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.

Why This Matters

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

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

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

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

The CYNAERA Method

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.

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.

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.

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.

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.

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.

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.

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.

Prompt Examples

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

Felt Pattern Example

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

• 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.

Author’s Note:

All insights, frameworks, and recommendations in this written material reflect the author's independent analysis and synthesis. References to researchers, clinicians, and advocacy organizations acknowledge their contributions to the field but do not imply endorsement of the specific frameworks, conclusions, or policy models proposed herein. This information is not medical guidance.

Applied Infrastructure Models Supporting This Analysis

Several standardized diagnostic and forecasting models available through CYNAERA were utilized or referenced in the construction of this white paper. These tools support real-time health surveillance, economic forecasting, and symptom stabilization planning for infection-associated chronic conditions (IACCs). You can get licensing here at CYNAERA Market.

Note: These models were developed to bridge critical infrastructure gaps in early diagnosis, stabilization tracking, and economic impact modeling. Select academic and public health partnerships may access these modules under non-commercial terms to accelerate independent research and system modernization efforts.

Licensing and Customization

Enterprise, institutional, and EHR/API integrations are available through CYNAERA Market for organizations seeking to license, customize, or scale CYNAERA's predictive systems.

Learn More: https://www.cynaera.com/systems

About the Author 

Cynthia Adinig is a researcher, health policy advisor, author, and patient advocate. She is the founder of CYNAERA and creator of the patent-pending Bioadaptive Systems Therapeutics (BST)™ platform. She serves as a PCORI Merit Reviewer, Board Member at Solve M.E., and collaborator with Selin Lab for t cell research at the University of Massachusetts.

Cynthia has co-authored research with Harlan Krumholz, MD, Dr. Akiko Iwasaki, and Dr. David Putrino, though Yale’s LISTEN Study, advised Amy Proal, PhD’s research group at Mount Sinai through its patient advisory board, and worked with Dr. Peter Rowe of Johns Hopkins on national education and outreach focused on post-viral and autonomic illness. She has also authored a Milken Institute essay on AI and healthcare, testified before Congress, and worked with congressional offices on multiple legislative initiatives. Cynthia has led national advocacy teams on Capitol Hill and continues to advise on chronic-illness policy and data-modernization efforts.

Through CYNAERA, she develops modular AI platforms, including the IACC Progression Continuum™, Primary Chronic Trigger (PCT)™, RAVYNS™, and US-CCUC™, that are made to help governments, universities, and clinical teams model infection-associated conditions and improve precision in research and trial design. She has been featured in TIME, Bloomberg, USA Today, and other major outlets, for community engagement, policy and reflecting her ongoing commitment to advancing innovation and resilience from her home in Northern Virginia.

Cynthia’s work with complex chronic conditions is deeply informed by her lived experience surviving the first wave of the pandemic, which strengthened her dedication to reforming how chronic conditions are understood, studied, and treated. She is also an advocate for domestic-violence prevention and patient safety, bringing a trauma-informed perspective to her research and policy initiatives.