Negative / Null Result ReportOpen accessPhysics
Naiara Patiño, Nuria Calvet, Gladis Magris et al. · 2026 · arXiv
Magnetospheric accretion is the paradigm for accretion in Classical T-Tauri Stars (CTTS). However, the standard, one-flow magnetospheric accretion model fails to replicate important characteristics such as the observed Balmer decrements. We address this limitation by adopting a model with two axisymmetric magnetospheric accretion flows of different accretion rates and geometries. We calculate the fluxes of the hydrogen $H_α$, $H_β$, and $H_γ$ lines of each flow with the magnetospheric accretion model and use Bayesian statistics to fit the Balmer line fluxes of 139 CTTS in the Orion OB1b subass
Failed Experiment ReportOpen accessComputer Science
Tilman Plehn, Michael Spannowsky, Michihisa Takeuchi · 2011 · arXiv
In time for the first tests on LHC data we introduce a set of improvements and tests of purely kinematic top tagging algorithms. First, we show how different jet algorithms can be used for different transverse momentum regimes. Combining pruning and filtering in the reconstruction can enhance the signal over background ratio significantly, while larger jet radii only give minor improvements. Finally, bottom tagging can be added to the top tagger, but at least for the HEPTopTagger does not improve the kinematic selection algorithm.
Negative / Null Result ReportOpen accessEconomics, Econometrics and Finance
Valentina Macchiati, Piero Mazzarisi, Diego Garlaschelli · 2024 · arXiv
Networks of financial exposures are the key propagators of risk and distress among banks, but their empirical structure is not publicly available because of confidentiality. This limitation has triggered the development of methods of network reconstruction from partial, aggregate information. Unfortunately, even the best methods available fail in replicating the number of directed cycles, which on the other hand play a crucial role in determining graph spectra and hence the degree of network stability and systemic risk. Here we address this challenge by exploiting the hypothesis that the stati
Negative / Null Result ReportOpen accessComputer Science
Mert Albaba, Sammy Christen, Thomas Langarek et al. · 2024 · arXiv
Acquiring complex behaviors is essential for artificially intelligent agents, yet learning these behaviors in high-dimensional settings poses a significant challenge due to the vast search space. Traditional reinforcement learning (RL) requires extensive manual effort for reward function engineering. Inverse reinforcement learning (IRL) uncovers reward functions from expert demonstrations but relies on an iterative process that is often computationally expensive. Imitation learning (IL) provides a more efficient alternative by directly comparing an agent's actions to expert demonstrations; how
Negative / Null Result ReportOpen accessComputer Science
Sarah Ball, Simeon Allmendinger, Frauke Kreuter et al. · 2025 · arXiv
Generative AI (GenAI) is increasingly used in survey contexts to simulate human preferences. While many research endeavors evaluate the quality of synthetic GenAI data by comparing model-generated responses to gold-standard survey results, fundamental questions about the validity and reliability of using LLMs as substitutes for human respondents remain. Our study provides a technical analysis of how demographic attributes and prompt variations influence latent opinion mappings in large language models (LLMs) and evaluates their suitability for survey-based predictions. Using 14 different model
Negative / Null Result ReportOpen accessPhysics
Matthew S. Clement, Nathan A. Kaib, John E. Chambers · 2019 · arXiv
Of the solar system's four terrestrial planets, the origin of Mercury is perhaps the most mysterious. Modern numerical simulations designed to model the dynamics of terrestrial planet formation systematically fail to replicate Mercury; which possesses just 5% the mass of Earth and the highest orbital eccentricity and inclination among the planets. However, Mercury's large iron-rich core and low volatile inventory stand out among the inner planets, and seem to imply a violent collisional origin. Because most algorithms used for simulating terrestrial accretion do not consider the effects of col
Negative / Null Result ReportOpen accessComputer Science
Xun Liang, Huayi Lai, Hanyu Wang et al. · 2025 · arXiv
Large language models (LLMs) have gained significant traction in medical decision support systems, particularly in the context of medical question answering and role-playing simulations. A common practice, Prompt-Based Role Playing (PBRP), instructs models to adopt different clinical roles (e.g., medical students, residents, attending physicians) to simulate varied professional behaviors. However, the impact of such role prompts on model reasoning capabilities remains unclear. This study introduces the RP-Neuron-Activated Evaluation Framework(RPNA) to evaluate whether role prompts induce disti
Negative / Null Result ReportOpen accessComputer Science
Jiarui Xie, Mutahar Safdar, Andrei Mircea et al. · 2024 · arXiv
