Negative / Null Result ReportOpen accessMathematics
Ziling Ma, Ángel López Oriona, Hernando Ombao et al. · 2026 · arXiv
We study adaptive pooling under predictive heterogeneity in high-dimensional multivariate time series forecasting, where global models improve statistical efficiency but may fail to capture heterogeneous predictive structure, while naive specialization can induce negative transfer. We formulate adaptive pooling as a statistical decision problem and propose a validation-driven framework that determines when and how specialization should be applied. Rather than grouping series based on representation similarity, we define partitions through out-of-sample predictive performance, thereby aligning
Negative / Null Result ReportOpen accessMathematics
Rong Chen, Simone Giannerini, Greta Goracci et al. · 2025 · arXiv
We develop an estimation methodology for a factor model for high-dimensional matrix-valued time series, where common stochastic trends and common stationary factors can be present. We study, in particular, the estimation of (row and column) loading spaces, of the common stochastic trends and of the common stationary factors, and the row and column ranks thereof. In a set of (negative) preliminary results, we show that a projection-based technique fails to improve the rates of convergence compared to a "flattened" estimation technique which does not take into account the matrix nature of the da
Negative / Null Result ReportOpen accessComputer Science
OFM Riaz Rahman Aranya, Kevin Desai · 2026 · arXiv
Synthetic data, an appealing alternative to extensive expert-annotated data for medical image segmentation, consistently fails to improve segmentation performance despite its visual realism. The reason being that synthetic and real medical images exist in different semantic feature spaces, creating a domain gap that current semi-supervised learning methods cannot bridge. We propose SRA-Seg, a framework explicitly designed to align synthetic and real feature distributions for medical image segmentation. SRA-Seg introduces a similarity-alignment (SA) loss using frozen DINOv2 embeddings to pull s
Negative / Null Result ReportOpen accessComputer Science
Hongsin Lee, Hye Won Chung · 2026 · arXiv
Adversarial Distillation aims to enhance student robustness by guiding the student with a robust teacher's soft labels within the min-max adversarial training framework, yet its success is notoriously inconsistent: a more robust teacher often fails to improve, or even harms, the student's robust generalization. In this paper, we identify a key mechanism of this teacher dependency: the misalignment between the teacher's supervisory confidence and the student's representational limitations on a consistent subset of training data -- the Robustly Unlearnable Set. We present a theoretical framework
Negative / Null Result ReportOpen accessComputer Science
Andrei Liviu Nicolicioiu, Mohammad Pezeshki, Aaron Courville · 2026 · arXiv
On-policy self-distillation achieves strong pass@1 accuracy by using a single model as both teacher and student, with the teacher conditioned on a correct demonstration to provide dense token-level feedback. We show that this could come at a hidden cost: rollout diversity decreases and pass@k curves flatten (i.e., generating more rollouts fails to improve accuracy). We trace this to compounding biases in the design of self-distillation with sampled demonstrations. The teacher scores each student rollout while conditioned on a sampled correct rollout, channeling its feedback through the model's
Negative / Null Result ReportOpen accessEngineering
Kwon Byung-Ki, Oh Hyun-Bin, Kim Jun-Seong et al. · 2023 · arXiv
Video motion magnification amplifies invisible small motions to be perceptible, which provides humans with a spatially dense and holistic understanding of small motions in the scene of interest. This is based on the premise that magnifying small motions enhances the legibility of motions. In the real world, however, vibrating objects often possess convoluted systems that have complex natural frequencies, modes, and directions. Existing motion magnification often fails to improve legibility since the intricate motions still retain complex characteristics even after being magnified, which may di
Negative / Null Result ReportOpen accessComputer Science
Lennart Luettgau, Vanessa Cheung, Magda Dubois et al. · 2025 · arXiv
