Knowledge catalogue
arXiv:2610.13476v1 Announce Type: new Abstract: Representational similarity measures (RSA, CKA, Procrustes) are chosen for the transformations they treat as irrelevant, and all of them take the set of
arXiv:2610.13653v1 Announce Type: cross Abstract: Reinforcement learning with verifiable rewards (RLVR) has substantially improved LLM reasoning, yet growing evidence suggests it mainly performs distr
arXiv:2610.12718v1 Announce Type: cross Abstract: Large language models are stochastic by nature. Repeated executions under the same experimental conditions may produce different responses. We propose
arXiv:2602.01149v2 Announce Type: replace-cross Abstract: Machine learning (ML) has emerged as a powerful approach for accelerating the discovery of thermoelectric (TE) materials, yet many models suff
arXiv:2610.13603v1 Announce Type: new Abstract: Psychology has proposed many principles of cognition. Each, however, was developed in isolation, leaving their combined explanatory power unknown. We as
arXiv:2610.13407v1 Announce Type: new Abstract: Exhaustive optimistic scoring can make action selection costly in linear bandits with a large fixed set of K actions, requiring O(K) score evaluations p
arXiv:2610.12701v1 Announce Type: new Abstract: Perspectivist NLP has established that annotator positionality shapes labels for subjective tasks, but the field still lacks methods for operationalisin
arXiv:2610.13799v1 Announce Type: cross Abstract: Variance reduction improves the convergence rate of stochastic non-convex optimization under the average smoothness condition. Existing adaptive varia
arXiv:2610.12972v1 Announce Type: new Abstract: Humanoid robots performing lifting and material-handling tasks must maintain balance despite unknown and varying payloads. However, payload uncertainty
arXiv:2610.12917v1 Announce Type: new Abstract: AI shopping assistants increasingly influence which products consumers consider. We study retail assortment planning under AI mediation, where a retaile
arXiv:2610.13928v1 Announce Type: new Abstract: Capability evaluations aim to surface dangerous model behaviors before deployment, but their reliability depends on the assumption that hidden capabilit
arXiv:2610.12679v1 Announce Type: new Abstract: Efficient sampling from Bayesian posterior distributions in high dimensions remains a hard problem, despite advances in theory and algorithms. Multiple
arXiv:2510.00734v2 Announce Type: replace-cross Abstract: Bayesian optimal experimental design provides a principled framework for selecting experimental configurations that maximize obtained informat
arXiv:2610.13518v1 Announce Type: cross Abstract: In machine learning, a self-attention dynamics describes the attention mechanism of transformers, and is obtained treating the layer index of a transf
arXiv:2610.12545v1 Announce Type: cross Abstract: Autoregressive decoding is less suited to non-causally ordered data, e.g., cross-references or future dependencies, since their outputs cannot be revi
arXiv:2501.18560v2 Announce Type: replace Abstract: We consider bandits with anytime knapsacks (BwAK), a novel version of the BwK problem where there is an extit{anytime} cost constraint instead of a
arXiv:2610.12997v1 Announce Type: new Abstract: Prior-art retrieval systems are structurally organized around classification codes, making cross-code discovery difficult. This paper asks if an unsuper
arXiv:2610.13121v1 Announce Type: new Abstract: Cyberbullying is a growing concern in online communities, yet existing Bengali corpora remain limited in contextual information, annotation transparency
arXiv:2602.01667v2 Announce Type: replace Abstract: Prediction-set size offers a coarse summary of conformal uncertainty. We develop a richer perspective through the geometry of credal sets: families
arXiv:2610.12919v1 Announce Type: cross Abstract: We consider online binary prediction with paid stochastic features, where the learner can spend resources to obtain better observations of the underly
arXiv:2610.12977v1 Announce Type: new Abstract: Generative recommendation (GR) of social media platforms commonly retrieves items by autoregressively decoding discrete Semantic IDs (SIDs) with finite-
arXiv:2505.22184v3 Announce Type: replace-cross Abstract: Social media platforms have experienced a significant rise in toxic content, including abusive language and discriminatory remarks, presenting
arXiv:2603.00530v2 Announce Type: replace Abstract: Sampling from unnormalized densities using diffusion models has emerged as a powerful paradigm. However, while recent approaches that use least-squa
arXiv:2610.12530v1 Announce Type: new Abstract: Training-free feature reuse accelerates diffusion transformer inference by replacing full denoising computation at selected steps with cached approximat
arXiv:2610.13693v1 Announce Type: cross Abstract: Neural networks (NNs), while highly flexible and versatile, are often poorly calibrated. Post-hoc calibration methods adjust the outputs of an already
arXiv:2610.12644v1 Announce Type: cross Abstract: Backdoor Attacks on Large Language Models (LLMs) typically follow a trigger-response pattern, where satisfying a trigger condition activates a single
arXiv:2610.13520v1 Announce Type: new Abstract: Identifying causal effects from observational data typically requires a fully specified causal graph, an assumption that is often unrealistic in complex
arXiv:2610.12520v1 Announce Type: cross Abstract: The goal of circuit discovery is to decompose a monolithic neural network into tiny modular subnetworks which are interpretable in the sense that each
arXiv:2610.12580v1 Announce Type: new Abstract: Event ontology is the foundational framework for defining events in a dataset. Event type induction generally involves domain experts who define guideli
arXiv:2610.13433v1 Announce Type: cross Abstract: This paper addresses the grid quantization error of deep-learning (DL)-based direction-of-arrival (DOA) estimation. Classification- based DOA methods
