The paper introduces Persona Dosing, a method that uses an activation‑steering coefficient to control the intensity of a language model’s persona traits. By conditioning a FLAS controller on trait descriptions and calibrating its flow time against measured trait expression, the approach can adjust trait intensity without requiring paired training data. Experiments on Llama‑3.1‑8B, Qwen3‑8B, and Gemma‑3‑4B show significant increases in core‑trait expression and low targeting errors across multiple traits.
arXiv:2607. 07916v1 Announce Type: new Abstract: Large language models exhibit recurring behavioural patterns -- personas -- that shape generalisation and safety, but we lack reliable tools for decomposing, measuring, and controlling them.
arXiv:2607. 13162v1 Announce Type: cross Abstract: What a language model will and will not do is largely set during post-training, but which behaviors it expresses, hides, or resists is not revealed by prompting alone.
The paper introduces a three-tier persona vector for user simulation in evaluating LLM agents, comprising 23 dimensions across demographics, behavioral traits, and emotional states, plus a query-complexity overlay. It demonstrates that these nuanced personas generate diverse, scenario-reactive conversations, leading to significant variations in agent goal achievement and compliance across different contexts. The model’s design allows for reproducible, auditable user behavior patterns without relying on learned covariance matrices.
arXiv:2601. 02896v3 Announce Type: replace Abstract: Controlling emergent behavioral personas (e.
arXiv:2604. 07102v2 Announce Type: replace-cross Abstract: Activation-based steering enables inference-time personalization of large language models, but its effects in educational applications are not well understood.
The paper introduces a three-tier persona vector to generate diverse, realistic user inputs for evaluating tool-augmented LLM agents. The vector includes 23 dimensions: categorical demographics, continuous behavioral traits, and continuous emotional states, plus a query-complexity overlay. Experiments on 64,698 conversations show that these persona dimensions produce measurable differences in agent performance and realistic scenario-reactive behavior.
arXiv:2609.39882v1 Announce Type: new Abstract: Pre-training equips large language models (LLMs) with a broad repertoire of behavioral patterns associated with roles, styles, values, and goals. Post-...
arXiv:2605. 21006v2 Announce Type: replace Abstract: We study the effect of different persona on \textbf{sycophancy}: model's agreement with users even when the user is incorrect.
arXiv:2608. 00023v2 Announce Type: replace-cross Abstract: Social simulations built from language-model agents need role-conditioned behavior that can be checked before agents are placed into a simulated population.
arXiv:2608. 10703v1 Announce Type: cross Abstract: Large language models (LLMs) increasingly act in interactive settings where their behavioral styles affect user experience, safety, and downstream decision making.
arXiv:2606.11269v2 Announce Type: replace Abstract: Personality assessment aims to infer stable traits from dynamic behaviors across modalities like language, voice, and facial expressions. Existing...
The study investigates how large language models (LLMs) encode persona-dependent preferences by training linear probes on residual-stream activations of Gemma‑3‑27B and Qwen‑3.5‑122B. It identifies a genuine preference vector that tracks the model’s task choices across various prompts and shows that steering along this vector can causally control pairwise choices. The research also finds that some preference information transfers between different personas, including a case where an evil persona’s preferences anti‑correlate with those of a helpful assistant.