研究

面向下一代智能发展需求，聚焦类脑大模型、类脑世界模型及类脑智能与生命科学交叉研究，致力于探索受脑启发的基座大模型、世界模型与机器人智能算法，并推动大模型赋能生命科学研究，涵盖基因致病分析、生命科学底层机制探索和脑疾病医学研究等方向。

(1) X. Li; Z. Zhou; K. Shen; W. Zhou; S. Guo*. Beyond Logits: Metastable Latent Dynamics for Sample-Efficient Best-of-N Selection in LLMs. Proceedings of the 43rd International Conference on Machine Learning (ICML), 2026.

(2) C. Gao; L. Li; Y. Zhou; S. Guo*. Complete-Tree Space Favors Data-Efficient Link Prediction. Proceedings of the 42nd International Conference on Machine Learning (ICML), Vancouver, Canada, 2025.

(3) Z. Yang; S. Guo*; Y. Fang; Z. Yu; J. K. Liu. Spiking Variational Policy Gradient for Brain Inspired Reinforcement Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2025, 47(3): 1975–1990.

(4) Y. Tang; S. Guo*; J. Liu; B. Wan; L. An; J. K. Liu. Hierarchical Reinforcement Learning from Imperfect Demonstrations through Reachable Coverage-Based Subgoal Filtering. Knowledge-Based Systems, 2024, 294(1).

(5) T. Zhang†; S. Guo†*; T. Tan; X. Hu; F. Chen*. Adjacency Constraint for Efficient Hierarchical Reinforcement Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023, 45(4): 4152–4166.

(6) S. Guo; Q. Yan; X. Su; X. Hu; F. Chen. State-Temporal Compression in Reinforcement Learning with the Reward-Restricted Geodesic Metric. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(9): 5572–5589.

† 共同第一作者；* 通讯作者