arXiv subjects

Kaituo Zhang

Publications and source records attributed to Kaituo Zhang.

At least 19 recordsLinked to original sources

arXiv2026

Observational constraints on fractional holographic dark energy in the light of DESI DR2

Based on the fractional entropy from fractional quantum mechanics, fractional holographic dark energy (FHDE) has been proposed with the Hubble horizon as the IR cutoff (FHDEH). We extend this framework by adopting the future event horizon and the particle horizon as the IR cutoff, proposing the FHDEF and FHDEP models. Using the SN+OHD+DESI DR2 dataset to constrain these models, we find that all three models provide a marginally lower $\chi^{2}_{min}$ compared to $\Lambda$CDM but without significant preference according to AIC and BIC. When CMB distance priors are included, the FHDEH and FHDEP models are strongly ruled out. We further analyze the cosmological evolution for these models, and find that only the FHDEF model predicts nearly identical evolutions of $\Omega_{m}$ and $\Omega_{de}$ to those of the $\Lambda$CDM model across cosmic history, but its deceleration parameter $q$ deviate from the $\Lambda$CDM model in the future, indicating richer late time dynamics beyond the standard $\Lambda$CDM cosmology.

gr-qc↗

arXiv2026

Are Tools All We Need? Unveiling the Tool-Use Tax in LLM Agents

Tool-augmented reasoning has become a popular direction for LLM-based agents, and it is widely assumed to improve reasoning and reliability. However, we demonstrate that this consensus does not always hold: in the presence of semantic distractors, tool-augmented reasoning does not necessarily outperform native CoT. To explain this performance gap, we propose a Factorized Intervention Framework that isolates the cost of prompt formatting, the overhead of the tool-calling protocol, and the actual gain from executing tools. Our analysis reveals a critical tradeoff: under semantic noise, the gains from tools often fail to offset the "tool-use tax", which is the performance degradation introduced by the tool-calling protocol itself. To address this, we introduce G-STEP, a lightweight inference-time gate to mitigate protocol-induced errors. While this yields partial recovery, our findings suggest that more substantial improvements still require strengthening the model's intrinsic reasoning and tool-interaction capabilities.

cs.AI↗

arXiv2026

A Survey on Evaluating Quality and Trustworthiness in LLM-Generated Data

Large Language Models (LLMs) have emerged as powerful tools for generating data across various modalities. By transforming data from a scarce resource into a controllable asset, LLMs mitigate the bottlenecks imposed by the acquisition costs of real-world data for model training, evaluation, and system iteration. However, ensuring the high quality of LLM-generated synthetic data remains a critical challenge. Existing research primarily focuses on generation methodologies, with limited direct attention to the quality of the resulting data. Furthermore, most studies are restricted to single modalities, lacking a unified perspective across different data types. To bridge this gap, we propose the \textbf{LLM Data Auditor framework}. In this framework, we first describe how LLMs are utilized to generate data across six distinct modalities. More importantly, we systematically categorize intrinsic metrics for evaluating synthetic data from two dimensions: quality and trustworthiness. This approach shifts the focus from extrinsic evaluation, which relies on downstream task performance, to the inherent properties of the data itself. Using this evaluation system, we analyze the experimental evaluations of representative generation methods for each modality and identify substantial deficiencies in current evaluation practices. Based on these findings, we offer concrete recommendations for the community to improve the evaluation of data generation. Finally, the framework outlines methodologies for the practical application of synthetic data across different modalities.

cs.AI↗

arXiv2026

Cleansing the Artificial Mind: A Self-Reflective Detoxification Framework for Large Language Models

Recent breakthroughs in Large Language Models (LLMs) have revealed remarkable generative capabilities and emerging self-regulatory mechanisms, including self-correction and self-rewarding. However, current detoxification techniques rarely exploit these built-in abilities; instead, they rely on external modules, labor-intensive data annotation, or human intervention --factors that hinder scalability and consistency. In this paper, we introduce a fully self-reflective detoxification framework that harnesses the inherent capacities of LLMs to detect, correct toxic content, and refine LLMs without external modules and data annotation. Specifically, we propose a Toxic Signal Detector --an internal self-identification mechanism, coupled with a systematic intervention process to transform toxic text into its non-toxic counterpart. This iterative procedure yields a contrastive detoxification dataset used to fine-tune the model, enhancing its ability for safe and coherent text generation. Experiments on benchmark datasets such as DetoxLLM and ParaDetox show that our method achieves better detoxification performance than state-of-the-art methods while preserving semantic fidelity. By obviating the need for human intervention or external components, this paper reveals the intrinsic self-detoxification ability of LLMs, offering a consistent and effective approach for mitigating harmful content generation. Ultimately, our findings underscore the potential for truly self-regulated language models, paving the way for more responsible and ethically guided text generation systems.

