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Chao Jiang

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Published work

12 published item(s)

preprint2026arXiv

Compressed Video Aggregator: Content-driven Module for Efficient Micro-Video Recommendation

We propose Compressed Video Aggregator (CVA), a lightweight micro-video recommendation module that decouples video information from preference learning. It aggregates frozen VFM embeddings, and uses latent reasoning without cross-attention projection, producing compact video embeddings for recommenders. Due to the redundancy in the frame count of the original benchmark and its overly coarse sampling, we used titles to re-select key frames based on CLIP. Experiments on MicroLens and Short-Video show consistent gains with orders-of-magnitude reductions in training time and GPU memory, and re-selected frames can further enhance the performance of all methods, including CVA. Furthermore, we also discussed the impact of several scenarios involving erroneous titles on our method. Code will be released soon.

preprint2026arXiv

KDCM: Reducing Hallucination in LLMs through Explicit Reasoning Structures

To mitigate hallucinations in large language models (LLMs), we propose a framework that focuses on errors induced by prompts. Our method extends a chain-style knowledge distillation approach by incorporating a programmable module that guides knowledge graph exploration. This module is embedded as executable code within the reasoning prompt, allowing the model to leverage external structured knowledge during inference. Based on this design, we develop an enhanced distillation-based reasoning framework that explicitly regulates intermediate reasoning steps, resulting in more reliable predictions. We evaluate the proposed approach on multiple public benchmarks using GPT-4 and LLaMA-3.3. Experimental results show that code-guided reasoning significantly improves contextual modeling and reduces prompt-induced hallucinations. Specifically, HIT@1, HIT@3, and HIT@5 increase by 15.64%, 13.38%, and 13.28%, respectively, with scores exceeding 95% across several evaluation settings. These findings indicate that the proposed method effectively constrains erroneous reasoning while improving both accuracy and interpretability.

preprint2026arXiv

Mitigating Prompt-Induced Hallucinations in Large Language Models via Structured Reasoning

To address hallucination issues in large language models (LLMs), this paper proposes a method for mitigating prompt-induced hallucinations. Building on a knowledge distillation chain-style model, we introduce a code module to guide knowledge-graph exploration and incorporate code as part of the chain-of-thought prompt, forming an external knowledge input that provides more accurate and structured information to the model. Based on this design, we develop an improved knowledge distillation chain-style model and leverage it to analyze and constrain the reasoning process of LLMs, thereby improving inference accuracy. We empirically evaluate the proposed approach using GPT-4 and LLaMA-3.3 on multiple public datasets. Experimental results demonstrate that incorporating code modules significantly enhances the model's ability to capture contextual information and effectively mitigates prompt-induced hallucinations. Specifically, HIT@1, HIT@3, and HIT@5 improve by 15.64%, 13.38%, and 13.28%, respectively. Moreover, the proposed method achieves HIT@1, HIT@3, and HIT@5 scores exceeding 95% across several evaluation settings. These results indicate that the proposed approach substantially reduces hallucination behavior while improving the accuracy and verifiability of large language models.

preprint2022arXiv

Application of simultaneous and continuous measurement of noncommutative observables: Preparation of the pure ideal quadrature-squeezed state by feedback control

As an application of the simultaneous and continuous measurement of noncommutative observables formulated in our previous paper [C. Jiang and G. Watanabe, Phys. Rev. A 102, 062216 (2020)], we propose a scheme to generate the pure ideal quadrature-squeezed state in a one-dimensional harmonic oscillator system by the feedback control based on such type of measurement of noncommutative quadrature observables. We find that, by appropriately setting the strengths of the measurement and the feedback control, the pure ideal quadrature-squeezed state with arbitrary squeezedness can be produced. This is in contrast to the scheme based on the single-observable measurement and the feedback control, where only nonideal squeezed states with squeezing of the measured quadrature are produced.

preprint2021arXiv

Densely connected neural networks for nonlinear regression

Densely connected convolutional networks (DenseNet) behave well in image processing. However, for regression tasks, convolutional DenseNet may lose essential information from independent input features. To tackle this issue, we propose a novel DenseNet regression model where convolution and pooling layers are replaced by fully connected layers and the original concatenation shortcuts are maintained to reuse the feature. To investigate the effects of depth and input dimension of proposed model, careful validations are performed by extensive numerical simulation. The results give an optimal depth (19) and recommend a limited input dimension (under 200). Furthermore, compared with the baseline models including support vector regression, decision tree regression, and residual regression, our proposed model with the optimal depth performs best. Ultimately, DenseNet regression is applied to predict relative humidity, and the outcome shows a high correlation (0.91) with observations, which indicates that our model could advance environmental data analysis.

preprint2021arXiv

Quantum dynamics under simultaneous and continuous measurement of noncommutative observables

We consider simultaneous and continuous measurement of two noncommutative observables of the system whose commutator is not necessarily a $c$-number. We revisit the Arthurs-Kelly model and generalize it to describe the simultaneous measurement of two observables of the system. Using this generalized model, we continuously measure the system by following the scheme proposed by Scott and Milburn [Scott and Milburn, Phys. Rev. A 63, 042101 (2001)]. We find that the unconditioned master equation reduces to the Lindblad form in the continuous limit. In addition, we find that the master equation does not contain a cross term of these two measurements. Finally, we propose a scheme to prepare the state of a two-level system in an external field by feedback control based on the simultaneous, continuous measurement of the two observables.