Machine learning (ML)-based cyber-physical systems (CPSs) have been extensively developed to improve the print quality of additive manufacturing (AM). However, the reproducibility of these systems, as presented in published research, has not been thoroughly investigated due to a lack of formal evaluation methods. Reproducibility, a critical component of trustworthy artificial intelligence, is achieved when an independent team can replicate the findings or artifacts of a study using a different experimental setup and achieve comparable performance. In many publications, critical information nec
Negative / Null Result ReportOpen accessComputer Science
Sungmin Cha, Kyunghyun Cho · 2024 · arXiv
Continual learning (CL) aims to train a model on a sequence of tasks (i.e., a CL scenario) while balancing the trade-off between plasticity (learning new tasks) and stability (retaining prior knowledge). The dominantly adopted conventional evaluation protocol for CL algorithms selects the best hyperparameters (e.g., learning rate, mini-batch size, regularization strengths, etc.) within a given scenario and then evaluates the algorithms using these hyperparameters in the same scenario. However, this protocol has significant shortcomings: it overestimates the CL capacity of algorithms and relies
Negative / Null Result ReportOpen accessComputer Science
Emily L. Aiken, Andre T. Nguyen, Mauricio Santillana · 2019 · arXiv
We introduce the use of a Gated Recurrent Unit (GRU) for influenza prediction at the state- and city-level in the US, and experiment with the inclusion of real-time flu-related Internet search data. We find that a GRU has lower prediction error than current state-of-the-art methods for data-driven influenza prediction at time horizons of over two weeks. In contrast with other machine learning approaches, the inclusion of real-time Internet search data does not improve GRU predictions.
Negative / Null Result ReportOpen accessEngineering
Di Wu, Yifei Jia, Siyuan Li et al. · 2025 · arXiv
Neurophysiological decoding, fundamental to advancing brain-computer interface (BCI) technologies, has significantly benefited from recent advances in deep learning. However, existing decoding approaches largely remain constrained to single-task scenarios and individual subjects, limiting their broader applicability and generalizability. Efforts towards creating large-scale neurophysiological foundation models have shown promise, but continue to struggle with significant challenges due to pervasive data heterogeneity across subjects and decoding tasks. Simply increasing model parameters and da
Negative / Null Result ReportOpen accessComputer Science
Syed Kazmi, Berk Gorgulu, Mucahit Cevik et al. · 2023 · arXiv
Wind power forecasting helps with the planning for the power systems by contributing to having a higher level of certainty in decision-making. Due to the randomness inherent to meteorological events (e.g., wind speeds), making highly accurate long-term predictions for wind power can be extremely difficult. One approach to remedy this challenge is to utilize weather information from multiple points across a geographical grid to obtain a holistic view of the wind patterns, along with temporal information from the previous power outputs of the wind farms. Our proposed CNN-RNN architecture combine
Negative / Null Result ReportOpen accessComputer Science
Nilesh Kumar Sahu, Snehil Gupta, Haroon R Lone · 2025 · arXiv
Social Anxiety Disorder (SAD) is a widespread mental health condition, yet its lack of objective markers hinders timely detection and intervention. While previous research has focused on behavioral and non-verbal markers of SAD in structured activities (e.g., speeches or interviews), these settings fail to replicate real-world, unstructured social interactions fully. Identifying non-verbal markers in naturalistic, unstaged environments is essential for developing ubiquitous and non-intrusive monitoring solutions. To address this gap, we present AnxietyFaceTrack, a study leveraging facial video
Negative / Null Result ReportOpen accessPhysics
Michael P. Grehan, Tanisha Ghosal, James R. Beattie et al. · 2025 · arXiv
High-energy astrophysical systems and compact objects are frequently modeled using ideal relativistic magnetohydrodynamic (MHD) or force-free electrodynamic (FFE) simulations, with the underlying assumption that the discretisation from the numerical scheme introduces an effective (numerical) magnetic resistivity that adequately resembles an explicit resistivity. However, it is crucial to note that numerical resistivity can fail to replicate essential features of explicit resistivity. In this study, we compare the 1D resistive decay and 2D reconnection properties of four commonly used physical
Negative / Null Result ReportOpen accessComputer Science
Ali Ebrahimpour-Boroojeny · 2025 · arXiv
We propose new methodologies for both unlearning random set of samples and class unlearning and show that they outperform existing methods. The main driver of our unlearning methods is the similarity of predictions to a retrained model on both the forget and remain samples. We introduce Adversarial Machine UNlearning (AMUN), which surpasses prior state-of-the-art methods for image classification based on SOTA MIA scores. AMUN lowers the model's confidence on forget samples by fine-tuning on their corresponding adversarial examples. Through theoretical analysis, we identify factors governing AM
Negative / Null Result ReportOpen accessComputer Science
