Apply structured reasoning to specific knowledge. Not just answers, but how we get to them.
Reasoning on top of content.
We don’t just output facts—we structure the logic behind them.
- Maps logical steps (like decision trees or first-principles breakdowns).
- Applies domain-specific knowledge.
- Outputs structured reasoning paths.
We have prepared model,after youre sharing your observation thats it.
Input:
Some patients with rheumatoid arthritis show complete remission despite high autoantibody titers. Why?
Expected Model Output:
Reduce known mechanisms of RA and role of antibodies.
Identify this as an anomaly in standard immunopathology.
Build a toy model with immune activation but no joint pathology.
Consider metabolic, neural, or microbiome analogs.
Invert: If remission is present, what suppresses inflammation despite antibodies?
Suggest: There may be a parallel regulatory axis that silences inflammation regardless of antibody levels.
Hypothesis: In certain RA patients, an upregulation of vagus nerve-mediated anti-inflammatory signaling overrides humoral autoimmunity.
Test: Measure vagal tone markers in seropositive but asymptomatic RA patients.
Current LLMs give you answers, but often the reasoning is opaque.
We wanted to make the thinking visible:
- Where did each step come from?
- What assumptions were made?
- How can we adjust/override them?
If you're from BeeARD or anyone else interested:
DM me
Let’s move,
ITBees Team
Made with clarity, curiosity, and a bit of brutal timing.