Your LLM, Your Style: Behavioral Mode Axes for LLM Behavioral Control

2026-08-11

AI Summary

Current personality assessments of large language models (LLMs) predominantly rely on first-person self-report questionnaires, which are susceptible to prompt perturbations and lack behavioral grounding. This work proposes a behavior-data (B-data) framework grounded in contextualized scenarios, employing 3,200 contrastive behavioral situations to capture stable behavioral patterns of LLMs across diverse interaction contexts. It introduces the first behavior-mode axis (BMA) derived from chain-of-thought reasoning, enabling precise modulation of LLM behavioral styles within an activation space. Integrating established psychometric scales (BFI-2, DOSPERT, HEXACO) with behavioral trajectory analysis, the study demonstrates that LLMs exhibit model-specific, context-dependent yet stable behavioral profiles. Furthermore, it shows that the chain-of-thought–derived BMA significantly enhances both the stability and mechanistic fidelity of behavioral control compared to response-chain approaches.

Abstract

Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making. Existing LLM personality studies largely rely on self-report questionnaires administered in first-person settings, making the resulting profiles sensitive to surface elicitation choices and poorly grounded in concrete model behavior. In this work, we introduce a situated behavioral-data (B-data) framework for studying and controlling LLM behavioral personality. We construct 3,200 contrastive behavioral scenarios spanning 20 behavioral patterns and four prompt registers, grounded in validated psychometric facets such as BFI-2, DOSPERT, and HEXACO. Using this framework, we find that LLMs exhibit stable and model-specific behavioral profiles, while also revealing register-dependent shifts across first-person decisions, advice-giving, and task execution. We then show that these behavioral patterns can be controlled through Behavioral Mode Axes (BMAs), activation-space directions derived from contrastive behavioral traces. Compared with response-derived BMAs, which are more prone to trait drift, thought-derived BMAs more faithfully capture the intended behavioral mechanism and provide cleaner control over situated behavioral styles. Our results suggest that LLM personality-like tendencies are better understood not as abstract self-report traits, but as measurable and controllable behavioral modes grounded in concrete interaction contexts. Our code and data are available at https://github.com/lhz191/LLM-Behavioral-Personality.

Problem

• behavioral control
• large language models
• personality assessment
• situated behavior
• behavioral modes

Innovation

• Behavioral Mode Axes
• situated behavioral data
• activation-space control
• LLM personality
• contrastive behavioral scenarios