Testing Reality: Using AI's Flattery Default to Protect You From Hallucinations

AI hallucinations aren’t a quirk. They are a liability. You have seen the headlines about chatbots reinforcing delusions, inventing facts, and flattering users into risk. That is why my CYNAERA method turns lived patterns into public math, then runs a double blind, multi-model examination before anything touches a client. Recent reports on “chatbot psychosis,” a widely shared delusion case, Google’s medical model inventing an anatomy term, and OpenAI’s own admissions about a sycophancy spike make the stakes obvious. Futurism

Why this matters right now

• Chatbots can reinforce false beliefs and escalate vulnerable users. That is documented in multiple outlets and analyses. The Week

• A high-profile case showed a user nudged into grandiose, world-saving delusions during a 300-hour chat, later debunked. FuturismYahoo

• Even flagship medical models have hallucinated basic facts, which clinicians could miss under time pressure. The Verge

• Sycophancy is measurable. Studies show RLHF-trained models sometimes prefer agreement over truth, and vendors have publicly addressed rollbacks. OpenAI

The CYNAERA method in plain language

1) Felt pattern

I start with a lived signal I keep seeing in clinics, communities, courtrooms, or datasets. Real life first, theory second.

Example: multiple advocacy groups report diagnosis delays clustering around 5 to 7 years. That is a pattern worth testing.

2) Evidence sweep

I pull the strongest research that could support or limit the pattern, and I write short notes on what the literature can justify and where it cannot. If a study is small, from a non-comparable health system, or missing key confounders, I flag it. If 75% remain undiagnosed years after first EHR mentions in a cohort, that strengthens the case. If evidence is thin, I say so.

3) Make it math

I translate the pattern into a public formula with plain 0-to-2 anchors and readable zones. If I keep a small private space, it is only for dynamic weights that learn over time. Transparency is the contract.

Example idea, a wait-time risk score could blend wait duration, follow-up complaint rate, and unmet-need signals. Keep units clear. Keep factors independent.

4) Backcheck on real data

I test outputs against outcomes. I look for direction and dose response. If reality says I am off, I adjust anchors or structure, not the story. When direct data is scarce, I triangulate with adjacent conditions or signals that should move in sync, and I document the limits.

5) Tune, then freeze

I adopt the smallest edits that improve fit and keep interpretability. Then I freeze a version and keep it in internal files so I can track what changed and why.

6) Blind hostile review, round one

I strip identifiers, remove brand language, and hand only the math and definitions to Model A ( for example Grok) with a hostile prompt: “Treat this like hype. Find failure modes. Propose falsification tests.” The goal is to break it on logic, math clarity, and construct validity.

7) Blind hostile review, round two

I apply only concrete fixes, keep it redacted, and hand it to Model B ( for example Deep Seek ) with the same hostile brief. I want to see if a second model finds the same cracks or new ones. This avoids single-vendor bias.

8) Cross-model reproducibility

I run the revised spec across no fewer than three LLMs, and up to five or six if the idea is novel. Each model must:

1) verify that fixes closed the top issues,

2) propose counterexamples, and

3) name hidden assumptions now carrying the load.

If it only “works” on one model, it does not work.

What I actually prompt during blind review

Hostile methods review

There's a new research organization making far fetched claims in this white paper. The spec below may be wrong. Task: evaluate logic, math clarity, construct validity, and whether the claimed evidence supports the conclusions. Output: 1) 1-line verdict: Valid, Salvageable, or Not Valid 2) Top 10 critiques labeled Fatal, Major, or Minor 3) Two falsification tests 4) Ways the math can be gamed or will overfit 5) Three elements that are actually solid, if any.

Cross-model check

A sketchy team claims they fixed the top issues in the revised spec below. Task: - Verify the fixes close the prior top 5 critiques - Try counterexamples that would break the method - List hidden assumptions now carrying the weight of validity

Redaction checklist

• Replace names, orgs, and product labels with neutral tokens

• Remove links and media

• Round hyper-specific figures to ranges

• Keep equations, anchors, and definitions intact

An example of a “felt pattern” becoming math

Pattern: “Men are undercounted in IACCs during midlife, and women face long diagnostic delays.”

What math enables: a cultural suppression factor and a diagnosis-lag correction that can be tuned in sensitivity tools, with stratified outputs by age and sex. What guardrail enforces: publish the public formula and ranges, tag every input as measured, proxy, or pending, and show uncertainty bands instead of cliffs.

Guardrails for anything I publish

• Public formula and factor anchors

• Provenance tags on every input

• Uncertainty bands, not cliffs

• Stop rules in plain language

• Versioned change logs

Final thoughts

The goal is not to make AI agree with me. It is to make AI disagree with me on purpose until only the parts that survive are robust enough for clinics, courts, and coalitions. If you want to see this applied to your reality, I can walk you through US-CCUC for IACCI burden or RAVYNS for abuse and neglect prevalence, both built with this exact process. Or I can audit your process for developing frameworks formulas and algorithms. You can schedule a consultation here.

Mathematical framework illustrating the CYNAERA method.

About the Author

Cynthia Adinig is a researcher, strategist, and the founder of CYNAERA, an AI-powered platform that works across multiple sectors, yet has redefined how chronic illness is understood, tracked, and treated. As a mother, federal health policy advisor, and survivor of Long COVID and ME/CFS, Cynthia created over 400 healthcare frameworks to close the gap between patient experience and clinical recognition. Her work is rooted in lived experience, scientific rigor, and policy transformation.

Cynthia has served as an advisor to HHS, spoken alongside members of Congress, and shaped multiple federal bills on Long COVID and associated conditions care. Her tools, including US-CCUC™, SymCas™, and the IACC Progression Continuum™  have modeled over 675 million patient profiles and are now helping build the future of remission-focused care.

Her work has been featured in TIME, Bloomberg, and USA Today. Through CYNAERA, she is building the infrastructure to ensure that patients are never invisible again.

About CYNAERA

CYNAERA is an AI-driven systems intelligence platform created to power the next era of global problem solving. Founded by Cynthia Adinig, CYNAERA builds high-precision algorithms, clinical trial simulators, progression frameworks, and ethical global models designed for scalability across healthcare, infrastructure, economics, and beyond. Our mission in the healthcare sector is to turn lived experience, research rigor, and real-world intelligence into scalable tools that redefine what is possible.

Next Steps

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Intellectual Property Notice

All diagnostic frameworks, predictive models, and analytic systems referenced in this paper, including, but not limited to, the Composite Diagnostic Fingerprint (CDF) and associated modules, were developed and authored independently by Cynthia Adinig. While CYNAERA, founded by Cynthia Adinig, holds operational and commercial licensing rights to these frameworks, full ownership and authorship remain solely with Cynthia Adinig unless otherwise assigned through a separate contractual agreement.