cs.CL↗

arXiv2025

New tests of cosmic distance duality relation with DESI 2024 BAO observations

In this paper, we test the cosmic distance duality relation (CDDR), as required by the Etherington reciprocity theorem, which connects the angular diameter distance and the luminosity distance via the relation $ D_{\rm L}(z) = D_{\rm A}(z)(1+z)^2 $. Our analysis is based on the latest baryon acoustic oscillation (BAO) measurements provided by the Dark Energy Survey (DES), the Baryon Oscillation Spectroscopic Survey (BOSS)/Extended BOSS (eBOSS), and the Dark Energy Spectroscopic Instrument (DESI) surveys. Specifically, an unbiased test of the CDDR is performed through a novel, model-independent method inspired by the two-point diagnostic approach, with DES-SN5YR and Pantheon type Ia supernova (SN Ia) sample reconstructed using the Artificial Neural Network (ANN) technique. This methodology effectively eliminates all nuisance parameters, including the sound horizon scale $ r_{\rm d} $ from BAO and the absolute magnitude $ M_{\rm B} $ from SN Ia. A set of $ N-1 $ independent CDDR ratios $ \eta_{ij} $ are constructed for statistical analysis. At the current observational level, no significant deviation from the CDDR is observed at low redshifts, whereas we find positive evidence ($>2\sigma$ C.L.) of deviation from the CDDR at two high redshifts ($z=2.33$ and $z=2.334$). Therefore, our results confirm that the BAO measurement provides a powerful tool to test such fundamental relation in modern cosmology.

astro-ph.CO↗

arXiv2025

Testing the cosmic distance duality relation using Type Ia supernovae and BAO observations

In this work, we propose to utilize the observed ratio of spherically-averaged distance to the sound horizon scale from Baryon Acoustic Oscillation (BAO) data to test the cosmic distance duality relation (CDDR) by comparing the luminosity distances (LDs) obtained from Type Ia supernovae (SNIa) observations with angular diameter distances (ADDs) derived from these ratio measurements, using a cosmological-model-independent method. To match the LDs with the ADDs at the identical redshifts, we employ two methods: a compressed form of the Pantheon sample and a hybrid approach that combines the binning method with an artificial neural network (ANN). The Hubble parameter $H(z)$ at any redshift is reconstructed from the observed Hubble parameter data with the ANN to derive the ADD. To avoid potential biases resulted from the specific prior values of the absolute magnitude $M_{\rm B}$ of SNIa and the sound horizon scale $r_{\rm d}$ from BAO measurements, we introduce the fiducial parameter $\kappa\equiv10^{M_{\rm B} \over 5}\, r_{\rm d}^{3 \over 2} $ and marginalize their impacts by treating them as nuisance parameters with flat prior distributions in our statistical analysis. Subsequently, we update the measurements of ratio of the transverse comoving distance to the sound horizon scale from the latest BAO data released by the Dark Energy Spectroscopic Instrument (DESI) collaboration for CDDR testing. Our results indicate that BAO observation provides a powerful tool for testing the CDDR, independent of both the absolute magnitude $M_{\rm B}$ and sound horizon scale $r_{\rm d}$, as well as any cosmological model.

astro-ph.CO↗

arXiv2025

Holographic inflation and holographic dark energy from entropy of the anti-de Sitter black hole

Based on the entropy of anti-de Sitter black hole, a new holographic dark energy model has been proposed. When the Hubble horizon and particle horizon are chosen as the IR cutoff, the late-time accelerated expansion of universe is realized. In this paper, we consider the Hubble horizon as the IR cutoff to investigate holographic inflation and slow-roll inflation in this model. We find that slow-roll inflation with the chaotic potential $V_{0}\phi^{n}$ is favored by Planck results for some special cases, such as $n=1/3$ and $n=1/2$, while holographic inflation is not supported by Planck results. Then, we analyze the reheating temperature and the number of reheating e-folds in this model, and we find that the results favor the cases $n=1/3$ and $n=1/2$. Finally, we use the dynamical analysis method, statefinder diagnostic pairs, and the Hubble diagram to analyze this model. Our results indicate that when $b^{2}$ takes a small value, this model cannot be distinguished from the standard $\Lambda$CDM model and can serve as an alternative to it.

