Personality assessment aims to infer stable personality traits from dynamic behaviors across language, voice, and facial cues. Since different personality dimensions are revealed through distinct behavioral perspectives, modeling trait-specific evidence is challenging. However, most existing approaches adopt a uniform multimodal fusion strategy across all dimensions, assuming identical modality contributions. This overlooks trait-specific modality preferences and introduces cross-modal interference. To address this issue, we propose a novel personality assessment framework called Traits Run Deeper, which consists of three components. Specifically, the Multimodal Foundation Representation (MFR) module constructs personality-oriented multimodal inputs and leverages psychology-informed semantic templates as anchors, enabling foundation models to capture trait-relevant information. Building upon MFR, the Trait-Specific Modality Fusion (TSMF) module acts as an asymmetric fusion mechanism, allowing each dimension to selectively exploit different modality pathways from modality-specific modeling to complementary fusion. Thus, TSMF captures heterogeneous modality preferences while reducing cross-modal contamination. Furthermore, the Distribution-Calibrated Personality Regression (DCPR) module mitigates label imbalance and central tendency bias through target distribution calibration, improving robustness and stability. Experimental results on the AVI Challenge 2026 validation set demonstrate the effectiveness of the proposed framework, reducing mean squared error (MSE) by approximately 25% compared with the baseline. Consistent improvements are observed on the official test set, where our method achieves the best performance and ranks first in the Personality Assessment Track. The source code will be made available at https://github.com/MSA-LMC/AVI2026.

翻译：人格评估旨在从语言、语音及面部表情等动态行为线索中推断稳定的性格特质。由于不同人格维度通过差异化的行为视角展现，建模特质特异性证据具有挑战性。然而现有方法大多对所有维度采用统一的多模态融合策略，假设各模态贡献相同，这忽视了特质特异性的模态偏好并引发跨模态干扰。为解决该问题，我们提出名为“特质深掘”（Traits Run Deeper）的新型人格评估框架，包含三个核心组件：首先，多模态基础表征（MFR）模块构建面向人格的多模态输入，并利用心理学启发的语义模板作为锚点，使基础模型捕获特质相关信息。基于MFR，特质特异性模态融合（TSMF）模块作为非对称融合机制，允许各维度从模态特异性建模到互补融合选择性利用不同模态路径，从而捕捉异构模态偏好并减少跨模态污染。此外，分布校准人格回归（DCPR）模块通过目标分布校准缓解标签失衡与趋中偏差，提升鲁棒性与稳定性。在AVI挑战赛2026验证集上的实验结果表明，与基线相比，所提框架能将均方误差（MSE）降低约25%。在官方测试集上，该方法实现持续改进并获人格评估赛道第一名。源代码将发布于https://github.com/MSA-LMC/AVI2026。