Announce Type: new Abstract: We propose a Byzantine-resilient federated conformal prediction (FCP) method that leverages partial model sharing, where only a subset of model paramete
arXiv:2602.04042v2 Announce Type: replace Abstract: We propose Partition Tree, a novel tree-based framework for conditional density estimation over general outcome spaces that supports both continuous
arXiv:2510.02107v4 Announce Type: replace Abstract: AdaBoost sequentially fits so-called weak learners to minimize an exponential loss, which penalizes misclassified data points more severely than oth
arXiv:2602.13690v2 Announce Type: replace Abstract: Magnetic-anomaly navigation, leveraging small-scale variations in the Earth's magnetic field, is a promising alternative when GPS is unavailable or
arXiv:2605.12492v1 Announce Type: new Abstract: We introduce Pion, a spectrum-preserving optimizer for large language model (LLM) training based on orthogonal equivalence transformation. Unlike additi
arXiv:2212.02011v3 Announce Type: replace Abstract: Point cloud learning is receiving increasing attention. However, most existing point cloud models lack the practical ability to deal with the unavoi
arXiv:2605.12422v1 Announce Type: new Abstract: Automatic generation of educational materials using large language models (LLMs) is becoming increasingly common, but assigning difficulty levels to suc
arXiv:2605.12241v1 Announce Type: cross Abstract: Specialized foundation models are beginning to emerge in various medical subdomains, but pretraining methodologies and parametric scaling with the siz
arXiv:2605.10984v1 Announce Type: new Abstract: Uncertainty quantification complements model predictions by characterizing their reliability, which is essential for high-stakes decision making such as
arXiv:2605.11506v1 Announce Type: new Abstract: Score-based diffusion models achieve state-of-the-art performance for inverse problems, but their practical deployment is hindered by long inference tim
arXiv:2601.13780v3 Announce Type: replace Abstract: Graph diffusion models achieve state-of-the-art performance in graph generation but suffer from quadratic complexity in the number of nodes -- and m
arXiv:2502.03061v2 Announce Type: replace Abstract: We introduce the problem of best arm identification (BAI) with post-action context, a new BAI problem in a stochastic multi-armed bandit environment
arXiv:2605.11314v1 Announce Type: new Abstract: Cerebral Palsy (CP) is a neurological disorder of movement and the most common cause of lifelong physical disability in childhood. Approximately 75% of
arXiv:2605.11045v1 Announce Type: cross Abstract: This paper presents the ReXCL tool, which automates the extraction and classification processes in requirements engineering, enhancing the software de
arXiv:2605.11290v1 Announce Type: new Abstract: Capability distillation applies knowledge distillation to selected model capabilities, aiming to compress a large language model (LLM) into a smaller on
arXiv:2605.12347v1 Announce Type: new Abstract: Stable, low-latency whole-body teleoperation of humanoid robots is an open research challenge, complicated by kinematic mismatches between human and rob
arXiv:2605.11927v1 Announce Type: new Abstract: While modern diffusion models excel at generating diverse single images, extending this to sequential generation reveals a fundamental challenge: balanc
arXiv:2602.02408v4 Announce Type: replace Abstract: Model editing aims to correct errors in large, pretrained models without altering unrelated behaviors. While some recent works have edited vision-la
arXiv:2508.08420v3 Announce Type: replace Abstract: We consider the problem of online regret minimization in linear bandits with access to prior observations (offline data) from the underlying bandit
arXiv:2508.21260v2 Announce Type: replace Abstract: Many estimation problems in aerospace navigation and robotics involve measurements that depend on prior states. A prominent example is odometry, whi
arXiv:2605.11242v1 Announce Type: new Abstract: In this paper, we present the RETUYT-INCO participation at the BEA 2026 shared task 'Rubric-based Short Answer Scoring for German'. Our team participate
arXiv:2605.11818v1 Announce Type: new Abstract: Recent diffusion-based approaches have made substantial progress in image layer decomposition. However, accurately decomposing complex natural images re
arXiv:2605.11212v1 Announce Type: new Abstract: Computer-use agents~(CUAs) rely on visual observations of graphical user interfaces, where each screenshot is encoded into a large number of visual toke
arXiv:2510.25609v3 Announce Type: replace Abstract: We propose an alternative to the standard GAN training approach, in which the discriminator is a binary classifier trained by cross-entropy to disti