gr-qc↗

arXiv2024

Improved AutoEncoder with LSTM module and KL divergence

The task of anomaly detection is to separate anomalous data from normal data in the dataset. Models such as deep convolutional autoencoder (CAE) network and deep supporting vector data description (SVDD) model have been universally employed and have demonstrated significant success in detecting anomalies. However, the over-reconstruction ability of CAE network for anomalous data can easily lead to high false negative rate in detecting anomalous data. On the other hand, the deep SVDD model has the drawback of feature collapse, which leads to a decrease of detection accuracy for anomalies. To address these problems, we propose the Improved AutoEncoder with LSTM module and Kullback-Leibler divergence (IAE-LSTM-KL) model in this paper. An LSTM network is added after the encoder to memorize feature representations of normal data. In the meanwhile, the phenomenon of feature collapse can also be mitigated by penalizing the featured input to SVDD module via KL divergence. The efficacy of the IAE-LSTM-KL model is validated through experiments on both synthetic and real-world datasets. Experimental results show that IAE-LSTM-KL model yields higher detection accuracy for anomalies. In addition, it is also found that the IAE-LSTM-KL model demonstrates enhanced robustness to contaminated outliers in the dataset. All code may be found at https://github.com/crazyn2/IAE-LSTM-KL_codes

cs.LG↗

arXiv2024

Testing the cosmic distance duality relation using Type Ia supernovae and radio quasars through model-independent methods

In this work, we perform a cosmological-model-independent test on the cosmic distance duality relation (CDDR) by comparing the angular diameter distance (ADD) obtained from the compact radio quasars (QSOs) with the luminosity distance (LD) from the Pantheon Type Ia supernovae (SNIa) sample. The binning method and Artificial Neural Network (ANN) are employed to match ADD data with LD data at the same redshift, and three different parameterizations are adopted to quantify the possible deviations from the CDDR. We initially investigate the impacts of the specific prior values for the absolute magnitude $M_{\rm B}$ from SNIa and the linear size scaling factor $l$ from QSOs on the CDDR test, demonstrating that these prior values introduce significant biases in the CDDR test. To avoid the biases, we propose a method independent of $M_{\rm B}$ and $l$ to test CDDR, which treats the fiducial value of a new variable $κ\equiv10^{M_{\rm B} \over 5}\,l$ as a nuisance parameter and then marginalize its impact with a flat prior in the statistical analysis. The results show that the CDDR is consistent with the observational data, and QSOs can serve as a powerful tool for testing the CDDR independent of cosmological models.

astro-ph.CO↗

arXiv2024

Evolution of the early universe in Einstein-Cartan theory

Einstein--Cartan theory is a generalization of general relativity that introduces spacetime torsion. In this paper, we perform phase space analysis to investigate the evolution of the early universe in Einstein--Cartan theory. By studying the stability of critical points in the dynamical system, we find that there exist two stable critical points which represent an Einstein static solution and an expanding solution, respectively. After analyzing the phase diagram of the dynamical system, we find that the early universe may exhibit an Einstein static state, an oscillating state, or a bouncing state. By assuming the equation of state $\omega$ can decrease over time $t$, the universe can depart from the initial Einstein static state, oscillating state, or bouncing state and then evolve into an inflationary phase. Then, we analyze four different inflationary evolution cases in Einstein--Cartan theory and find that a time-variable equation of state $\omega$ cannot yield values of $n_{s}$ and $r$ consistent with observations, while a time-invariant equation of state $\omega$ is supported by the Planck 2018 results. Thus, in Einstein--Cartan theory, the universe likely originates from a bouncing state rather than an Einstein static state or an oscillating state.

gr-qc↗

arXiv2024

Constant-roll inflation and primordial black holes within Barrow entropic framework

In this paper, starting from the modified Einstein field equations, we derive the modified scalar spectral index $n_{s}$ and the modified tensor-to-scalar ratio $r$ in Barrow entropy model, calculate their values for the power-law, periodic, and hilltop potential models, constrain the model parameter $\delta$ and the potential parameter using Planck 2018 data, and find that increasing $\delta$ causes a significant decrease in $r$. Then, we calculate the primordial curvature perturbation power spectra, primordial black hole (PBH) abundance, and scalar induced gravitational waves (SIGWs) for these models, finding PBH mass of approximately $10^{-12} M_{\odot}$, PBH abundance nearly $0.98$, and the peak frequencies of SIGWs on the order $10^{-3} \mathrm{Hz}$, indicating that these models not only generate sufficient PBHs which can contribute one-third of the dark matter content but could also be detected by next-generation missions such as LISA, Taiji, and TianQin. Subsequently, we analyze the evolution of PBHs and find that when the effective equation of state parameter evolves from $1/3$ to $-1/3$, the accretion mass increases to approximately $10^{2}M_{i}$, while the temperature of the PBHs decreases from $10^{4}K$ to $10^{2}K$, suggesting that PBHs exist and are detectable today.

