Skip connections [He et al., 2016] have become an ubiquitous feature of neural network architectures from facilitating the stable training of deep models. However, despite their success, prior works [Veit et al., 2016, Gromov et al., 2024, Zhang et al., 2024] have raised the concern that the benefits of skip connections, namely ease of training, may be obscuring deeper issues, in terms of representation learning, that skip connections induce. The main point behind these criticisms is that skip connections appear to bias models away from properly utilising the full depth of their architectures. For instance, Ji et al. [2025a] argues that since skip connections continually reintroduce earlier features into deeper layers, they disrupt the learning of hierarchical and progressively more abstract representations, fundamentally harming representation learning. Motivated by this line of reasoning, we explore designing Transformers that are able to be trained stably without skip connections. Previous works [He et al., 2023, Ji et al., 2025a] have tackled this through modifications to Softmax Self-Attention (SSA) [Vaswani et al., 2017] and weight initialisations to improve signal propagation and the conditioning of the Jacobian matrix. However, these works restrict themselves to standard Softmax-based Transformers which appear to be inherently unstable without skip connections [Dong et al., 2021, Ji et al., 2025b] due to SSA.

Obstructive sleep apnoea (OSA) is a prevalent condition with significant health consequences, yet many patients remain undiagnosed due to the complexity and cost of over-night polysomnography. Acoustic-based screening provides a scalable alternative, yet performance is limited by environmental noise and the lack of physiological context. Respiratory effort is a key signal used in clinical scoring of OSA events, but current approaches require additional contact sensors that reduce scalability and patient comfort. This paper presents the first study to estimate respiratory effort directly from nocturnal audio, enabling physiological context to be recovered from sound alone. We propose a latent-space fusion framework that integrates the estimated effort embeddings with acoustic features for OSA detection. Using a dataset of 157 nights from 103 participants recorded in home environments, our respiratory effort estimator achieves a concordance correlation coefficient of 0.48, capturing meaningful respiratory dynamics. Fusing effort and audio improves sensitivity and AUC over audio-only baselines, especially at low apnoea-hypopnoea index thresholds. The proposed approach requires only smartphone audio at test time, which enables sensor-free, scalable, and longitudinal OSA monitoring.

Herein we provide the proof of Theorem 1 in the main text. Proof A.2 We can construct the Householder matrix with vector u = Q is the product of n 1 orthogonal Householder matrices. Proof A.5 With Lemma A.3, we can upper triangularize the given real orthogonal matrix A as: H We train the models with two common settings: "1 The AdamW optimizer is used with learning rate of 0.0001, weight decay of 0.05 and batch-size of 16. We apply Orthogonal Transformer pretrained on ImageNet-1K as the backbone network. I and Fig.II show the detailed architectures of the convolutional patch embedding and the The last convolution is with the kernel-size of 1 1, following by a LayerNorm layer.

Mitigating the detrimental effects of noisy labels on the training process has become increasingly critical, as obtaining entirely clean or human-annotated samples for large-scale pre-training tasks is often impractical. Nonetheless, existing noise mitigation methods often encounter limitations in practical applications due to their task-specific design, model dependency, and significant computational overhead. In this work, we exploit the properties of high-dimensional orthogonality to identify a robust and effective boundary in cone space for separating clean and noisy samples. Building on this, we propose One-step Anti-Noise (OSA), a model-agnostic noisy label mitigation paradigm that employs an estimator model and a scoring function to assess the noise level of input pairs through just one-step inference, a cost-efficient process. We empirically demonstrate the superiority of OSA, highlighting its enhanced training robustness, improved task transferability, ease of deployment, and reduced computational costs across various benchmarks, models, and tasks. Our code is released at https://github.com/leolee99/OSA.

This paper presents CoverUp, a novel system that drives the generation of high-coverage Python regression tests via a combination of coverage analysis and large-language models (LLMs). CoverUp iteratively improves coverage, interleaving coverage analysis with dialogs with the LLM to focus its attention on as yet uncovered lines and branches. The resulting test suites significantly improve coverage over the current state of the art: compared to CodaMosa, a hybrid LLM / search-based software testing system, CoverUp substantially improves coverage across the board. On a per-module basis, CoverUp achieves median line coverage of 81% (vs. 62%), branch coverage of 53% (vs. 35%) and line+branch coverage of 78% (vs. 55%). We show that CoverUp's iterative, coverage-guided approach is crucial to its effectiveness, contributing to nearly half of its successes.