People increasingly seek personal advice from large language models (LLMs), yet whether humans follow their advice, and its consequences for their well-being, remains unknown. In a longitudinal randomised controlled trial with a representative UK sample (N = 6,474), we found that up to 79% of participants who had a 20-minute discussion with one of three AI chatbots (GPT-4o, LLama-3.3-70B, Gemini 3 Pro) about health, careers or relationships subsequently reported following its advice. Advice-following remained above 60% even for high-stakes recommendations, suggesting that users only weakly cal
Negative / Null Result ReportOpen accessComputer Science
Ruth Cohen, Lu Feng, Ayala Bloch et al. · 2026 · arXiv
While natural-language explanations from large language models (LLMs) are widely adopted to improve transparency and trust, their impact on objective human-AI team performance remains poorly understood. We identify a Persuasion Paradox: fluent explanations systematically increase user confidence and reliance on AI without reliably improving, and in some cases undermining, task accuracy. Across three controlled human-subject studies spanning abstract visual reasoning (RAVEN matrices) and deductive logical reasoning (LSAT problems), we disentangle the effects of AI predictions and explanations u
Negative / Null Result ReportMedicine
Fuentes-Expósito, Frid, Muñoz-Mateu et al. · 2026 · JCO clinical cancer informatics
We evaluated whether offering access to a multicomponent mHealth app improves quality of life (QoL) and psychosocial outcomes among breast cancer survivors under pragmatic, nonprescriptive conditions. In this single-center, randomized,…
View details →DOI: 10.1200/cci-26-00025 Negative / Null Result ReportMedicine
Yang, Zhu, Chen et al. · 2026 · Frontiers in neuroscience
To observe the therapeutic effects of deep transcranial magnetic stimulation (dTMS) and repetitive transcranial magnetic stimulation (rTMS) on upper and lower limb motor dysfunction in patients with basal ganglia infarction, and to…
View details →DOI: 10.3389/fnins.2026.1870537 Negative / Null Result ReportOpen accessComputer Science
Amanpreet Singh, Mike D'Arcy, Arman Cohan et al. · 2022 · arXiv
Learned representations of scientific documents can serve as valuable input features for downstream tasks without further fine-tuning. However, existing benchmarks for evaluating these representations fail to capture the diversity of relevant tasks. In response, we introduce SciRepEval, the first comprehensive benchmark for training and evaluating scientific document representations. It includes 24 challenging and realistic tasks, 8 of which are new, across four formats: classification, regression, ranking and search. We then use this benchmark to study and improve the generalization ability o
Negative / Null Result ReportOpen accessComputer Science
Chunliang Li, Tianze Cao, Sanyuan Zhao · 2026 · arXiv
Visual Autoregressive (VAR) modeling inefficiently applies a fixed computational depth to each position when generating high-resolution images. While existing methods accelerate inference by pruning tokens using frequency maps, their binary hard-pruning approach is fundamentally limited and fails to improve quality even with better frequency estimation. Observing that VAR models possess significant depth redundancy, we propose a paradigm shift from pruning entire tokens to adaptively allocating per-token computational depth. To this end, we introduce DepthVAR, a training-free framework that dy
Negative / Null Result ReportOpen accessComputer Science
Adam Byerly, Daniel Khashabi · 2024 · arXiv
Self-consistency (SC) improves the performance of large language models (LLMs) across various tasks and domains that involve short content. However, does this support its effectiveness for long-context problems? We challenge the assumption that SC's benefits generalize to long-context settings, where LLMs often struggle with position bias, the systematic over-reliance on specific context regions-which hinders their ability to utilize information effectively from all parts of their context. Through comprehensive experimentation with varying state-of-the-art models, tasks, and SC formulations, w
Negative / Null Result ReportMedicine
Du, Zhang, Xu et al. · 2026 · Scientific reports
To evaluate the remediation strategies for newly reclaimed, heavily salinized farmland in Xinjiang province, China, a three-year field experiment was conducted comparing different treatments, such as agricultural improvement measures…
View details →DOI: 10.1038/s41598-026-57986-1 Negative / Null Result ReportOpen accessComputer Science
Aaron B. Wagner, Nirmal V. Shende, Yücel Altuğ · 2019 · arXiv
