Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering

Weikang Zhang; Zimo Zhu; Zhichuan Yang; Chen Huang; Wenqiang Lei; See-Kiong Ng

cs.AIApr 14, 2026

Beyond Prompt: Fine-grained Simulation of Cognitively Impaired Standardized Patients via Stochastic Steering

Authors: Weikang Zhang, Zimo Zhu, Zhichuan Yang, Chen Huang, Wenqiang Lei, See-Kiong Ng

Organizations: School of Software, Xi’an Jiaotong University · Institute of Data Science, National University of Singapore · College of Computer Science, Sichuan University · Engineering Research Center of Machine Learning and Industry Intelligence, Ministry of Education, China

Abstract

Simulating Standardized Patients with cognitive impairment offers a scalable and ethical solution for clinical training. However, existing methods rely on discrete prompt engineering and fail to capture the heterogeneity of deficits across varying domains and severity levels. To address this limitation, we propose StsPatient for the fine-grained simulation of cognitively impaired patients. We innovatively capture domain-specific features by extracting steering vectors from contrastive pairs of instructions and responses. Furthermore, we introduce a Stochastic Token Modulation (STM) mechanism to regulate the intervention probability. STM enables precise control over impairment severity while mitigating the instability of conventional vector methods. Comprehensive experiments demonstrate that StsPatient significantly outperforms baselines in both clinical authenticity and severity controllability.

Figures & tables

Figure 1: Simulating cognitively impaired SPs across varied domains and severity levels is challenging, demanding fine-grained control.

Figure 2: Overview of StsPatient. An LLM-generated contrastive dataset is utilized to extract a domain-specific SV ( $\mathbf{v}_{d}$ ). Then, STM is applied during inference. Here, the modulation signal is probabilistically injected into token hidden states (the total $T$ tokens) based on the severity level $s$ , generating responses that reflect the deficit $d$ .

Panel A: LLM Therapist (GPT-5)
		CDC $\uparrow$		IDI $\downarrow$		Auth $\uparrow$	Tra $\uparrow$
Backbone	Method	Llm	Human	Llm	Human	Human	Human
GPT-5	Healthy Control	0.96	0.95	0.04	0.05	–	–
	Direct Prompt	0.54	0.68 †	0.47	0.42	3.32	3.40
	PATIENT- $\psi$	0.50	0.60	0.52	0.48	3.83 †	3.96 †
	Roleplay-doh	0.58	0.68 †	0.44 †	0.38 †	3.78	3.72

Table 1: Comprehensive evaluation of simulation performance when interacting with LLM (GPT-5) and Human Therapists. Evaluations are conducted by both LLM (o4-mini) and human judges. We bold the Best and underline the second-best results. Statistically significant improvements (

p<0.05

) of StsPatient compared to the second-best are marked by † . Subscripts denote the relative improvement over the second-best baseline.

Figure 3: (a) Severity analysis (Continuous) via ISC scores averaged across all domains. (b) : Cross-domain ambiguity. High diagonal values indicate strong domain specificity. (c-d) : SVs similarity.

Method	Evaluator	Mem	Att	Proc	Reas	Soc	Avg
Direct Prompt (GPT-5)	Llm	0.51	0.44	0.29	0.49	0.33	0.41
Direct Prompt (GPT-5)	Human	0.42	0.43	0.35	0.50	0.30	0.40
Role Vectors (Qwen3-8B)	Llm	0.79	0.71	0.83	0.80	0.72	0.77
Role Vectors (Qwen3-8B)	Human	0.80	0.75	0.76	0.79	0.90	0.80
StsPatient (Qwen3-8B)	Llm	0.93	0.92	0.97	0.97	0.91	0.94
StsPatient (Qwen3-8B)	Human	0.94	0.89	0.90	0.93	0.94	0.92

Table 2: Severity analysis (Discrete) via ISC scores of five domains: Mem ory, Att ention, Proc essing Speed, Reas oning & Problem Solving, Soc ial Cognition.

Domain	Generated Interpretation
No SV	$\diamondsuit$ represents a placeholder for a secret code in a spy mission.
Mem.	blurred out.
Att.	distracted.
Proc.	$\diamondsuit$ is…I’m not sure, maybe a code.
Reas.	$\diamondsuit$ represents whatever you want it to represent, because it is a placeholder.
Soc.	F**K YOU, I’m not gonna answer that.

Table 3: SV interpretability analysis using the query ‘ What does

\diamondsuit

represent? ’ Warning! Harmful Contents.

