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Xiaoming Huang

Xiaoming Huang appears in the imported research catalog. Authorship, coauthor and topic links are available while profile ownership is still unclaimed.

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cond-mat.mtrl-sciArtificial IntelligenceComputer VisionComputation and Languagecond-mat.mes-hallCryptography and Securityphysics.app-ph

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Published work

8 published item(s)

preprint2026arXiv

FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments

Financial agents powered by large language models (LLMs) are increasingly deployed for investment analysis, risk assessment, and automated decision-making, where their abilities to plan, invoke tools, and manipulate mutable state introduce new security risks in high-stakes and highly regulated financial environments. However, existing safety evaluations largely focus on language-model-level content compliance or abstract agent settings, failing to capture execution-grounded risks arising from real operational workflows and state-changing actions. To bridge this gap, we propose FinVault, the first execution-grounded security benchmark for financial agents, comprising 31 regulatory case-driven sandbox scenarios with state-writable databases and explicit compliance constraints, together with 107 real-world vulnerabilities and 963 test cases that systematically cover prompt injection, jailbreaking, financially adapted attacks, as well as benign inputs for false-positive evaluation. Experimental results reveal that existing defense mechanisms remain ineffective in realistic financial agent settings, with average attack success rates (ASR) still reaching up to 50.0\% on state-of-the-art models and remaining non-negligible even for the most robust systems (ASR 6.7\%), highlighting the limited transferability of current safety designs and the need for stronger financial-specific defenses. Our code can be found at https://github.com/aifinlab/FinVault.

0 citations0 reviewsNo code linkedOpen access
preprint2026arXiv

Improving Multi-turn Dialogue Consistency with Self-Recall Thinking

Large language model (LLM) based multi-turn dialogue systems often struggle to track dependencies across non-adjacent turns, undermining both consistency and scalability. As conversations lengthen, essential information becomes sparse and is buried in irrelevant context, while processing the entire dialogue history incurs severe efficiency bottlenecks. Existing solutions either rely on high latency external memory or lose fine-grained details through iterative summarization. In this paper, we propose Self-Recall Thinking (SRT), a framework designed to address long-range contextual dependency and sparse informative signals in multi-turn dialogue. SRT identifies helpful historical turns and uses them to generate contextually appropriate responses, enabling the model to selectively recall and reason over context during inference. This process yields an endogenous reasoning process that integrates interpretable recall steps without external modules. SRT incorporates: (1) Dependency Construction: Generating and converting it into self-recall chains; (2)Capability Initialization: Training to enable reasoning chains with recall tokens capability; (3)Reasoning Improvement: Refining accuracy via verifiable rewards to optimize recall and reasoning for correct answers. Experiments on multiple datasets demonstrate that SRT improves F1 score by 4.7% and reduces end-to-end latency by 14.7% over prior methods, achieving a balance between reasoning latency and accuracy, and outperforming state-of-the-art baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ASFD: Automatic and Scalable Face Detector

Along with current multi-scale based detectors, Feature Aggregation and Enhancement (FAE) modules have shown superior performance gains for cutting-edge object detection. However, these hand-crafted FAE modules show inconsistent improvements on face detection, which is mainly due to the significant distribution difference between its training and applying corpus, COCO vs. WIDER Face. To tackle this problem, we essentially analyse the effect of data distribution, and consequently propose to search an effective FAE architecture, termed AutoFAE by a differentiable architecture search, which outperforms all existing FAE modules in face detection with a considerable margin. Upon the found AutoFAE and existing backbones, a supernet is further built and trained, which automatically obtains a family of detectors under the different complexity constraints. Extensive experiments conducted on popular benchmarks, WIDER Face and FDDB, demonstrate the state-of-the-art performance-efficiency trade-off for the proposed automatic and scalable face detector (ASFD) family. In particular, our strong ASFD-D6 outperforms the best competitor with AP 96.7/96.2/92.1 on WIDER Face test, and the lightweight ASFD-D0 costs about 3.1 ms, more than 320 FPS, on the V100 GPU with VGA-resolution images.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

