Qimin Zhong, Hao Liao, Haiming Qin, Mingyang Zhou, Rui Mao, Wei Chen, Naipeng Chao. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Role-playing capabilities in large language models (LLMs) often lack cognitive consistency in complex scenarios that require deep understanding and coherent reasoning.While recent reasoning models excel in math and coding tasks, they show limited effectiveness in open-ended role-playing scenarios.We introduce R-CHAR (Role-Consistent Hierarchical Adaptive Reasoning), a metacognition-driven framework that enhances role-playing performance through guided thinking trajectories synthesis and adaptive evaluation.Our approach demonstrates that concise thinking processes can achieve superior performance efficiently compared to elaborate reasoning chains in roleplaying social intelligence tasks, outperforming existing specialized models.Experimental results on the SocialBench benchmark show significant and stable performance improvements across varying scenario complexities, showing particular strength in long-context comprehension (from 34.64% to 68.59%) and grouplevel social interactions.Our work advances the development of cognitively consistent roleplaying systems, bridging the gap between surface-level mimicry and authentic character simulation.

The fraction of photosynthetically active radiation (FPAR) is a key parameter in controlling mass and energy exchanges between vegetation and atmosphere. LiDAR data-derived canopy vertical structural information and hyperspectral image-derived vegetation spectral information can be considered as complementary for vegetation FPAR estimation. To the best of our knowledge, few studies have estimated vegetation FPAR by both LiDAR and hyperspectral data based on physical models. This letter aims to explore the ability of combining airborne LiDAR and hyperspectral data to retrieve maize FPAR based on the energy budget balance principle. First, canopy gap probability and openness were estimated from airborne LiDAR data. Next, canopy reflectance and soil background reflectance were retrieved from hyperspectral image. Then, we estimated maize FPAR based on the energy budget balance principle. Finally, model validity was assessed byin situdata and results showed the physical FPAR estimation model estimated maize FPAR accurately. These results indicated that the physical method proposed in this letter was efficient and reliable to estimate maize FPAR, and FPAR retrieval can benefit from the complementary nature of LiDAR-captured canopy structural information and hyperspectral-detected vegetation spectral characteristics.