CORTEXA
← Browse
arxivcs.CLcs.AIcs.LG2026-07-23

The Geometry of Personality: Activation Steering with Jungian Cognitive Functions

Liu Zai, Yumeng Wang, Junchen Fu, Joemon M. Jose

Activation steering enables control and interpretation of LLMs, yet existing work primarily models personality through static trait frameworks such as the Big Five. We investigate whether personality can instead be represented and controlled as a set of cognitive processes using the eight Jungian Cognitive Functions. To this end, we introduce a framework comprising a Jungian evaluation protocol and a dataset of over 2,100 role-playing character narrations. Activation steering vector extraction and evaluation experiments on Llama-3.1-8B demonstrate effective monotonic control over all eight cognitive functions through activation steering. Beyond controllability, our analysis reveals that: 1. personality information is concentrated in middle transformer layers; 2. steering vectors exhibit structured geometric relationships consistent with distinctions between rational and irrational functions; 3. effective multi-dimensional steering directions cannot be recovered as linear combinations of single-function directions. These findings provide new insights into the representation of personality in LLM activation space and establish a framework for studying interpretable, effective, and multi-dimensional personality control.

View free PDFSource page

Related papers

arxivcs.AIcs.CLcs.LG2026-07-09

A First-Principles Theory of Slow Thinking and Active Perception

Hongkang Yang, Zhi-Qin John Xu, Feiyu Xiong, Weinan E

As part of a series on first-principles modeling of cognitive functions, this paper attempts to provide a mathematical formulation of thinking and perception. It formally derives slow thinking or more generally, active perception, and encompasses the design, training and inferenc…

View free PDFSource page
arxivcs.LGcs.AIcs.CL2026-07-13

Inside the Unfair Judge: A Mechanistic Interpretability Account of LLM-as-Judge Bias

Zixiang Xu, Sixian Li, Huaxing Liu, Xiang Wang, Shuai Li, Zirui Song, et al.

Existing studies of LLM-as-judge scoring bias work predominantly at the input-output level: they perturb inputs, measure score deltas, and propose prompt-level mitigations. We argue that the same biases admit a representation-level account in the judge's hidden state, complementa…

View free PDFSource page
arxivcs.LGcond-mat.mtrl-scics.AIcs.CL2026-06-28

Interpretable Inverse Design of Metal-Organic Frameworks with Large Language Model Agents

Kyungmin Nam, Seunghee Han, Jihan Kim

Inverse design of metal-organic frameworks (MOFs) requires searching a combinatorially vast space where property labels are expensive and most machine-learning models reveal little about why a structure succeeds. We introduce LLM4MOF, a closed-loop framework in which language-mod…

View free PDFSource page
arxivcs.CVcs.AIcs.CLcs.LG2026-07-17

An Exam for Active Observers

Jiarui Zhang, Muzi Tao, Shangshang Wang, Ollie Liu, Xuezhe Ma, Willie Neiswanger

Human vision is a closed loop: gaze is continuously redirected by intermediate hypotheses rather than a single snapshot. Decades of psychophysics and cognitive science have argued that this active observation is essential for a wide range of tasks. Whether today's multimodal larg…

View free PDFSource page
arxivcs.CLcs.AIcs.CYcs.LG2026-06-26

Cognitive Episodes in LLM Reasoning Traces Enable Interpretable Human Item Difficulty Prediction

Chenguang Wang, Ming Li, Xinyue Zeng, Zhuochun Li, Hong Jiao, Tianyi Zhou, et al.

Predicting human item difficulty is central to educational assessment, where reliable estimates support fairness and effective test construction. Existing methods often depend on costly human calibration or item-level textual representations, providing limited evidence about the…

View free PDFSource page