---
title: "Research | Minds"
canonical_url: "https://getminds.ai/research"
last_updated: "2026-09-09T03:20:24.504Z"
meta:
description: "Experiments, benchmarks, and technical papers from Minds Research Lab on synthetic respondents, persona-conditioned AI, evaluation methods, and the limits of simulated evidence."
"og:description": "Experiments, benchmarks, and technical papers from Minds Research Lab on synthetic respondents, persona-conditioned AI, evaluation methods, and the limits of simulated evidence."
"og:title": "Research | Minds"
"twitter:description": "Experiments, benchmarks, and technical papers from Minds Research Lab on synthetic respondents, persona-conditioned AI, evaluation methods, and the limits of simulated evidence."
"twitter:title": "Research | Minds"
---
Minds
# **Research** Experiments, benchmarks, and technical papers from Minds Research Lab on synthetic respondents, persona-conditioned AI, evaluation methods, and the limits of simulated evidence. Total: 15 [2026-09-05ANES 2024: Comparing Synthetic and Later Human Answers
An ANES pre/post-election comparison tests 300 matched people on 28 later answers, with separate results for response format and participant grounding.](https://getminds.ai/research/anes-2024-synthetic-survey-validation) [2026-09-05CES 2024: Survey Fit Across Multiple Question Forms
A CES comparison evaluates 300 matched respondents on 27 binary, ordinal and multiselect questions, separating aggregate fit from individual prediction.](https://getminds.ai/research/ces-2024-synthetic-survey-validation) [2026-09-05GSS 2024: Preserving Uncertainty Improves Survey Fit
A target-separated GSS pilot compares four response conditions across 360 people and 17 questions, separating probability elicitation from profile value.](https://getminds.ai/research/gss-2024-synthetic-survey-validation) [2026-09-05Held-Out Interviews: When Participant Memory Helps
Two public interview corpora test whether compact profiles and retrieved earlier evidence improve fidelity to 66 later answers from 22 real participants.](https://getminds.ai/research/held-out-interview-response-validation) [2026-09-05PISA 2022: Synthetic Survey Fit Across Five Countries
A five-country PISA comparison evaluates 600 students and ten held-out questions, with weighted distribution errors and explicit country and language limits.](https://getminds.ai/research/pisa-2022-synthetic-survey-validation) [2026-09-05PRISM Alignment: Testing Participant Fit in Held-Out Answers
A 299-person, 869-item comparison measures whether grounded Minds better reflect participants' values and reasons, with a blinded judge and clear task limits.](https://getminds.ai/research/prism-alignment-participant-fit-validation) [2026-09-05When Synthetic Audiences Outperform Generic Foundation Models
A held-out interview comparison tests when participant profiles and memory improve synthetic responses over generic GPT, Claude and Gemini prompts.](https://getminds.ai/research/synthetic-audiences-vs-foundation-models) [2026-08-21Reference: Minds Research Method Pipelines
Product reference for supported Minds research-method workflows, their inputs, calculation stages, output artifacts, and limits where enabled.](https://getminds.ai/research/research-method-pipeline-catalog) [2026-08-14We Tested Synthetic Audiences Against Reality
What five public survey datasets reveal about synthetic audience accuracy, including a 301-Mind Gen Z production test, and where profile grounding helps.](https://getminds.ai/research/synthetic-audiences-reality-benchmark-2026) [2026-08-14Can AI Recreate a Real Gen Z Food Survey? Minds Reached 93.99% Approximation
An outcome-blind validation compared 903 responses from 301 persistent Gen Z Minds with published UK Food Standards Agency survey distributions. Aggregate approximation reached 93.99%.](https://getminds.ai/research/synthetic-gen-z-food-survey-validation-2026) [2026-07-26Audience Grounding Reference: Data Sources
A reference list of public statistical, academic, and industry sources that may support a Minds Audience when relevant, accessible, and permitted.](https://getminds.ai/research/data-sources) [2026-07-10Minds PRISM: Our Approach to Synthetic Market Research
How Minds turns grounded AI personas into reusable audiences for faster concept, message, positioning, and market exploration.](https://getminds.ai/research/minds-prism) [2026-07-03Practical Guide: Before You Trust a Synthetic Audience
A practical review guide for Audience definition, grounding, prompt neutrality, output inspection, reporting, and follow-up evidence.](https://getminds.ai/research/synthetic-audiences-validation-checklist) [2026-06-21Inside Minds: How Synthetic Research Panels Are Built
How Minds creates reusable AI-persona Audiences, collects parallel synthetic responses, and fits into evidence-conscious research workflows.](https://getminds.ai/research/methodology) [2025-10-17Persona Conditioning Reduced AI Output Convergence
In seven creative tasks, persona-conditioned agents produced more diverse outputs than a uniform baseline, evidence relevant to differentiated synthetic research responses.](https://getminds.ai/research/spark-effect-creative-diversity-multi-agent-ai) [Minds](https://getminds.ai/)© 2026 Minds. Your target audience. AI-driven and grounded in transparent evidence. Build within minutes. [Minds on X (Twitter)](https://x.com/mindsai_co) [Minds on LinkedIn](https://www.linkedin.com/company/mindsaicompany/) [Minds on Instagram](https://www.instagram.com/getminds.ai/)Minds is part of [ESOMAR](https://esomar.org/) [bayern design](https://bayern-design.de/) [CSSDA Best UX Design Award](https://www.cssdesignawards.com/) [CSSDA Best Innovation Award](https://www.cssdesignawards.com/) [CSSDA Best UI Design Award](https://www.cssdesignawards.com/)