Cao, B., Zhou, H., Yang, R., Li, J., Sun, S., Song, Y., Gao, H., Yu, Z., Zhang, T.-Y., & Wang, J. (2026). A self-learning scientific agent for X-ray diffraction. arXiv preprint arXiv:2610.07862.
Zeng, Y., Cui, X., Song, Y., Liu, G., Xin, H., Zhang, K., Deng, C., Zhan, K., Ying, J., Zhao, J., Zhang, H., & Wang, J. (2026). Information-Time Proximal Policy Optimization. arXiv preprint arXiv:2609.24380.
Yu, Z.*, Song, Y.*, Yan, X., Liu, A., Lu, X., Chen, Y., Zhou, H., Guo, S., Sun, L., Chen, S., Yu, X., & Wang, J. (2026). Large Discovery Models: Empirically-grounded Model-Based Open-Ended Search. arXiv preprint arXiv:2608.15669.
Song, Y., Feng, X., Liu, B., Cui, X., Liu, Z., Fu, H., Yang, M., Deng, C., Zhao, J., & Wang, J. (2026). Learning Stateful Predictive Knowledge From Experience. ICML 2026 Workshop on Agents, Interaction, and Intelligence in the Wild (AIWILD).
Liu, A., Song, Y., Chen, Z., Gong, Z., Yu, Z., & Wang, J. (2026). ToolGate: Token-Efficient Pre-Call Control for Tool-Augmented Vision-Language Agents. Conference on Empirical Methods in Natural Language Processing (EMNLP).
Liu, A., Gong, Z., Song, Y., Chen, Y., Liu, X., Lu, H., Zhang, K., Wei, C., & Wang, J. (2026). The Perceptual Bandwidth Bottleneck in Vision-Language Models: Active Visual Reasoning via Sequential Experimental Design. International Conference on Machine Learning (ICML).
Ma, W., Zeng, Y., Song, Y., Cui, X., Zhao, J., Liu, X., & Elhoseiny, M. (2026). Freshness-Aware Prioritized Experience Replay for LLM/VLM Reinforcement Learning. arXiv preprint arXiv:2604.16918.
Sun, L., Jiang, J., Ding, Y., Li, F., Song, Y., Zhang, H., Ying, J., Ren, L., Zhan, K., Chen, W., Xie, Y., & Deng, C. (2026). Hardware Co-Design Scaling Laws via Roofline Modelling for On-Device LLMs. Advances in Neural Information Processing Systems (NeurIPS). Accepted.
Yan, X., Ou, Z., Yang, M., Song, Y., Zhang, H., Li, Y., & Wang, J. (2025). Memory-Driven Self-Improvement for Decision Making with Large Language Models. arXiv preprint arXiv:2509.26340.
Wan, Z., LI, Y., Wen, X., Song, Y., Wang, H., Yang, L., Schmidt, M., Wang, J., Zhang, W., Hu, S., & Wen, Y. (2025). REMA: Learning to Meta-Think for LLMS with Multi-Agent Reinforcement Learning. Advances in Neural Information Processing Systems (NeurIPS).
Huang, S., Yang, L., Song, Y., Chen, S., Cui, L., Wan, Z., Zeng, Q., Wen, Y., Shao, K., Zhang, W., Wang, J., & Zhang, Y. (2025). ThinkBench: Dynamic Out-of-Distribution Evaluation for Robust LLM Reasoning. Advances in Neural Information Processing Systems (NeurIPS) Datasets & Benchmarks Track.
Yan, X., Song, Y., Cui, X., Christianos, F., Zhang, H., Mguni, D. H., & Wang, J. (2025). Ask more, know better: Reinforce-Learned Prompt Questions for Decision Making with Large Language Models. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD).
Yan, X., Song, Y., Feng, X., Yang, M., Zhang, H., Bou Ammar, H., & Wang, J. (2025). Efficient Reinforcement Learning with Large Language Model Priors. International Conference on Learning Representations (ICLR).
Feng, X., Liu, B., Song, Y., Fu, H., Wan, Z., Koushik, G. A., Hu, Z., Yang, M., Wen, Y., & Wang, J. (2024). Natural Language Reinforcement Learning. arXiv preprint arXiv:2411.14251.
Wang, J., Fang, M., Wan, Z., Wen, M., Zhu, J., Liu, A., Gong, Z., Song, Y., Chen, L., Ni, L. M., Yang, L., Wen, Y., & Zhang, W. (2024). OpenR: An Open Source Framework for Advanced Reasoning with Large Language Models. arXiv preprint arXiv:2410.09671.
Wang, C., Song, Y., Wu, S., Wu, S., Zhang, R., Lin, S., & Zhang, H. (2024). AI-Olympics: Exploring the Generalization of Agents through Open Competitions. Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI) Demo Track.
Song, Y., Jiang, H., Zhang, H., Tian, Z., Zhang, W., & Wang, J. (2024). Boosting Studies of Multi-Agent Reinforcement Learning on Google Research Football Environment: the Past, Present, and Future. Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
Mi, Q., Xia, S., Song, Y., Zhang, H., Zhu, S., & Wang, J. (2024). TaxAI: A Dynamic Economic Simulator and Benchmark for Multi-Agent Reinforcement Learning. Proceedings of the International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS).
Song, Y., Jiang, H., Tian, Z., Zhang, H., Zhang, Y., Zhu, J., Dai, Z., Zhang, W., & Wang, J. (2024). An empirical study on google research football multi-agent scenarios. Machine Intelligence Research, 21, 549–570.
Conference proceedings talk at Testing Institute of America 2014 Annual Conference, Los Angeles, CA
Talk at London School of Testing, London, UK
Tutorial at UC-Berkeley Institute for Testing Science, Berkeley CA, USA
Talk at UC San Francisco, Department of Testing, San Francisco, California