IFRNet: Intermediate Feature Refine Network for Efficient Frame Interpolation

Prevailing video frame interpolation algorithms, that generate the intermediate frames from consecutive inputs, typically rely on complex model architectures with heavy parameters or large delay, hindering them from diverse real-time applications. In this work, we devise an efficient encoder-decoder based network, termed IFRNet, for fast intermediate frame synthesizing. It first extracts pyramid features from given inputs, and then refines the bilateral intermediate flow fields together with a powerful intermediate feature until generating the desired output. The gradually refined intermediate feature can not only facilitate intermediate flow estimation, but also compensate for contextual details, making IFRNet do not need additional synthesis or refinement module. To fully release its potential, we further propose a novel task-oriented optical flow distillation loss to focus on learning the useful teacher knowledge towards frame synthesizing. Meanwhile, a new geometry consistency regularization term is imposed on the gradually refined intermediate features to keep better structure layout. Experiments on various benchmarks demonstrate the excellent performance and fast inference speed of proposed approaches. Code is available at https://github.com/ltkong218/IFRNet.

0 citations0 reviewsNo code linkedOpen access
preprint2019arXiv

Physical properties and device applications of graphene oxide

Graphene oxide (GO), the functionalized graphene with oxygenated groups (mainly epoxy and hydroxyl), has attracted resurgent interests in the past decade owing to its large surface area, superior physical and chemical properties, and easy composition with other materials via surface functional groups. Usually, GO is used as an important raw material for mass production of graphene via reduction. However, under different conditions, the coverage, types, and arrangements of oxygen-containing groups in GO can be varied, which give rise to excellent and controllable physical properties, such as tunable electronic and mechanical properties depending closely on oxidation degree, suppressed thermal conductivity, optical transparency and fluorescence, and nonlinear optical properties. Based on these outstanding properties, many electronic, optical, optoelectronic, and thermoelectric devices with high performance can be achieved on the basis of GO. Here we present a comprehensive review on recent progress of GO, focusing on the atomic structures, fundamental physical properties, and related device applications, including transparent and flexible conductors, field-effect transistors, electrical and optical sensors, fluorescence quenchers, optical limiters and absorbers, surface enhanced Raman scattering detectors, solar cells, light-emitting diodes, and thermal rectifiers.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Enhancement of interfacial thermal conductance of SiC by overlapped carbon nanotubes and intertube atoms

We proposed a new way, adding intertube atoms, to enhance interfacial thermal conductance (ITC) between SiC-carbon nanotube (CNT) array structure. Non-equilibrium molecular dynamics method was used to study the ITC. The results show that the intertube atoms can significantly enhance the ITC. The dependence of ITC on both the temperature and the number of intertube atoms are shown. The mechanism is analyzed by calculating probability distributions of atomic forces and vibrational density of states. Our study may provide some guidance on enhancing the ITC of CNT-based composites.

0 citations0 reviewsNo code linkedOpen access
preprint2016arXiv

Enhancing thermal conductivity of bulk polyethylene along two directions by paved crosswise laminate

Recently, some reports show that the ultra-low thermal conductivity of bulk polymers can be enhanced along one direction, which limits its applications. Here, we proposed paved crosswise laminate methods which can enhance the thermal conductivity of bulk polyethylene (PE) along two directions. We find that the thermal conductivity of paved crosswise polyethylene laminate (PPEL) reaches as high as 181 W/m-K along two in-plane directions, which is three orders of magnitude larger than bulk amorphous polyethylene and even more than two times larger than PE single chain (54 W/m-K). The analyses of mechanism indicated that PPEL is a much more crystal-like structure due to the inter-chain van der Waals interactions. Our study may provide guides on the design and fabrication of polymer structures with high thermal conductivity.

0 citations0 reviewsNo code linkedOpen access
preprint2015arXiv

Enhancing thermoelectric figure-of-merit by low-dimensional electrical transport in phonon-glass crystals

Low-dimensional electronic and glassy phononic transport are two important ingredients of highly-efficient thermoelectric material, from which two branches of the thermoelectric research emerge. One focuses on controlling electronic transport in the low dimension, while the other on multiscale phonon engineering in the bulk. Recent work has benefited much from combining these two approaches, e.g., phonon engineering in low-dimensional materials. Here, we propose to employ the low-dimensional electronic structure in bulk phonon-glass crystal as an alternative way to increase the thermoelectric efficiency. Through first-principles electronic structure calculation and classical molecular dynamics simulation, we show that the $π$-$π$ stacking Bis-Dithienothiophene molecular crystal is a natural candidate for such an approach. This is determined by the nature of its chemical bonding. Without any optimization of the material parameter, we obtain a maximum room-temperature figure of merit, $ZT$, of $1.48$ at optimal doping, thus validating our idea.

0 citations0 reviewsNo code linkedOpen access