Introduces Tailor, a warm-start strategy that initializes RL fine-tuning of LLM reasoners from a tailored set of reasoning primitives. I built the embedding-similarity pipeline used for the paper’s diversity analysis, showing that Tailor’s warm-start data is more reasoning-diverse than Standard-CoT and 4-STaR baselines.
@inproceedings{yao2026tailored,title={Tailored primitive initialization is the secret key to reinforcement learning},author={Yao, Y. and Zeng, G. and Wu, R. and Zhang, Y. and Zhao, D. and Hong, Z.-W. and Gan, C.},booktitle={Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},pages={33300--33318},address={San Diego, CA},year={2026},doi={10.18653/v1/2026.acl-long.1537},}
Combines path-based link prediction with influence-maximization heuristics to identify future vital nodes in weighted collaboration networks, evaluated on arXiv co-authorship data. Introduces the RA-2 and quasi-local RA-2 similarity metrics. Authors are listed alphabetically.
@article{lin2026influence,title={Influence prediction in collaboration networks: An empirical study on {arXiv}},author={Lin, M. and Schaposnik, L. P. and Wu, R.},journal={Physica A: Statistical Mechanics and its Applications},volume={689},pages={131451},year={2026},doi={10.1016/j.physa.2026.131451},}
AI agents in engineering design: A multi-agent framework for aesthetic and aerodynamic car design
M. Elrefaie, J. Qian, R. Wu, and 3 more authors
In Proceedings of the ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), Volume 3B: 51st Design Automation Conference, 2025
A multi-agent framework in which LLM-driven agents collaborate on aesthetic and aerodynamic car design. I built the sketch-to-3D retrieval pipeline (2D sketch to nearest 3D car mesh) and benchmarked retrieval models and similarity metrics.
@inproceedings{elrefaie2025agents,title={{AI} agents in engineering design: A multi-agent framework for aesthetic and aerodynamic car design},author={Elrefaie, M. and Qian, J. and Wu, R. and Chen, Q. and Dai, A. and Ahmed, F.},booktitle={Proceedings of the ASME 2025 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (IDETC-CIE), Volume 3B: 51st Design Automation Conference},pages={V03BT03A048},address={Anaheim, CA},year={2025},doi={10.1115/DETC2025-169682},}
A heuristic model for predicting the future influence of nodes in evolving networks, developed as part of MIT PRIMES-USA. Authors are listed alphabetically.
@misc{lin2024social,title={The social sphere model: Heuristic influence prediction in evolving networks},author={Lin, M. and Schaposnik, L. P. and Wu, R.},journal={arXiv preprint},year={2024},}