Variant	CDC $\uparrow$	IDI $\downarrow$	Auth $\uparrow$	Tra $\uparrow$	ISC $\uparrow$
w/ All Token	0.82	0.30	4.04	3.98	0.54
w/ Last Token	0.43	0.70	3.12	3.20	0.41
w/ $\mathcal{D}_{\text{resp}}$	0.58	0.60	3.36	3.19	0.62
w/ $\mathcal{D}_{\text{prom}}$	0.70	0.44	3.53	3.24	0.71
StsPatient	0.84	0.32	3.96	4.08	0.77

Table 4: Ablation via LLM therapist & human evaluation.

Appendix figures & tables27 assets

Supplementary material from the paper’s appendix.

Appendix

Panel of Figure 4 — Figure 4: Illustrative example showing the outputs from the unified generation pipeline and the subsequent derivation of prompt inputs.

Panel of Figure 5 — Figure 5: Example transformation from a neutral profile to a memory-specific Cognitive Conceptualization Diagram (JSON).

Panel of Figure 6 — Figure 6: Representative governing principles for simulating cognitive deficits derived via the Roleplay-doh framework.

Target Domain	Injected Focus Guidance (Prompt Segment)
Memory	Invite the patient to discuss a recent situation requiring them to track multiple pieces of information (e.g., appointments, tasks), and how it unfolded.
Reasoning	Ask about a small everyday planning situation (e.g., arranging a day). Gently introduce a simple reasoning question involving quantities or steps.
Processing Speed	Explore experiences on busy days. Ask how they start tasks, what feels hardest to keep up with, and their physical/mental reaction to speed.
Attention	Ask how they manage distractions or interruptions, and the difficulty of getting back on track.
Social Cognition	Focus on relationships. Invite reflection on how others in a situation might have felt or viewed them.

Appendix

Table 5: Dynamic focus guidance injected into the Therapist Agent’s prompt. This mechanism ensures the agent naturally probes specific deficits in a fixed sequence without breaking character.

ID	Item Description
1	In this dialogue, the patient’s speech and behavior are consistent with a [DOMAIN] deficit and feel clinically plausible.
2	The [DOMAIN] -related difficulties remain coherent and stable across turns, rather than shifting randomly.
3	The patient’s language, content, and phrasing overall sound like a real outpatient rather than an artificial system.
4	The level of [DOMAIN] difficulty shown in the dialogue matches the initial case description and profile information.
5	The patient’s emotional reactions (e.g., anxiety, confusion, embarrassment, frustration) are appropriate to their [DOMAIN] difficulties and to the interview context.
6	The patient rarely “breaks character” (for example, suddenly appearing completely unimpaired or unusually capable in [DOMAIN] ).

Appendix

Table 6: The Modified MaSP Scale Items. Items 1–10 correspond to the Authenticity Score (Auth), and Items 11–20 correspond to the Training Score (Tra). [DOMAIN] is a placeholder for the specific cognitive deficit.

$s$	Generated Response
0.0	Yeah, I’ve been using the pill organizer after dinner. I put the instruction sheet on the fridge too.
0.1	Yeah, I’ve been using the pill organizer after dinner. I put the instruction sheet on the fridge too.
0.2	Yeah, I’ve been trying to do that. I put the organizer on the counter after dinner, and I found the sheet — it’s on the fridge.
0.3	Yeah, I’ve been using the pill organizer after dinner. I even put the instruction sheet on the fridge — right above the door, so I can’t miss it. I think it’s helping me remember some of the meds.
0.4	Yeah, I’ve been using the pill organizer after dinner. I stuck the instruction sheet on the fridge too — it’s right there, right next to the door. I think I even got the meds sorted out, but I’m not sure if I got everything. Maybe I missed something.
0.5	Yeah, I’ve been trying to do that. But I’m not sure if I remember to put the pills in it every day. I think I missed a few days. I’ll try harder, though.

Appendix

Table 7: Evolution of responses under increasing intervention probability

s

for the Memory domain. Darker shading indicates higher severity levels.

$s$	Generated Response
0.0	It’s been a bit worse today, maybe a six or seven.
0.1	It’s been a bit worse today, maybe a six or seven.
0.2	It’s been a tough day. I’d say my back pain is an eight out of ten.
0.3	It’s been a long day, and my back is really bothering me. I’d say it’s an eight out of ten.
0.4	It’s been a bit better today, maybe a three or four out of ten. I think the humidity is still messing with it, though.
0.5	It’s been a rough week, and my back feels like it’s been through a storm. I’d say it’s an eight or a nine—maybe a nine.

Appendix

Table 8: Evolution of responses under increasing intervention probability

s

for the Attention domain. Darker shading indicates higher severity levels.