gr-qc↗

arXiv2023

Joint Constraints on the Hubble Constant, Spatial Curvature, and Sound Horizon from the Late-time Universe with Cosmography

In this paper, using the latest Pantheon+ sample of Type Ia supernovae (SNe Ia), Baryon Acoustic Oscillation (BAO) measurements, and observational Hubble data (OHD), we carry out a joint constraint on the Hubble constant $H_0$, the spatial curvature $Ω_{\rm K}$, and the sound horizon at the end of drag epoch $r_{\rm d}$. To be model-independent, four cosmography models, i.e., the Taylor series in terms of redshift $y_1=z/(1+z)$, $y_2=\arctan(z)$, $y_3=\ln(1+z)$, and the Padé approximants, are used without the assumption of flat Universe. The results show that the $H_0$ is anti-correlated with $Ω_{\rm K}$ and $r_{\rm d}$, indicating smaller $Ω_{\rm K}$ or $r_{\rm d}$ would be helpful in alleviating the Hubble tension. And the values of $H_0$ and $r_{\rm d}$ are consistent with the estimate derived from the Planck Cosmic Microwave Background (CMB) data based on the flat $Λ$CDM model, but $H_0$ is in 2.3$\sim$3.0$σ$ tension with that obtained by \cite{Riess2022} in all these cosmographic approaches. Meanwhile, a flat Universe is preferred by the present observations under all approximations except the third order of $y_1$ and $y_2$ of the Taylor series. Furthermore, according to the values of the Bayesian evidence, we found that the flat $Λ$CDM remains to be the most favored model by the joint datasets, and the Padé approximant of order (2,2), the third order of $y_3$ and $y_1$ are the top three cosmographic expansions that fit the datasets best, while the Taylor series in terms of $y_2$ are essentially ruled out.

astro-ph.CO↗

arXiv2023

Robust Outlier Detection Method Based on Local Entropy and Global Density

By now, most outlier-detection algorithms struggle to accurately detect both point anomalies and cluster anomalies simultaneously. Furthermore, a few K-nearest-neighbor-based anomaly-detection methods exhibit excellent performance on many datasets, but their sensitivity to the value of K is a critical issue that needs to be addressed. To address these challenges, we propose a novel robust anomaly detection method, called Entropy Density Ratio Outlier Detection (EDROD). This method incorporates the probability density of each sample as the global feature, and the local entropy around each sample as the local feature, to obtain a comprehensive indicator of abnormality for each sample, which is called Entropy Density Ratio (EDR) for short in this paper. By comparing several competing anomaly detection methods on both synthetic and real-world datasets, it is found that the EDROD method can detect both point anomalies and cluster anomalies simultaneously with accurate performance. In addition, it is also found that the EDROD method exhibits strong robustness to the number of selected neighboring samples, the dimension of samples in the dataset, and the size of the dataset. Therefore, the proposed EDROD method can be applied to a variety of real-world datasets to detect anomalies with accurate and robust performances.

cs.IT↗

arXiv2023

CMB power spectrum in the emergent universe with k-essence

The emergent universe provides a possible method to avoid the big bang singularity by considering that the universe stems from an stable Einstein static universe rather than the singularity. Since the Einstein static universe exists before inflation, it may leave some relics in the CMB power spectrum. In this paper, we analyze the stability condition for the Einstein static universe in general relativity with k-essence against both the scalar and tensor perturbations. And we find the emergent universe can be successfully realized by constructing a scalar potential and an equation of state parameter. Solving the curved Mukhanov-Sasaki equation, we obtain the analytical approximation for the primordial power spectrum, and then depict the TT-spectrum of the emergent universe. The results show that both the primordial power spectrum and CMB TT-spectrum are suppressed on large scales.

gr-qc↗

arXiv2022

Model-independent test for the cosmic distance duality relation with Pantheon and eBOSS DR16 quasar sample

