Selected work

Representative Papers

Rydberg Atomic Quantum Receivers for Classical Wireless Communications and Sensing: Their Models and Performance

Dec 07, 2024arXiv.org

The absence of a rigorous system-level model and quantitative performance characterization for Rydberg atom quantum receivers (RAQRs) impedes their integration into classical wireless communication and sensing systems. Method: This paper establishes, for the first time, an end-to-end RAQR reception scheme and its equivalent baseband signal model, introducing a unified analytical framework that jointly incorporates quantum sensing, optical detection, and RF modeling. Contribution/Results: We derive fundamental theoretical gain bounds for RAQRs relative to classical RF receivers, demonstrating ≥27 dB and ≥40 dB improvements in received signal-to-noise ratio (SNR) under photon shot-noise-limited and standard quantum-limited conditions, respectively. These results fill critical gaps in RAQR system modeling, performance quantification, and design guidance for classical infrastructure. The work provides a verifiable theoretical foundation and actionable engineering pathway toward quantum-enhanced wireless technologies.

4 citationsRead paper

Benchmarking Cross-Domain Audio-Visual Deception Detection

May 11, 2024arXiv.org

Current audio-visual spoofing detection methods suffer from poor cross-scenario generalization and lack a standardized cross-domain evaluation benchmark. To address this, we introduce the first unified, standardized cross-domain benchmark for audio-visual spoofing detection, supporting both single-source-to-single-target and multi-source-to-single-target domain adaptation settings. We propose MM-IDGM, a gradient-coordinated optimization algorithm, and Attention-Mixer, a novel multimodal fusion architecture. Additionally, we design three novel multi-source domain sampling strategies and integrate OpenSMILE/ResNet-50 feature extractors with CNN/RNN/Transformer backbones. Extensive experiments demonstrate that our approach achieves an average accuracy improvement of 5.2% under the multi-source-to-single-target setting, significantly enhancing cross-domain generalization. The benchmark and methodology provide a reproducible, comparable, and realistic evaluation framework for practical deployment.

2 citationsRead paper

Camera Calibration Using Inaccurate and Asynchronous Discrete GPS Trajectory from Drones

Aug 26, 2026

本文通过估计GPS高度偏差、时间偏移及相机角度偏差，使用迭代最小二乘算法解决基于不准确且异步的无人机GPS轨迹的静止相机校准问题。

0 citationsRead paper

Security Games on Series-Parallel Attack Graphs with Adaptive Attackers

Aug 21, 2026

研究了在串联并联攻击图上，通过使用扩展的Gittins指数策略来对抗自适应攻击者的问题，提出了一种有效计算防御资源分配的方法。

0 citationsRead paper

Native Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages

Aug 01, 2026

This work addresses the issue of cross-lingual collapse in large language models when performing complex reasoning in low-resource Southeast Asian languages, where intermediate reasoning steps often regress to English, undermining localized reasoning capabilities. To mitigate this, the authors propose Onramp-Sequence Cross-Distillation (OSCD), a post-training algorithm that iteratively projects high-resource language reasoning trajectories into low-resource language subspaces via translation agents. OSCD further incorporates a joint embedding mechanism that semantically aligns reasoning trajectories between reference and target languages, enabling stable native chain-of-thought reasoning. The method is the first to prevent cross-lingual collapse in low-resource settings, achieving up to a 3.2× improvement in mathematical reasoning performance on the AIME25 and HMMT25 benchmarks. Additionally, the semantic alignment component yields up to a 6.4% reduction in language bias compared to pure translation baselines.

0 citationsRead paper

Recent publications

Latest Papers

Camera Calibration Using Inaccurate and Asynchronous Discrete GPS Trajectory from Drones

Aug 26, 2026

本文通过估计GPS高度偏差、时间偏移及相机角度偏差，使用迭代最小二乘算法解决基于不准确且异步的无人机GPS轨迹的静止相机校准问题。

0 citationsRead paper

Security Games on Series-Parallel Attack Graphs with Adaptive Attackers

Aug 21, 2026

研究了在串联并联攻击图上，通过使用扩展的Gittins指数策略来对抗自适应攻击者的问题，提出了一种有效计算防御资源分配的方法。

0 citationsRead paper

Native Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages

Aug 01, 2026

This work addresses the issue of cross-lingual collapse in large language models when performing complex reasoning in low-resource Southeast Asian languages, where intermediate reasoning steps often regress to English, undermining localized reasoning capabilities. To mitigate this, the authors propose Onramp-Sequence Cross-Distillation (OSCD), a post-training algorithm that iteratively projects high-resource language reasoning trajectories into low-resource language subspaces via translation agents. OSCD further incorporates a joint embedding mechanism that semantically aligns reasoning trajectories between reference and target languages, enabling stable native chain-of-thought reasoning. The method is the first to prevent cross-lingual collapse in low-resource settings, achieving up to a 3.2× improvement in mathematical reasoning performance on the AIME25 and HMMT25 benchmarks. Additionally, the semantic alignment component yields up to a 6.4% reduction in language bias compared to pure translation baselines.

0 citationsRead paper

Tokenizer-Agnostic Engram Module

Jul 31, 2026

This work addresses the limited reusability of embeddings in the Deepseek Engram module, which stems from its reliance on tokenizer-specific tokenization. To overcome this limitation, the authors propose a tokenizer-agnostic embedding approach that treats N-grams as byte sequences and replaces the original XOR-based hashing with a universal polynomial hash function. This ensures consistent hash representations for byte-equivalent sequences across different tokenizers. By constructing a shared joint embedding space across tokenizers and integrating a conditional memory mechanism, the method substantially enhances the transferability and reusability of the Engram module while maintaining comparable model performance.

0 citationsRead paper

Formal Mechanisms for Market Stability in Self-Interested Agent Societies: A Marketplace Simulation Study

Jul 09, 2026

This study addresses the fragility of cooperation among self-interested agents in unconstrained environments, which can precipitate market collapse and threaten systemic stability. The authors construct a multi-agent market simulation comprising 18 DeepSeek-V3 agents and integrate open communication with formal mechanism design to propose and empirically validate a “mediation” mechanism as pivotal for sustaining equilibrium. Leveraging LLM-based simulation, adversarial red-teaming, iterative prompt-optimization attacks, and a utility-exchange model under social network constraints, they formally define and quantify the mechanism’s adversarial robustness under optimal attacks—the first such formalization in this domain. Experimental results demonstrate that even under sustained, high-intensity adversarial pressure, the market remains intact, with honest agents experiencing only a 13.3% utility loss, thereby exhibiting a “bend-but-not-break” resilience.

0 citationsRead paper