We introduce a novel mechanism, called timid/bold coding, by which feedback can be used to improve coding performance. For a certain class of DMCs, called compound-dispersion channels, we show that timid/bold coding allows for an improved second-order coding rate compared with coding without feedback. For DMCs that are not compound dispersion, we show that feedback does not improve the second-order coding rate. Thus we completely determine the class of DMCs for which feedback improves the second-order coding rate. An upper bound on the second-order coding rate is provided for compound-dispersi
Negative / Null Result ReportMedicine
Tasgaonkar, Rathi, Agrawal et al. · 2026 · International journal of clinical pediatric dentistry
Preserving primary teeth with crowns is vital in pediatric dentistry, especially for those compromised by dental caries, previous treatments, or malformations. To address the limitations of traditional esthetic crowns, Bioflx crowns have…
View details →DOI: 10.5005/jp-journals-10005-3480 Negative / Null Result ReportOpen accessAgricultural and Biological Sciences
Denis Boyer, Peter D. Walsh · 2010 · arXiv
Thanks to recent technological advances, it is now possible to track with an unprecedented precision and for long periods of time the movement patterns of many living organisms in their habitat. The increasing amount of data available on single trajectories offers the possibility of understanding how animals move and of testing basic movement models. Random walks have long represented the main description for micro-organisms and have also been useful to understand the foraging behaviour of large animals. Nevertheless, most vertebrates, in particular humans and other primates, rely on sophistic
Negative / Null Result ReportMedicine
Lahaye, Haegdorens, Franck et al. · 2026 · European geriatric medicine
To evaluate the effectiveness of a structured, team-delivered intervention based on cognitive behavioural therapy for insomnia (CBT-I) on sleep and related outcomes in nursing home residents. A cluster randomised controlled trial with…
View details →DOI: 10.1007/s41999-026-01519-6 Negative / Null Result ReportOpen accessComputer Science
Fırat Öncel, Matthias Bethge, Beyza Ermis et al. · 2024 · arXiv
In the last decade, the generalization and adaptation abilities of deep learning models were typically evaluated on fixed training and test distributions. Contrary to traditional deep learning, large language models (LLMs) are (i) even more overparameterized, (ii) trained on unlabeled text corpora curated from the Internet with minimal human intervention, and (iii) trained in an online fashion. These stark contrasts prevent researchers from transferring lessons learned on model generalization and adaptation in deep learning contexts to LLMs. To this end, our short paper introduces empirical ob
Negative / Null Result ReportMedicine
Abelovska, Malinova, Sucha et al. · 2026 · Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology
This pilot study evaluated "Your Health Belongs to You," a six-week group intervention combining psychotherapy and exercise to improve quality of life, emotional health, and symptom management in individuals with Multiple Sclerosis (MS),…
View details →DOI: 10.1007/s10072-026-09130-0 Negative / Null Result ReportOpen accessComputer Science
Piyawat Lertvittayakumjorn, David Kinney, Vinodkumar Prabhakaran et al. · 2025 · arXiv
Generative large language models (LLMs) have demonstrated gaps in diverse cultural awareness across the globe. We investigate the effect of retrieval augmented generation and search-grounding techniques on LLMs' ability to display familiarity with various national cultures. Specifically, we compare the performance of standard LLMs, LLMs augmented with retrievals from a bespoke knowledge base (i.e., KB grounding), and LLMs augmented with retrievals from a web search (i.e., search grounding) on multiple cultural awareness benchmarks. We find that search grounding significantly improves the LLM p
Negative / Null Result ReportMedicine
Wang, Li, Zhang et al. · 2026 · Child development
This study examines how center-based parenting interventions aimed at improving early child development in rural China affect the mental health of caregivers. Data from an analytic sample of 615 caregiver-child dyads (children aged 6 to 24…
View details →DOI: 10.1093/chidev/aacag099 Negative / Null Result ReportOpen accessEngineering
Haoyang Li, Yuchen Hu, Chen Chen et al. · 2024 · arXiv