In this paper, we carry out a new model-independent cosmological test for the cosmic distance duality relation~(CDDR) by combining the latest five baryon acoustic oscillations (BAO) measurements and the Pantheon type Ia supernova (SNIa) sample. Particularly, the BAO measurement from extended Baryon Oscillation Spectroscopic Survey~(eBOSS) data release~(DR) 16 quasar sample at effective redshift $z=1.48$ is used, and two methods, i.e. a compressed form of Pantheon sample and the Artificial Neural Network~(ANN) combined with the binning SNIa method, are applied to overcome the redshift-matching problem. Our results suggest that the CDDR is compatible with the observations, and the high-redshift BAO and SNIa data can effectively strengthen the constraints on the violation parameters of CDDR with the confidence interval decreasing by more than 20 percent. In addition, we find that the compressed form of observational data can provide a more rigorous constraint on the CDDR, and thus can be generalized to the applications of other actual observational data with limited sample size in the test for CDDR.

astro-ph.CO↗

arXiv2022

Analytical approximations for primordial power spectra in a spatially closed emergent universe

The emergent universe scenario was proposed to solve the big bang singularity by suggesting that the universe originates from an Einstein static state and then evolves into a subsequently inflationary era. Thus, to find the relic of the existence of the Einstein static state becomes a crucial work. In this paper, we derive analytical approximation of the primordial power spectra and analyze the CMB TT-spectra for the spatially closed emergent universe. After analyzing the CMB TT-spectrum of the emergent universe scenario, we find that both the CMB TT-spectra produced by the Einstein static state followed by the ultraslow-roll inflationary epoch (method I) and by a special evolution of the scale factor in the emergent scenario as $a=a_{0}+A e^{H_{0}t}$(method II) are suppressed at $l<30$, and their spectra are nearly identical. Additionally, by comparing the spectra of the emergent universe scenario with the ones of the ultraslow-roll inflationary model in the closed universe, we find that the CMB TT-spectrum of the emergent universe is similar to the one of the inflationary model with the special case $η_t = η_{max}$.

gr-qc↗

arXiv2022

A Demographic Attribute Guided Approach to Age Estimation

Face-based age estimation has attracted enormous attention due to wide applications to public security surveillance, human-computer interaction, etc. With vigorous development of deep learning, age estimation based on deep neural network has become the mainstream practice. However, seeking a more suitable problem paradigm for age change characteristics, designing the corresponding loss function and designing a more effective feature extraction module still needs to be studied. What is more, change of face age is also related to demographic attributes such as ethnicity and gender, and the dynamics of different age groups is also quite different. This problem has so far not been paid enough attention to. How to use demographic attribute information to improve the performance of age estimation remains to be further explored. In light of these issues, this research makes full use of auxiliary information of face attributes and proposes a new age estimation approach with an attribute guidance module. We first design a multi-scale attention residual convolution unit (MARCU) to extract robust facial features other than simply using other standard feature modules such as VGG and ResNet. Then, after being especially treated through full connection (FC) layers, the facial demographic attributes are weight-summed by 1*1 convolutional layer and eventually merged with the age features by a global FC layer. Lastly, we propose a new error compression ranking (ECR) loss to better converge the age regression value. Experimental results on three public datasets of UTKFace, LAP2016 and Morph show that our proposed approach achieves superior performance compared to other state-of-the-art methods.

cs.CV↗

arXiv2020

Running vacuum model in non-flat universe

We investigate observational constraints on the running vacuum model (RVM) of $Λ=3ν(H^{2}+K/a^2)+c_0$ in the spatially curved universe, where $ν$ is the model parameter, $K$ corresponds to the spatial curvature constant, and $c_{0}$ is a constant defined by the boundary conditions. As $\dotΛ \ne 0$, there are energy exchanges between vacuum, matter and radiation in RVM. We study the "geometrical degeneracy" of RVM on the CMB power spectra. By fitting the cosmological data, we find that the values of $χ^2$ in RVM and $Λ$CDM are similar to each other for the non-flat universe. Explicitly, we obtain the constraints of $ν\leq O(10^{-4})$ (68 $\%$ C.L.) and $|Ω_K|\leq O(10^{-2})$ (95 $\%$ C.L.) in our study. In addition, we show that the cosmological constraints of $Σm_ν=0.416^{+0.311}_{-0.407}$ (RVM) and $Σm_ν=0.497^{+0.335}_{-0.387}$ ($Λ$CDM) at 95$\%$ C.L. for the neutrino mass sum are relaxed in both models in the spatially curved universe.

astro-ph.CO↗