HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps
为解决人机交互中传统接口的延迟问题，提出HINT-Blimp框架，通过物理推动和语音命令等多模态信号直接传达意图，并用粒子滤波在线估计意图。
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Selected work
Representative Papers
See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game
通过赛车游戏互动活动，学生学习将主观知识转化为先验分布，并使用Beta-Binomial模型结合数据更新后验推断，从而教授贝叶斯统计。
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Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals
该研究通过结合大型语言模型和实证数据评分的神经符号框架，生成关于不良妊娠结局的合理因果假设，以解决数据稀缺和领域知识不完整的问题。
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One Adaptive Trailing Head Can Outperform Many Oblivious Trailing Heads
This study investigates the advantage of adaptive trailing heads over arbitrarily many non-adaptive trailing heads in sequence predictability within the framework of finite-state strong dimension. By integrating multi-head finite-state automata, finite-state dimension theory, and information-theoretic analysis, the authors construct a binary sequence for which the strong dimension under an adaptive two-head model is at least 0.3 lower than that under any non-adaptive multi-head model. This result demonstrates—by a substantial and consistent margin—that even a single adaptive trailing head can outperform any number of non-adaptive heads employing fixed strategies. The finding strengthens and extends existing dimension separation results, highlighting the fundamental superiority of adaptive strategies in finite-state prediction.
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ScamPilot: Simulating Conversations with LLMs to Protect Against Online Scams
This study addresses the growing challenge of evolving online scams, which outpace existing automated defense systems in equipping users to recognize novel fraud tactics. To bridge this gap, the authors propose a conversational anti-fraud training framework powered by large language models, featuring two interacting agents—one simulating a scammer and the other a potential victim—to dynamically recreate realistic scam scenarios. The approach integrates real-time user intervention with multiple-choice prompts, encouraging participants to provide actionable advice that reinforces fraud awareness. In a controlled experiment involving 150 participants, the method significantly improved scam identification accuracy by 8%, response effectiveness by 9%, and self-efficacy by 19%. Notably, users predominantly offered action-oriented recommendations without compromising trust in legitimate interactions.
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Recent publications
Latest Papers
HINT-Blimp: Human INTent Inference from Multimodal Cues for Robotic Blimps
为解决人机交互中传统接口的延迟问题，提出HINT-Blimp框架，通过物理推动和语音命令等多模态信号直接传达意图，并用粒子滤波在线估计意图。
0 citationsRead paper
See You at the Posterior Line: Learning Bayesian Modeling Through a Car Racing Game
通过赛车游戏互动活动，学生学习将主观知识转化为先验分布，并使用Beta-Binomial模型结合数据更新后验推断，从而教授贝叶斯统计。
0 citationsRead paper
Causal Modeling of Adverse Pregnancy Outcomes via Adaptive LLM Proposals
该研究通过结合大型语言模型和实证数据评分的神经符号框架，生成关于不良妊娠结局的合理因果假设，以解决数据稀缺和领域知识不完整的问题。
0 citationsRead paper
One Adaptive Trailing Head Can Outperform Many Oblivious Trailing Heads
This study investigates the advantage of adaptive trailing heads over arbitrarily many non-adaptive trailing heads in sequence predictability within the framework of finite-state strong dimension. By integrating multi-head finite-state automata, finite-state dimension theory, and information-theoretic analysis, the authors construct a binary sequence for which the strong dimension under an adaptive two-head model is at least 0.3 lower than that under any non-adaptive multi-head model. This result demonstrates—by a substantial and consistent margin—that even a single adaptive trailing head can outperform any number of non-adaptive heads employing fixed strategies. The finding strengthens and extends existing dimension separation results, highlighting the fundamental superiority of adaptive strategies in finite-state prediction.
0 citationsRead paper
ScamPilot: Simulating Conversations with LLMs to Protect Against Online Scams
This study addresses the growing challenge of evolving online scams, which outpace existing automated defense systems in equipping users to recognize novel fraud tactics. To bridge this gap, the authors propose a conversational anti-fraud training framework powered by large language models, featuring two interacting agents—one simulating a scammer and the other a potential victim—to dynamically recreate realistic scam scenarios. The approach integrates real-time user intervention with multiple-choice prompts, encouraging participants to provide actionable advice that reinforces fraud awareness. In a controlled experiment involving 150 participants, the method significantly improved scam identification accuracy by 8%, response effectiveness by 9%, and self-efficacy by 19%. Notably, users predominantly offered action-oriented recommendations without compromising trust in legitimate interactions.
0 citationsRead paper