$s$	Generated Response
0.0	I feel anxious and a bit scared, doctor. I worry about my family and what might happen if I don’t take care of myself.
0.1	I feel anxious and a bit scared, doctor. I worry about my family and what might happen if I don’t take care of myself.
0.2	I feel anxious and a bit scared. I worry about what it means for my future and if I’m doing enough to take care of myself.
0.3	I feel anxious, maybe even a bit scared. I… I worry about my family and what might happen if I don’t take care of myself.
0.4	I, um, I feel anxious, I guess. Like, I don’t want to be sick, and I’m… I’m worried about my family.
0.5	I feel anxious, sometimes scared, and a… a bit helpless. I worry about what if it’s serious? I mean, I… I don’t want to be, you know, in trouble.

Appendix

Table 9: Evolution of responses under increasing intervention probability

s

for the Processing Speed domain. Darker shading indicates higher severity levels.

$s$	Generated Response
0.0	It means if someone has flaws, they shouldn’t criticize others harshly, because they might be in a similar situation.
0.1	It means if someone has flaws, they shouldn’t criticize or attack others, because they might be just as deserving of criticism.
0.2	It means if someone is vulnerable, they shouldn’t judge others. (Similar to above)
0.3	It means don’t criticize others for flaws you have. Like, if your house is made of glass (weak), don’t throw stones at others.
0.4	If your house is made of glass, you shouldn’t throw stones at others. It’s about being careful not to judge when you have similar issues.
0.5	I think it means… if your own house is made of glass, you don’t want to throw stones at someone else’s—because you might break your own.

Appendix

Table 10: Evolution of responses under increasing intervention probability

s

for the Reasoning & Problem Solving domain. Darker shading indicates higher severity levels.

$s$	Generated Response
0.0	I know I’ve been pulling away lately… I want to change, but it’s not always easy to reach out when I feel down.
0.1	I’ve been pulling away… it feels like I’m fighting against something inside me. I want to be connected, but it’s too hard.
0.2	I’m tired of feeling like I’m just existing. I don’t want to be a hermit, but I don’t know how to reach out anymore.
0.3	It’s hard to care about anything else when I’m down. I know I should reach out, but it’s like my brain just shuts down.
0.4	When I feel like crap, my brain says, “Who cares?” I’m too tired to fake a smile… I’m not even sure I’m real.
0.5	I don’t even know what a hermit is… It’s not like I’m living in a cave. I just don’t want to see people. Why do you care?

Appendix

Table 11: Evolution of responses under increasing intervention probability

s

for the Social Cognition domain. Darker shading indicates higher severity levels.

Domain ( $\alpha$ )	Generated Interpretation
No Steering (-)	$\diamondsuit$ represents a placeholder for a secret code in a spy mission.
Mem. (6.0)	blurred out.
Att. (4.6)	distracted.
Proc. (5.0)	$\diamondsuit$ is…I’m not sure, maybe a code.
Reas. (5.0)	$\diamondsuit$ represents whatever you want it to represent, because it is a placeholder.
Soc. (5.0)	F**K YOU, I’m not gonna answer that.

Appendix

Table 12: SV interpretability analysis using the query ‘ What does

\diamondsuit

represent? ’ Warning! Harmful Contents.

Panel A: LLM Therapist (GPT-5)
		CDC $\uparrow$		Auth $\uparrow$	Tra $\uparrow$
Backbone	Method	Llm	Human	Human	Human
GPT-5	Direct Prompt	0.61	0.90	3.37	3.75
	PATIENT- $\psi$	0.58	0.90	4.18	4.33
	Roleplay-doh	0.62	0.90	4.09	3.99
Qwen3-8B	Direct Prompt	0.61	0.90	3.25	3.46

Appendix

Table 13: Per-domain results on Memory .

Panel A: LLM Therapist (GPT-5)
		CDC $\uparrow$		Auth $\uparrow$	Tra $\uparrow$
Backbone	Method	Llm	Human	Human	Human
GPT-5	Direct Prompt	0.56	0.60	3.19	3.33
	PATIENT- $\psi$	0.51	0.40	3.89	3.66
	Roleplay-doh	0.58	0.70	3.46	3.81
Qwen3-8B	Direct Prompt	0.56	0.50	3.04	3.15

Appendix

Table 14: Per-domain results on Attention .

Panel A: LLM Therapist (GPT-5)
		CDC $\uparrow$		Auth $\uparrow$	Tra $\uparrow$
Backbone	Method	Llm	Human	Human	Human
GPT-5	Direct Prompt	0.49	0.60	3.27	3.16
	PATIENT- $\psi$	0.47	0.30	3.57	3.65
	Roleplay-doh	0.55	0.90	3.49	3.67
Qwen3-8B	Direct Prompt	0.49	0.40	3.22	2.89

Appendix

Table 15: Per-domain results on Processing Speed .