preprint2020arXiv

Discourse Level Factors for Sentence Deletion in Text Simplification

This paper presents a data-driven study focusing on analyzing and predicting sentence deletion -- a prevalent but understudied phenomenon in document simplification -- on a large English text simplification corpus. We inspect various document and discourse factors associated with sentence deletion, using a new manually annotated sentence alignment corpus we collected. We reveal that professional editors utilize different strategies to meet readability standards of elementary and middle schools. To predict whether a sentence will be deleted during simplification to a certain level, we harness automatically aligned data to train a classification model. Evaluated on our manually annotated data, our best models reached F1 scores of 65.2 and 59.7 for this task at the levels of elementary and middle school, respectively. We find that discourse level factors contribute to the challenging task of predicting sentence deletion for simplification.

preprint2016arXiv

Accelerated atomistic simulation study on the stability and mobility of carbon tri-interstitial cluster in cubic SiC

Using a combination of kinetic Activation Relaxation Technique with empirical potential and ab initio based climbing image nudged elastic band method, we perform an extensive search of the migration and rotation paths of the most stable carbon tri-interstitial cluster in cubic SiC. Our research reveals paths with the lowest energy barriers to migration, rotation, and dissociation of the most stable cluster. The kinetic properties of the most stable cluster, including its mobility, rotation behavior at different temperatures and stability against high temperature annealing, are discussed based on the calculated transition barriers. In addition to fundamental insights, our study provides a methodology for investigation of other extended defects in a technologically important material.

preprint2016arXiv

Probabilistic Human Mobility Model in Indoor Environment

Understanding human mobility is important for the development of intelligent mobile service robots as it can provide prior knowledge and predictions of human distribution for robot-assisted activities. In this paper, we propose a probabilistic method to model human motion behaviors which is determined by both internal and external factors in an indoor environment. While the internal factors are represented by the individual preferences, aims and interests, the external factors are indicated by the stimulation of the environment. We model the randomness of human macro-level movement, e.g., the probability of visiting a specific place and staying time, under the Bayesian framework, considering the influence of both internal and external variables. We use two case studies in a shopping mall and in a college student dorm building to show the effectiveness of our proposed probabilistic human mobility model. Real surveillance camera data are used to validate the proposed model together with survey data in the case study of student dorm.

preprint2016arXiv

Using machine learning to identify factors that govern amorphization of irradiated pyrochlores

Structure-property relationships is a key materials science concept that enables the design of new materials. In the case of materials for application in radiation environments, correlating radiation tolerance with fundamental structural features of a material enables materials discovery. Here, we use a machine learning model to examine the factors that govern amorphization resistance in the complex oxide pyrochlore ($A_2B_2$O$_7$). We examine the fidelity of predictions based on cation radii and electronegativities, the oxygen positional parameter, and the energetics of disordering and amorphizing the material. No one factor alone adequately predicts amorphization resistance. We find that, when multiple families of pyrochlores (with different B cations) are considered, radii and electronegativities provide the best prediction but when the machine learning model is restricted to only the $B$=Ti pyrochlores, the energetics of disordering and amorphization are optimal. This work provides new insight into the factors that govern the amorphization susceptibility and highlights the ability of machine learning approaches to generate that insight.

preprint2015arXiv

Band-gap and Band-edge Engineering of Multicomponent Garnet Scintillators: A First-principles Study

Complex doping schemes in RE$_3$Al$_5$O$_{12}$ (RE=rare earth element) garnet compounds have recently led to pronounced improvements in scintillator performance. Specifically, by admixing lutetium and yttrium aluminate garnets with gallium and gadolinium, the band-gap was altered in a manner that facilitated the removal of deleterious electron trapping associated with cation antisite defects. Here, we expand upon this initial work to systematically investigate the effect of substitutional admixing on the energy levels of band edges. Density functional theory was used to survey potential admixing candidates that modify either the conduction band minimum (CBM) or valence band maximum (VBM). We considered two sets of compositions based on Lu$_3$B$_5$O$_{12}$ where B = Al, Ga, In, As, and Sb; and RE$_3$Al$_5$O$_{12}$, where RE = Lu, Gd, Dy, and Er. We found that admixing with various RE cations does not appreciably effect the band gap or band edges. In contrast, substituting Al with cations of dissimilar ionic radii has a profound impact on the band structure. We further show that certain dopants can be used to selectively modify only the CBM or the VBM. Specifically, Ga and In decrease the band gap by lowering the CBM, while As and Sb decrease the band gap by raising the VBM. These results demonstrate a powerful approach to quickly screen the impact of dopants on the electronic structure of scintillator compounds, identifying those dopants which alter the band edges in very specific ways to eliminate both electron and hole traps responsible for performance limitations. This approach should be broadly applicable for the optimization of electronic and optical performance for a wide range of compounds by tuning the VBM and CBM.

preprint2012arXiv

First-principles based modeling of hydrogen permeation through Pd-Cu alloys

The solubility and diffusivity of hydrogen in disordered Pd1-xCux alloys are investigated using a combination of first-principles calculations, a composition-dependent local cluster expansion (CDLCE) technique, and kinetic Monte Carlo simulations. We demonstrate that a linear CDCLE model can already accurately describe interstitial H in Pd1-xCux alloys over the entire composition range (0\leqx\leq1) with accuracy comparable to that of direct first-principles calculations. Our predicted H solubility and permeability results are in reasonable agreement with experimental measurements. The proposed model is quite general and can be employed to rapidly and accurately screen a large number of alloy compositions for potential membrane applications. Extension to ternary or higher-order alloy systems should be straightforward. Our study also highlights the significant effect of local lattice relaxations on H energetics in size-mismatched disordered alloys, which has been largely overlooked in the literature.