Shaoxuan Zhou, Yafei Sun, Jing Zhang et al. · 2026 · arXiv
Short-video platforms like Douyin and Kwai have become central to adolescent digital life, but they also risk exposing teens to algorithmically amplified harmful content. Despite its societal importance, the scale, mechanisms, and real-world impact of this exposure remain poorly understood. Measuring it is challenging: recommendation feeds are personalized black boxes, harmful content employs sophisticated evasion tactics, and naive crawlers fail to replicate authentic teen behavior. To bridge this gap, we propose PHTV-Scout, the first large-scale, behaviorally grounded measurement framework f
Failed Experiment ReportOpen accessComputer Science
Jayoo Hwang, Xiaowen Zhang, Vedant Padwal · 2026 · arXiv
Autonomous web navigation remains challenging for LLM agents, and the strongest generalist systems rely on proprietary reasoning models whose inference cost is prohibitive for the repetitive tasks where such agents would be most useful. We argue this gap stems not from insufficient model capability but from agent architectures that fail to replicate three human cognitive advantages: selective attention to relevant page regions, persistent memory of website structure, and procedural fluency with common interaction patterns. We introduce WebChallenger, a web agent framework that addresses each g
Negative / Null Result ReportOpen accessPhysics
Dominic Anstey, Eloy de Lera Acedo, Will Handley · 2022 · arXiv
Global 21cm cosmology aims to investigate the cosmic dawn and epoch of reionisation by measuring the sky averaged HI absorption signal, which requires, accurate modelling of, or correction for, the bright radio foregrounds and distortions arising from chromaticity of the antenna beam. We investigate the effect of improving foreground modelling by fitting data sets from many observation times simultaneously in a single Bayesian analysis, fitting for the same parameter set by performing these fits on simulated data. We find that for a hexagonal dipole antenna, this simultaneous fitting produces
Negative / Null Result ReportOpen accessMathematics
Justin Boutilier, Jonas Oddur Jonasson, Hannah Li et al. · 2024 · arXiv
We study RCTs that evaluate the impact of service interventions, for example, teachers or advisors conducting proactive outreach to at-risk students, medical providers giving medication adherence support by calling or texting, or social workers that conduct home visits. A defining feature of service interventions is that they are delivered by a capacity-constrained resource -- teachers, healthcare providers, or social workers -- whose limited availability creates causal inference complications. Because participants share a finite service capacity, adding more participants can reduce the timeli
Negative / Null Result ReportOpen accessPhysics
Christian Quirouette, Risavarshni Thevakumaran, Kyosuke Adachi et al. · 2024 · arXiv
Endpoint dilution (TCID50) assays cannot count the number of infectious virions (IVs), and instead are limited to counting the number of Specific INfections caused by the sample (SIN). The latter depends not only on whether virions are infectious, but also on the cells and the experimental conditions under which they interact. These interactions are random and controlled by parameters such as the rates at which IVs lose infectivity, enter cells, or fail to replicate following cell entry. Here, stochastic TCID50 assays are simulated to determine how the random number of infected wells relates t
Negative / Null Result ReportOpen accessComputer Science
Rohit Agarwala, Kalyan Dey · 2025 · arXiv
We employ PYTHIA8 simulations to study forward-backward (FB) correlations in pp collisions at LHC energies, probing non-perturbative QCD dynamics via color reconnection (CR) and QCD radiation (ISR/FSR). Using \texttt{PYTHIA8} (v8.311) under ALICE/ATLAS kinematics, we analyze: extensive: FB multiplicity ($b_{\rm corr}^{\rm mult}$) and summed $p_{\rm T}$ ($b_{\rm corr}^{\sum p_{\rm T}}$) correlations; intensive: FB mean $p_{\rm T}$ ($b_{\rm corr}^{\overline p_{\rm T}}$) correlations; and \textit{strongly intensive:} $Σ_{\rm N_F N_B}$ quantity in symmetric pseudorapidity intervals, validated agai
Failed Experiment ReportOpen accessComputer Science
Liyun Zhang, Jiayi Guo · 2026 · arXiv
We document an empirical phenomenon in chain-of-thought and ReAct agents driven by ten large language models from seven architecture families: meaning-bearing perturbations (e.g., paraphrase, synonym) alter final answers more often than presentation perturbations (e.g., formatting, reordering) of comparable severity. Across 68 cells spanning GSM8K, MATH, and HotpotQA (1,530 originals and $\sim$11,150 variants), the inconsistency gap averages +19.69 pp after severity matching (paired $t=9.58$, $p<0.0001$), with 64/68 cells positive. The gap survives four severity-proxy audits and remains signif
Negative / Null Result ReportOpen accessPhysics
David M. Hernandez, Eric Agol, Matthew J. Holman et al. · 2021 · arXiv