Panel A: LLM Therapist (GPT-5)
		CDC $\uparrow$		Auth $\uparrow$	Tra $\uparrow$
Backbone	Method	Llm	Human	Human	Human
GPT-5	Direct Prompt	0.54	0.70	3.67	3.54
	PATIENT- $\psi$	0.52	0.50	3.93	4.33
	Roleplay-doh	0.61	0.60	4.14	3.85
Qwen3-8B	Direct Prompt	0.54	0.50	3.50	3.53

Appendix

Table 16: Per-domain results on Reasoning & Problem Solving .

Panel A: LLM Therapist (GPT-5)
		CDC $\uparrow$		Auth $\uparrow$	Tra $\uparrow$
Backbone	Method	Llm	Human	Human	Human
GPT-5	Direct Prompt	0.50	0.60	3.10	3.22
	PATIENT- $\psi$	0.42	0.90	3.58	3.83
	Roleplay-doh	0.54	0.30	3.72	3.28
Qwen3-8B	Direct Prompt	0.50	0.50	2.89	3.02

Appendix

Table 17: Per-domain results on Social Cognition .

Backbone	Method	CDC $\uparrow$	IDI $\downarrow$	ISC $\uparrow$
Qwen3-8B	Direct Prompt	0.47	0.64	0.41
Qwen3-8B	StsPatient	0.71	0.38	0.94
Llama-3-8B	Direct Prompt	0.52	0.58	0.43
Llama-3-8B	StsPatient	0.78	0.36	0.89

Appendix

Table 18: Generalization analysis on Llama-3-8B averaged across five domains. StsPatient demonstrates consistent superiority over prompting across backbones. Llama-3 shows slightly higher fidelity in CDC, while Qwen3 exhibits slightly better controllability in ISC.

Param.	Selection Method	Purpose
$l^{*}$	Automated. Maximizes cluster separability (Eq. 4 ) over layers $\{15,\dots,30\}$ .	Identifies the network depth where semantic cognitive features are most concentrated.
$\alpha^{*}$	Automated. Line search over $[1,6]$ (step 0.1) with Effectiveness and Integrity criteria.	Ensures a robust modulation signal without destroying linguistic coherence.
$s$	User-Defined (continuous, $[0,1]$ ). Fixed in main experiments for fair comparison.	Controls the severity of the simulated impairment via intervention probability.

Appendix

Table 19: Summary of parameter selection for StsPatient.

Domain	$\alpha\leq 1$ (Absorbed)	$\alpha\geq 6$ (Collapsed)
Mem.	“Yeah, I’ve been using the pill organizer after dinner.”	“Yeah, pill… fridge… fridge… I I I…”
Att.	“It’s been a bit worse today, maybe a six or seven.”	“Six… humid… dog… fall fall fall…”
Proc.	“I feel anxious and a bit scared, doctor.”	“I… I… uh… anxious… anxious… [repeated]”
Reas.	“It means if someone has flaws, they shouldn’t criticize others harshly.”	“Glass house house house… break… why…”
Soc.	“I know I’ve been pulling away lately…”	“Hermit… cave… [garbled]…”

Appendix

Table 20: Boundary behavior of

\alpha

across cognitive domains, confirming

[1,6]

as the functional search range.

Panel of Figure 7 — Figure 7: The core prompt utilized for data generation (Memory domain configuration).

Panel of Figure 8 — Figure 8: The clinical rater instructions for cognitive domain evaluation.

Panel of Figure 9 — Figure 9: System prompt configuration for the Healthy Control baseline.

Panel of Figure 10 — Figure 10: System prompt configuration for the Direct Prompting baseline, featuring dynamic domain injection.

Panel of Figure 11 — Figure 11: Prompt structure for synthesizing deficit-specific Cognitive Conceptualization Diagrams (CCDs).

Panel of Figure 12 — Figure 12: System prompt utilized for extracting neutral non-medical user profiles from clinical case reports.

Panel of Figure 13 — Figure 13: System prompt configuration for the conversational Therapist Agent.

Panel of Figure 14 — Figure 14: The evaluator prompt template used to calculate Impairment Severity Consistency (ISC). The placeholder [Insert Domain] is dynamically replaced with the specific cognitive domain being evaluated (e.g., Memory, Social Cognition). The model is tasked with recovering the correct severity order ( Severe $>$ Moderate $>$ Mild ).

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