arXiv:2610.12837v1 Announce Type: cross Abstract: Knowledge distillation aims to train a smaller student model to mimic a larger teacher model for resource-constrained environments. However, the proce
arXiv:2610.13931v1 Announce Type: cross Abstract: The expressivity of a neural network Phi describes the class of functions that a network can express. The term topological expressivity refers to a fa
arXiv:2610.12570v1 Announce Type: new Abstract: Speculative decoding accelerates large language model inference by using a lightweight drafter to propose multiple tokens that are subsequently verified
arXiv:2610.12915v1 Announce Type: new Abstract: Minimax optimization is fundamental to adversarial learning yet suffers from severe stability challenges, often manifesting as persistent limit cycles o
arXiv:2610.13665v1 Announce Type: new Abstract: This paper presents a dual-quadrotor hook- suspended transport system for cooperative aerial manipulation of a payload that is not rigidly attached to a
arXiv:2610.12776v1 Announce Type: new Abstract: Automatic differentiation (AD) is a cornerstone of modern machine learning. Since all AD frameworks implicitly fix a basis of Euclidean space to define
arXiv:2610.12775v1 Announce Type: cross Abstract: The two main traditional player statistics used in soccer are goals scored and assists, with an assist credited when a player's pass immediately leads
arXiv:2610.12694v1 Announce Type: new Abstract: Molecular machine learning evaluates out-of-distribution performance with structure disjoint splits, a construction rule fixed once at the scale its lib
arXiv:2412.13844v2 Announce Type: replace-cross Abstract: Retrieval models for recommendation need to account for outcomes such as watch time while searching large item corpora efficiently. We present
arXiv:2610.12960v1 Announce Type: new Abstract: Matching repair after utility changes can reuse valid observations, but new evaluations must respect matching capacity and may return out of order. We s
arXiv:2410.06786v2 Announce Type: replace Abstract: In this study, we present a novel Survival Analysis algorithm designed to efficiently handle large-scale longitudinal data. Our approach draws inspi
arXiv:2610.13406v1 Announce Type: cross Abstract: Forecasting desertification has received far less attention than mapping it. We use a convolutional LSTM (ConvLSTM) trained on ten annual Earth Observ
arXiv:2610.13917v1 Announce Type: new Abstract: A central challenge in de novo protein design is generating proteins with desired structural and functional properties. Existing design models typically
arXiv:2411.15533v2 Announce Type: replace Abstract: Electromyography (EMG) is a measure of muscular electrical activity and is used in many clinical/biomedical disciplines and modern human computer in
arXiv:2502.08598v3 Announce Type: replace Abstract: Diffusion models have been widely adopted as a powerful generative framework across numerous scientific domains, yet their inherently iterative samp
arXiv:2610.13808v1 Announce Type: cross Abstract: Variational quantum circuits (VQCs) are highly sensitive to circuit architecture, yet most quantum machine learning pipelines select a single best des
arXiv:2610.13287v1 Announce Type: new Abstract: Prompt optimizers rewrite a prompt to raise one target metric, but prompts also state requirements the metric does not score, such as an output format,
arXiv:2610.13073v1 Announce Type: new Abstract: Processing a long sequence does not guarantee that distant information remains usable. As context grows, a fixed-size memory must retain many associatio
arXiv:2610.13918v1 Announce Type: cross Abstract: Clinical sleep medicine repeatedly documents profound divergences between objective sleep duration and subjective daytime impairment, notably exemplif
arXiv:2610.13599v1 Announce Type: new Abstract: Diagnostic information in multi-view medical imaging studies, like echocardiography, is distributed across views, each capturing different aspects of ca
arXiv:2610.13649v1 Announce Type: cross Abstract: Machine learning methods are increasingly used to predict chemical properties directly from the molecular structure to bypass the often prohibitive co
arXiv:2610.12507v1 Announce Type: cross Abstract: Sensing-aided beam prediction can reduce the beam-training overhead of millimeter-wave (mmWave) systems by leveraging environmental information from c
arXiv:2610.12542v1 Announce Type: new Abstract: Monte Carlo Tree Search (MCTS) is often proposed for scheduling directed-acyclic-graph (DAG) workflows, yet most studies never run the comparison that c
arXiv:2610.13807v1 Announce Type: new Abstract: Data augmentation is standard practice for low-resource automatic speech recognition (ASR), but its benefits are almost always reported as aggregate err
arXiv:2610.13049v1 Announce Type: new Abstract: Multi-robot coordination is essential in applications such as coverage, target tracking, and information gathering, where robots must jointly select act
arXiv:2610.12493v1 Announce Type: new Abstract: Explainable Artificial Intelligence (XAI) tools have often been presented as methods to increase trust in machine learning models by making aspects of t
arXiv:2610.12706v1 Announce Type: new Abstract: Concerns about falling and potential resulting injuries prevent many older adults from participating in forms of health-improving exercise such as walki
arXiv:2610.12841v1 Announce Type: new Abstract: Emotion classifiers are routinely applied beyond their training data, on the assumption that same-named labels such as fear or surprise mean the same th
arXiv:2610.13851v1 Announce Type: cross Abstract: We establish finite-sample guarantees for conditional diffusion models learned from data and implemented with finitely many denoising steps. Our appro
arXiv:2610.12987v1 Announce Type: new Abstract: If a second text corpus were collected from the same population, how much would a predictor's outputs change? We study this question for masked-text rec