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期刊文章
可靠的生成式人工智能
The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning

Zheng, T., Chen, Y., Li, C., Li, C., Zong, Q., Shi, H., Xu, B., Song, Y., Wong, G. Y. & See, S., 2025, The Curse of CoT: On the Limitations of Chain-of-Thought in In-Context Learning. Transactions on Machine Learning Research. vol. 2025-November.

期刊文章
可靠的生成式人工智能
Threat-Aware UAV Dodging of Human-Thrown Projectiles with an RGB-D Camera

Zhang, Y., Fan, N., Zheng, H., Liang, J., Pan, Z., Chen, Q. & Lyu, X., 2025, Threat-aware UAV Dodging of Human-Thrown Projectiles with an RGB-D Camera. IEEE Robotics and Automation Letters. vol. 11, no. 2, p. 1178–1185, article no. 11283030.

期刊文章
可持續發展的模型
PAL: Boosting Skin Lesion Segmentation via Probabilistic Attribute Learning

Yuan, Y., Wang, X., Li, J., Chen, G. & Heng, P. A., 2025, PAL: Boosting Skin Lesion Segmentation via Probabilistic Attribute Learning. IEEE Transactions on Medical Imaging. vol. 44, no. 12, p. 5183–5196, article no. 11078393.

期刊文章
可持續發展的模型
Temporal-multimodal consistency alignment for Alzheimer’s cognitive assessment prediction

Yang, X., Dang, X., Cai, J., Li, J., Wang, X. & Heng, P. A., 2025, Temporal-Multimodal Consistency Alignment for Alzheimer's Cognitive Assessment Prediction. Medical Physics. vol. 52, no. 6, p. 5064–5080.

學術論文
人類與機器人互動
“AI Afterlife” as Digital Legacy: Perceptions, Expectations, and Concerns

Lei, Y., Ma, S., Sun, Y. & Ma, X., 2025, "AI Afterlife" as Digital Legacy: Perceptions, Expectations, and Concerns. CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), p. 1–18 (article no. 981).

學術論文
人類與機器人互動
Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission Interviews

Liu, D., Zhang, Y., Zhao, B., Ma, S., Shi, C. & Ma, X., 2025, Scaffolded Turns and Logical Conversations: Designing Humanized LLM-Powered Conversational Agents for Hospital Admission Interviews. CHI 2025 - Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery (ACM), p. 1–23 (article no. 643).

學術論文
機器人與人工智能的融合
A Data-Driven Velocity Estimator for Autonomous Underwater Vehicles Experiencing Unmeasurable Flow and Wave Disturbance

Cai, J., Mayberry, S., Yin, H. & Zhang, F., 2025, A Data-Driven Velocity Estimator for Autonomous Underwater Vehicles Experiencing Unmeasurable Flow and Wave Disturbance. 2025 IEEE International Conference on Robotics and Automation, ICRA 2025. Institute of Electrical and Electronics Engineers (IEEE), p. 4138–4144.

學術論文
機器人與人工智能的融合
BEINGS: Bayesian Embodied Image-Goal Navigation with Gaussian Splatting

Meng, W., Wu, T., Yin, H. & Zhang, F., 2025, BEINGS: Bayesian Embodied Image-Goal Navigation with Gaussian Splatting. 2025 IEEE International Conference on Robotics and Automation, ICRA 2025. Institute of Electrical and Electronics Engineers (IEEE), p. 5252–5258.

學術論文
機器人與人工智能的融合
Design of a Formation Control System to Assist Human Operators in Flying a Swarm of Robotic Blimps

Wu, T., Fu, J., Meng, W., Cho, S., Zhan, H. & Zhang, F., 2025, Design of a Formation Control System to Assist Human Operators in Flying a Swarm of Robotic Blimps. 2025 IEEE International Conference on Robotics and Automation, ICRA 2025. Institute of Electrical and Electronics Engineers (IEEE), p. 8929–8935 (article no. 11128354).

學術論文
機器人與人工智能的融合
Design of a Gesture-Controlled Multi-Blimp System

Lyu, M., Wu, T. & Zhang, F., 2025, Design of a Gesture-Controlled Multi-Blimp System. Proceedings - 2025 10th International Conference on Automation, Control and Robotics Engineering, CACRE 2025. Institute of Electrical and Electronics Engineers (IEEE), p. 65–71 (article no. 11119595).

學術論文
機器人與人工智能的融合
QuietBlimp: A Human-Friendly Miniature Autonomous Noise-Mild Blimp for Indoor Environment

Wu, T., Lyu, M., Zhao, Y., Fu, J., Yang, Y., Tao, Q. & Zhang, F., 2025, QuietBlimp: A Human-Friendly Miniature Autonomous Noise-Mild Blimp for Indoor Environment. 2025 IEEE/ASME International Conference on Advanced Intelligent Mechatronics, AIM 2025. Institute of Electrical and Electronics Engineers (IEEE), article no. 11175635.

學術論文
可靠的生成式人工智能
COMPARISONQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty

Zong, Q., Wang, Z., Ren, X., Zheng, T. & Song, Y., 2025, ComparisonQA: Evaluating Factuality Robustness of LLMs Through Knowledge Frequency Control and Uncertainty. Findings of the Association for Computational Linguistics: ACL 2025. Association for Computational Linguistics (ACL), p. 4101–4117.

學術論文
可靠的生成式人工智能
CONKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning

Zhang, L., Wang, W., Fang, T. & Song, Y., 2025, ConKE: Conceptualization-Augmented Knowledge Editing in Large Language Models for Commonsense Reasoning. Findings of the Association for Computational Linguistics, ACL 2025. Association for Computational Linguistics (ACL), p. 627–635.

