Lower Bounds for Linear-Oracle Online Learning
arXiv:2609.38375v1 Announce Type: cross Abstract: Can a constant number of linear minimizations per round improve on the T^{3/4} regret rate of online Frank-Wolfe on general convex sets? Weibel et al.
Knowledge catalogue
arXiv:2609.38375v1 Announce Type: cross Abstract: Can a constant number of linear minimizations per round improve on the T^{3/4} regret rate of online Frank-Wolfe on general convex sets? Weibel et al.
arXiv:2609.39583v1 Announce Type: cross Abstract: We present the results of extbf{MADGRAV}, a deep-learning-based search for high-mass compact binary coalescences, applied to the data collected by the
arXiv:2609.39870v1 Announce Type: new Abstract: World-action models (WAMs) augment robot policies with action-conditioned environment dynamics, yet existing approaches largely rely on future observati
arXiv:2609.40043v1 Announce Type: cross Abstract: Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by in
arXiv:2609.39950v1 Announce Type: new Abstract: Continuum robots are well suited for gentle manipulation because of their inherent compliance and ability to adapt to complex environments. However, the
arXiv:2601.23151v2 Announce Type: replace Abstract: Generative models have enjoyed widespread success in a variety of applications. However, they encounter inherent mathematical limitations in modelin
arXiv:2609.38627v1 Announce Type: new Abstract: Yoruba is a tonal language in which contour tones pose persistent orthographic challenges. These are especially notable for personal names and lexical i
arXiv:2609.39888v1 Announce Type: new Abstract: Neural trajectory predictors can reach low prediction error while violating dynamics, actuator limits, or state constraints, especially when controls ar
arXiv:2609.38278v1 Announce Type: new Abstract: Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The auto
arXiv:2602.03537v2 Announce Type: replace Abstract: Matryoshka Quantization (MatQuant), Any-Precision-LLM (AP) and AnyBCQ (AB) are recent quantization approaches showing that a single integer-quantize
arXiv:2609.40087v1 Announce Type: cross Abstract: Flow-matching approaches to voice conversion (VC) have gained attention owing to their high speech quality and strong speaker similarity. Among them,
arXiv:2609.39273v1 Announce Type: new Abstract: This report presents extbf{MegaAvatar}, a controllable talking avatar generation framework built on top of the Wan2.2-TI2V-5B model. Compared with previ
arXiv:2609.38609v1 Announce Type: cross Abstract: This note is a technical companion to a previously published preprint describing an Attention Residual U-Net that postprocesses deterministic forecast
arXiv:2609.39212v1 Announce Type: cross Abstract: We study additive regression under a potentially non-product random design on [0,1]^d, allowing the dimension d to grow with the sample size n. We int
arXiv:2609.38979v1 Announce Type: new Abstract: Multiple object hallucination, where large vision-language models (LVLMs) generate objects not supported by the visual input, is a persistent challenge
arXiv:2609.38246v1 Announce Type: cross Abstract: Deepfakes no longer need to fake a whole video. Generators that read the transcript now alter only the few seconds in which a video's meaning turns, s
arXiv:2609.38487v1 Announce Type: new Abstract: Judging image quality is not only ecologically relevant to everyday human tasks, but also underpins many machine vision tasks such as image generation.
arXiv:2609.39456v1 Announce Type: cross Abstract: This work proposes a mutual feedback architecture, MEQ, that refines the two inputs, of possibly different modalities, into a pair of coupled embeddin
arXiv:2609.38587v1 Announce Type: new Abstract: Electroencephalography (EEG) is recorded continuously over hours, with relevant dynamics spanning timescales from milliseconds to hours. Most EEG founda
arXiv:2609.38261v1 Announce Type: cross Abstract: In this paper we propose NinaXander, a series of composed language models obtained by connecting layers of frozen language models from different archi
arXiv:2609.39017v1 Announce Type: new Abstract: Reliable dexterous manipulation requires continuous estimation of object geometry and hand-object contact throughout interaction. With egocentric sensin
arXiv:2606.20487v2 Announce Type: replace Abstract: Computer use agents are expanding from single-device operation toward cross-device systems that coordinate tasks across heterogeneous environments.