Symplectic integrators are widely used for the study of planetary dynamics and other $N$-body problems. In a study of the outer Solar system, we demonstrate that individual symplectic integrations can yield biased errors in the semi-major axes and possibly other orbital elements. The bias is resolved by studying an ensemble of initial conditions of the outer Solar system. Such statistical sampling could significantly improve measurement of planetary system properties like their secular frequencies. We also compared the distributions of action-like variables between high and low accuracy integr
Negative / Null Result ReportOpen accessPhysics
M. Cretignier, X. Dumusque, N. C. Hara et al. · 2021 · arXiv
Aims: Even the most-precise radial-velocity instruments gather high-resolution spectra that present systematic errors that a data reduction pipeline cannot identify and correct for efficiently. In this paper, we aim at improving the radial-velocity precision of HARPS measurements by cleaning individual extracted spectra using the wealth of information contained in spectra time-series. Methods: We developed YARARA, a post-processing pipeline designed to clean high-resolution spectra from instrumental systematics and atmospheric contamination. Spectra are corrected for: tellurics, interference p
Failed Experiment ReportOpen accessComputer Science
Xinzhe Chen, Jianjiang Li · 2023 · arXiv
The escalating surge in data generation presents formidable challenges to information technology, necessitating advancements in storage, retrieval, and utilization. With the proliferation of artificial intelligence and big data, the "Data Age 2025" report forecasts an exponential increase in global data production. The escalating data volumes raise concerns about efficient data processing. The paper addresses the predicament of achieving a lower compression ratio while maintaining or surpassing the compression performance of state-of-the-art techniques. This paper introduces a lossy compressio
Negative / Null Result ReportOpen accessPhysics
Peng Zhang, Long Zhang, Youjin Deng · 2012 · arXiv
We show that the Bethe-Peierls (BP) boundary condition should be modified for ultracold atoms with spin-orbit (SO) coupling. Moreover, we derive a general form of the modified BP boundary condition, which is applicable to a system with arbitrary kind of SO coupling. In the modified BP condition, an anisotropic term appears and the inter-atomic scattering length is normally SO-coupling dependent. For the special system in the current experiments, however, it can be proved that the scattering length is SO-coupling independent, and takes the same value as in the case without SO coupling. Our resu
Negative / Null Result ReportOpen accessComputer Science
Yoel Zimmermann, Adib Bazgir, Zartashia Afzal et al. · 2024 · arXiv
Here, we present the outcomes from the second Large Language Model (LLM) Hackathon for Applications in Materials Science and Chemistry, which engaged participants across global hybrid locations, resulting in 34 team submissions. The submissions spanned seven key application areas and demonstrated the diverse utility of LLMs for applications in (1) molecular and material property prediction; (2) molecular and material design; (3) automation and novel interfaces; (4) scientific communication and education; (5) research data management and automation; (6) hypothesis generation and evaluation; and
Negative / Null Result ReportOpen accessPhysics
Taufiq Murtadho, Federica Cataldini, Sebastian Erne et al. · 2024 · arXiv
Studying the dynamics of quantum many-body systems is often constrained by the limitations in probing relevant observables, especially in continuous systems. A powerful method to gain information about such systems is the reconstruction of local currents from the continuity equation. We show that this approach can be used to extract the total phase fluctuation of adjacent Bose gases. We validate our technique numerically and demonstrate its effectiveness by analyzing data from selected experiments simulating 1D quantum field theories through the phase difference of two parallel 1D Bose gases.
Negative / Null Result ReportOpen accessComputer Science
S. Haouat, K. Nouicer · 2013 · arXiv
In this paper we have studied the problem of scalar particles pair creation by an electric field in the presence of a minimal length. Two sets of exact solutions for the Klein Gordon equation are given in momentum space. Then the canonical method based on Bogoliubov transformation connecting the "in" with the "out" states is applied to calculate the probability to create a pair of particles and the mean number of created particles. The number of created particles per unit of time per unit of length, which is related directly to the experimental measurements, is calculated. It is shown that, wi
Negative / Null Result ReportOpen accessComputer Science
Maxim Krasnov, Ufuk Aydemir, Maxim Khlopov · 2025 · arXiv
We investigate the particle production by the Nambu-Goldstone boson in the spontaneous baryogenesis scenario for large misalignment angles. Studying numerically the case of an arbitrary initial phase, we reproduce the cubic dependence on the initial value of the phase of baryon asymmetry in the case of small oscillations and present our results for an initial phase close to π. Our calculations indicate that there is a saturation in particle production as the initial phase approaches π in Minkowski spacetime. Furthermore, we present numerical solutions in the Friedmann-Lemaître-Robertson-Walker