學術論文
可靠的生成式人工智能
ECOMSCRIPTBENCH: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association

Wang, W., Cui, L., Liu, X., Nag, S., Xu, W., Luo, C., Sarwar, S. M., Li, Y., Gu, H., Liu, H., Yu, C., Bai, J., Gao, Y., Zhang, H., He, Q., Ji, S. & Song, Y., 2025, EcomScriptBench: A Multi-task Benchmark for E-commerce Script Planning via Step-wise Intention-Driven Product Association. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2025. Association for Computational Linguistics (ACL), p. 1–22.

學術論文
可靠的生成式人工智能
KNOWSHIFTQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education?

Zheng, T., Li, W., Bai, J., Wang, W. & Song, Y., 2025, KnowShiftQA: How Robust are RAG Systems when Textbook Knowledge Shifts in K-12 Education? Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2025. Association for Computational Linguistics (ACL), p. 183–195.

學術論文
可靠的生成式人工智能
MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset

Wang, W. & Song, Y., 2025, MARS: Benchmarking the Metaphysical Reasoning Abilities of Language Models with a Multi-task Evaluation Dataset. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2025. Association for Computational Linguistics (ACL), p. 1568–1596.

學術論文
可靠的生成式人工智能
PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance

Li, H., Hu, W., Jing, H., Chen, Y., Hu, Q., Han, S., Chu, T., Hu, P. & Song, Y., 2025, PrivaCI-Bench: Evaluating Privacy with Contextual Integrity and Legal Compliance. Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics (ACL), p. 10544–10559.

學術論文
可靠的生成式人工智能
Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models’ Uncertainty?

Liu, J., Zong, Q., Wang, W. & Song, Y., 2025, Revisiting Epistemic Markers in Confidence Estimation: Can Markers Accurately Reflect Large Language Models' Uncertainty? Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2025. Association for Computational Linguistics (ACL), p. 206–221.

學術論文
以人為本為基準
Large Language Model Tokens are Psychologically Salient

Haslett, D. A., Chan, A. B. & Hsiao, J. H.-W., 2025, Large Language Model Tokens are Psychologically Salient. Paper presented at The 47th Annual Meeting of the Cognitive Science Society (COGSCI2025), San Francisco, United States. p. 4819.

學術論文
以人為本為基準
Whose Values Prevail? Bias in Large Language Model Value Alignment

Qi, R., Papyshev, G., Tsai, K. S., Chan, A. B. & Hsiao, J. H.-W., 2025, Whose Values Prevail? Bias in Large Language Model Value Alignment. Paper presented at 47th Annual Meeting of the Cognitive Science Society, San Francisco, United States. p. 665–672.

學術論文
可靠的生成式人工智能
Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning

Hu, W., Li, H., Jing, H., Hu, Q., Zeng, Z., Han, S., Xu, H., Chu, T., Hu, P. & Song, Y., 2025, Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), p. 865–883.

學術論文
可靠的生成式人工智能
INTEGROUND: On the Evaluation of Verification and Retrieval Planning in Integrative Grounding

Cheng, J., Zhuang, Q., Li, H., Chan, C., Liu, X., Qiu, L. & Song, Y., 2025, InteGround: on the Evaluation of Verification and Retrieval Planning in Integrative Grounding. EMNLP 2025 - 2025 Conference on Empirical Methods in Natural Language Processing, Findings of EMNLP 2025. Association for Computational Linguistics (ACL), p. 13587–13602.

學術論文
可靠的生成式人工智能
LOGIDYNAMICS: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning

Zheng, T., Cheng, J., Li, C., Shi, H., Wang, Z., Bai, J., Song, Y., Wong, G. Y. & See, S., 2025, LogiDynamics: Unraveling the Dynamics of Inductive, Abductive and Deductive Logical Inferences in LLM Reasoning. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), p. 20710–20731.

學術論文
可靠的生成式人工智能
MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol

Jing, H., Li, H., Hu, W., Hu, Q., Xu, H., Chu, T., Hu, P. & Song, Y., 2025, MCIP: Protecting MCP Safety via Model Contextual Integrity Protocol. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics (ACL), p. 1177–1194.

學術論文
可持續發展的模型
GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling

Chen, T., Xu, X., Liu, Z., Li, P., Song, X., Jaiswal, A. K., Zhang, F., Hu, J., Wang, Y., Chen, H., Diao, S., Liu, S., Li, Y., Yin, L. & Yang, C., 2025, GPAS: Accelerating Convergence of LLM Pretraining via Gradient-Preserving Activation Scaling. Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS 2025). article no. 21177.

學術論文
可靠的生成式人工智能
MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly

Wang, Z., Yu, W., Ren, X., Zhang, J., Zhao, Y., Saxena, R., Cheng, L., Wong, G., See, S., Minervini, P., Song, Y. & Steedman, M., 2025, MMLongBench: Benchmarking Long-Context Vision-Language Models Effectively and Thoroughly. Paper presented at The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS 2025), California, United States.

學術論文
可靠的生成式人工智能
ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding

Wang, H., Lu, J., Li, H. & Li, X., 2025, ZEBRA: Towards Zero-Shot Cross-Subject Generalization for Universal Brain Visual Decoding. Proceedings of the 39th Conference on Neural Information Processing Systems (NeurIPS 2025). 2025.

學術論文
以人為本為基準
Made in China, thinking in America: U.S. Values Persist in Chinese LLMs

Haslett, D., Huang, L. T.-L., Khalatbari, L., Hsiao, J. H.-W. & Chan, A. B., 2025, Made-in China, Thinking in America: U.S. Values Persist in Chinese LLMs. CogSci Asia–Pacific Meetup 2025.