This study introduces a novel, rich dataset obtained from home sleep apnea tests using the FDA-approved WatchPAT-300 device, collected from 7,077 participants over 21,412 nights. The dataset comprises three levels of sleep data: raw multi-channel time-series from sensors, annotated sleep events, and computed summary statistics, which include 447 features related to sleep architecture, sleep apnea, and heart rate variability (HRV). We present reference values for Apnea/Hypopnea Index (AHI), sleep efficiency, Wake After Sleep Onset (WASO), and HRV sample entropy, stratified by age and sex. Moreover, we demonstrate that the dataset improves the predictive capability for various health related traits, including body composition, bone density, blood sugar levels and cardiovascular health. These results illustrate the dataset's potential to advance sleep research, personalized healthcare, and machine learning applications in biomedicine.

Obstructive Sleep Apnea Syndrome (OSAS) is the most common sleep-related breathing disorder. It is caused by an increased upper airway resistance during sleep, which determines episodes of partial or complete interruption of airflow. The detection and treatment of OSAS is particularly important in stroke patients, because the presence of severe OSAS is associated with higher mortality, worse neurological deficits, worse functional outcome after rehabilitation, and a higher likelihood of uncontrolled hypertension. The gold standard test for diagnosing OSAS is polysomnography (PSG). Unfortunately, performing a PSG in an electrically hostile environment, like a stroke unit, on neurologically impaired patients is a difficult task; also, the number of strokes per day outnumbers the availability of polysomnographs and dedicated healthcare professionals. Thus, a simple and automated recognition system to identify OSAS among acute stroke patients, relying on routinely recorded vital signs, is desirable. The majority of the work done so far focuses on data recorded in ideal conditions and highly selected patients, and thus it is hardly exploitable in real-life settings, where it would be of actual use. In this paper, we propose a convolutional deep learning architecture able to reduce the temporal resolution of raw waveform data, like physiological signals, extracting key features that can be used for further processing. We exploit models based on such an architecture to detect OSAS events in stroke unit recordings obtained from the monitoring of unselected patients. Unlike existing approaches, annotations are performed at one-second granularity, allowing physicians to better interpret the model outcome. Results are considered to be satisfactory by the domain experts. Moreover, based on a widely-used benchmark, we show that the proposed approach outperforms current state-of-the-art solutions.

Training agents in cooperative settings offers the promise of AI agents able to interact effectively with humans (and other agents) in the real world. Multi-agent reinforcement learning (MARL) has the potential to achieve this goal, demonstrating success in a series of challenging problems. However, whilst these advances are significant, the vast majority of focus has been on the self-play paradigm. This often results in a coordination problem, caused by agents learning to make use of arbitrary conventions when playing with themselves. This means that even the strongest self-play agents may have very low cross-play with other agents, including other initializations of the same algorithm. In this paper we propose to solve this problem by adapting agent strategies on the fly, using a posterior belief over the other agents' strategy. Concretely, we consider the problem of selecting a strategy from a finite set of previously trained agents, to play with an unknown partner. We propose an extension of the classic statistical technique, Gibbs sampling, to update beliefs about other agents and obtain close to optimal ad-hoc performance. Despite its simplicity, our method is able to achieve strong cross-play with unseen partners in the challenging card game of Hanabi, achieving successful ad-hoc coordination without knowledge of the partner's strategy a priori.

Following the announcement of the first Networks Advanced Naval Technology Exercise (NetANTX) Prize Challenge, Naval Information Warfare Systems Command (NAVWAR) launched a second prize challenge in support of Project Overmatch, this one seeking artificial intelligence (AI) solutions. Project Overmatch is a high priority Department of the Navy initiative to deliver a more lethal, better-connected fleet of the future by connecting manned and unmanned platforms, weapons and sensors together in a robust Naval Operational Architecture that integrates with Joint All-Domain Command and Control for enhanced Distributed Maritime Operations. Critical to Project Overmatch is the development of networks, infrastructure, data architecture, tools and analytics that support the operational and developmental environment that will enable sustained maritime dominance for years to come. To deliver this modernized network, the AINetANTX challenge aims to identify and leverage the latest in AI-enabled technologies to allow warfighters to make critical decisions quickly in operationally relevant maritime environments. The challenge is offering $100,000 in total cash prizes for the best solution presented, with the first place entry winning $75,000, and second place winning $25,000.