Deep neural network (DNN)-based speech enhancement (SE) usually uses conventional activation functions, which lack the expressiveness to capture complex multiscale structures needed for high-fidelity SE. Group-Rational KAN (GR-KAN), a variant of Kolmogorov-Arnold Networks (KAN), retains KAN's expressiveness while improving scalability on complex tasks. We adapt GR-KAN to existing DNN-based SE by replacing dense layers with GR-KAN layers in the time-frequency (T-F) domain MP-SENet and adapting GR-KAN's activations into the 1D CNN layers in the time-domain Demucs. Results on Voicebank-DEMAND sho
Negative / Null Result ReportMedicine
Partlova, Comley, Haigh et al. · 2026 · Experimental neurology
Spinal muscular atrophy (SMA) is characterized by motor neuron loss and neuromuscular junction (NMJ) pathology. Although SMN-upregulating therapies such as Nusinersen markedly improve survival and motor function for many patients,…
View details →DOI: 10.1016/j.expneurol.2026.115898 Negative / Null Result ReportMedicine
Shalamov, Cromwell, Guha · 2026 · AJP reports
Background: Congenital central hypoventilation syndrome (CCHS) is a rare genetic disorder that causes alveolar hypoventilation because of impaired chemoreceptor response to hypercapnia and hypoxia occurring due to a dysfunctional central…
View details →DOI: 10.1055/a-2873-6868 Negative / Null Result ReportMedicine
Su, Huang, Wang · 2026 · SLAS technology
The accumulation of pathological bronchial secretions compromises ventilation and oxygenation in critically ill patients and may lead to atelectasis or secondary infection in severe cases, making timely identification and removal of…
View details →DOI: 10.1016/j.slast.2026.100443 Negative / Null Result ReportOpen accessComputer Science
Àlex Miranda-Pascual, Javier Parra-Arnau, Thorsten Strufe · 2026 · arXiv
Sampling is renowned for its privacy amplification in differential privacy (DP), and is often assumed to improve the utility of a DP mechanism by allowing a noise reduction. In this paper, we further show that this last assumption is flawed: When measuring utility at equal privacy levels, sampling as preprocessing consistently yields penalties due to utility loss from omitting records over all canonical DP mechanisms -- Laplace, Gaussian, exponential, and report noisy max -- , as well as recent applications of sampling, such as clustering. Extending this analysis, we investigate suppression as
Negative / Null Result ReportOpen accessComputer Science
Hanbing Liu, Haoyang Li, Xiaokang Zhang et al. · 2025 · arXiv
Direct Preference Optimization (DPO) has proven effective in complex reasoning tasks like math word problems and code generation. However, when applied to Text-to-SQL datasets, it often fails to improve performance and can even degrade it. Our investigation reveals the root cause: unlike math and code tasks, which naturally integrate Chain-of-Thought (CoT) reasoning with DPO, Text-to-SQL datasets typically include only final answers (gold SQL queries) without detailed CoT solutions. By augmenting Text-to-SQL datasets with synthetic CoT solutions, we achieve, for the first time, consistent and
Negative / Null Result ReportOpen accessMathematics
Oliver J. Hines, Christian L. Hines · 2026 · arXiv
Gradient boosted decision trees require a stopping rule to avoid overfitting. The standard rule monitors a validation loss and stops if the loss fails to improve for a fixed patience period. However, the patience parameter has no interpretable scale and validation losses can be noisy or implicitly defined by a user-specified gradient. We propose ScoreStop, a gradient-based early-stopping rule that casts the stopping decision at each iteration as a test of the null hypothesis that the current predictor is the population risk minimizer. We use a functional score test, computed on validation data
Negative / Null Result ReportOpen accessComputer Science
Zelu Deng, Yujie Zhong, Sheng Guo et al. · 2021 · arXiv
This work aims at improving instance retrieval with self-supervision. We find that fine-tuning using the recently developed self-supervised (SSL) learning methods, such as SimCLR and MoCo, fails to improve the performance of instance retrieval. In this work, we identify that the learnt representations for instance retrieval should be invariant to large variations in viewpoint and background etc., whereas self-augmented positives applied by the current SSL methods can not provide strong enough signals for learning robust instance-level representations. To overcome this problem, we propose InsCL