arXiv:2609.39351v1 Announce Type: new Abstract: We study preference-based bandits with general reward function classes, where a learner sequentially selects pairs of arms and observes binary preferenc
arXiv:2603.14474v2 Announce Type: replace Abstract: Sketch techniques have been extensively studied in recent years and are especially well-suited to data streaming scenarios, where the sketch summary
arXiv:2609.40121v1 Announce Type: cross Abstract: While Abstract Meaning Representation (AMR) has historically improved performance on a range of NLP tasks, the benefit---or lack thereof---of AMR augm
arXiv:2508.10148v2 Announce Type: replace-cross Abstract: Accurate and explainable out-of-distribution (OOD) detection is required to use machine learning systems safely. Previous work has shown that
arXiv:2604.02543v2 Announce Type: replace Abstract: As vision-language models (VLMs) are increasingly deployed in clinical decision support, more than accuracy is required: knowing when to trust their
arXiv:2609.39975v1 Announce Type: cross Abstract: This paper presents an overview of the fourteenth edition of the BioASQ challenge, organized in the context of the Conference and Labs of the Evaluati
arXiv:2609.39626v1 Announce Type: cross Abstract: Ensemble smoothers are the most successful and efficient techniques currently available for history matching. However, because these methods rely on G
arXiv:2609.39329v1 Announce Type: new Abstract: Long-context inference with Large Language Models (LLMs) is bottlenecked by the linearly growing memory of the key-value (KV) cache. Existing compressio
arXiv:2609.38715v1 Announce Type: new Abstract: This paper proposes a control strategy for path following that is model-free and relies solely on deceleration for skid-steering planetary rovers naviga
arXiv:2609.39911v1 Announce Type: new Abstract: Patient preference, defined as a patient's demonstrated willingness and capacity to adhere to clinical recommendations, is a primary determinant of ther
arXiv:2609.39630v1 Announce Type: new Abstract: Pretrained tabular generators can reproduce training records even when aggregate utility remains high. When retraining is unavailable or too costly, sam
arXiv:2609.39130v1 Announce Type: new Abstract: Accurate assessment of color differences is essential for applications ranging from digital design to quality control. While existing color difference m
arXiv:2609.40118v1 Announce Type: new Abstract: As LLM capabilities advance, agents are tackling increasingly complex tasks over longer horizons. Their growing interaction histories make memory compac
arXiv:2609.39024v1 Announce Type: new Abstract: Text-to-image (T2I) generation is gaining increasing popularity with the general public, motivating the development of reliable mechanisms for copyright
arXiv:2609.38490v1 Announce Type: cross Abstract: Large language model (LLM) agents that reason over clinical records must track changes in a patient's state while preserving the history needed to und
arXiv:2609.38377v1 Announce Type: new Abstract: Evaluating the physical consistency of generated videos remains a fundamental challenge. Existing approaches rely on off-the-shelf vision-language model
arXiv:2609.40008v1 Announce Type: cross Abstract: Extracting the pion electromagnetic form factor F_{pi}(s) through phenomenological curve-fitting models introduces model dependence, unphysical artefa
arXiv:2609.38597v1 Announce Type: new Abstract: Unified Multimodal Models (UMMs) often rely on separate visual representations for understanding and generation, increasing visual context length and co
arXiv:2609.39123v1 Announce Type: new Abstract: When optimizing an expensive black-box function sequentially, as in hyperparameter optimization, we may want to stop once the best evaluated value is ce
arXiv:2609.38890v1 Announce Type: new Abstract: Outcome-based reinforcement learning (RL) post-trains vision--language--action policies using terminal success signals, but assigns the same trajectory-
arXiv:2609.39440v1 Announce Type: cross Abstract: We compare the instance-wise, finite-sample risks of monotone spectral filters for linear regression, a broad class of estimators including principal
arXiv:2609.38289v1 Announce Type: cross Abstract: In personalized AI applications, such as conversational assistants and recommender systems, users interact not with models in isolation but with broad
arXiv:2609.39798v1 Announce Type: cross Abstract: Building on a probabilistic perspective in which adversarial examples arise from the overlap between a distance-based distribution p_{dis} and a victi
arXiv:2609.38593v1 Announce Type: cross Abstract: Skills are external artifacts that Large Language Models (LLMs) consume at inference time to improve their performance on specialized domains by incor
arXiv:2512.00763v2 Announce Type: replace-cross Abstract: Adaptive and non-Euclidean optimizers often outperform Euclidean methods such as stochastic gradient descent (SGD) in language modeling by a l
arXiv:2609.38672v1 Announce Type: cross Abstract: Beam-search-based test-time methods provide an effective way to improve large language model (LLM) performance on long-horizon generation by pruning i
arXiv:2609.40185v1 Announce Type: new Abstract: Constrained generation aims to sample from language models (LMs) conditioned on hard constraints. Existing constrained-generation techniques for nondete
arXiv:2609.39075v1 Announce Type: cross Abstract: Retrieval-augmented generation (RAG) systems need inexpensive ways to route generated answers: accept low-risk outputs, review uncertain ones, and res
arXiv:2609.40361v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) are rapidly advancing clinical diagnosis, yet their adaptation pipelines remain anchored to accuracy-based ob
arXiv:2609.39801v1 Announce Type: new Abstract: Post-training quantization (PTQ) has become a widely adopted technique for reducing the memory footprint and inference cost of large language models (LL
arXiv:2609.38265v1 Announce Type: cross Abstract: Onboard AI is gaining interest for space applications such as vessel, wildfire, and cloud detection, where real-time processing can improve mission re
arXiv:2609.39445v1 Announce Type: new Abstract: Time series foundation models (TSFMs) commonly adapt to new data by attaching a single trainable head to a frozen backbone, a one-size-fits-all setup th
arXiv:2609.38448v1 Announce Type: new Abstract: Open-ended LLM homogeneity can create false plurality when several systems appear to offer independent perspectives while returning the same familiar de
arXiv:2602.23321v2 Announce Type: replace-cross Abstract: Using advanced machine learning techniques, we developed a method to reconstruct the arrival direction and energy of ultra-high-energy cosmic
arXiv:2609.39168v1 Announce Type: new Abstract: Reinforcement Learning with Verifiable Rewards (RLVR) has improved the reasoning capabilities of Multimodal Large Language Models (MLLMs), yet existing
arXiv:2609.39934v1 Announce Type: cross Abstract: Checkpoint selection in domain generalization often relies on source-validation accuracy, yet the selected checkpoint need not provide reliable probab
arXiv:2609.40089v1 Announce Type: new Abstract: Continued pretraining enables language models to adapt to new domains and knowledge, but often at the cost of forgetting previously acquired capabilitie
arXiv:2609.38707v1 Announce Type: new Abstract: We develop a task-relative framework for certifying residual wrench authority after hover and contact loading in multirotors with bounded actuators. Usi