What it is

RGPxScientist is a retrieval‑first assistant that turns a research question into a traceable, falsifiable next‑step plan. It optimizes for auditability (evidence trails + failure modes), not rhetorical flourish.

The RGPx lens

RGPx reads pre‑metric structure: constraints and stabilizing patterns that exist before they crystallize into standard observables.

What RGPxScientist outputs

·  Definitions (terms pinned down; no hand‑waving)

·  Invariant candidates (what should remain stable across modeling/measurement choices)

·  Falsifiers (what would refute the claim)

·  Next checks / experiments (minimal set; highest information gain)

·  Evidence trail (source → excerpt → implication)

What problem it solves

Robustness is often asserted but rarely operationalized. Researchers need to state quickly: which assumptions matter, which changes should not matter, and what would decisively break the claim. RGPxScientist makes this explicit and testable.

One example

Question: “Is claim X robust, or pipeline‑dependent?”

Conventional answer: plausible mechanisms + references, but often no explicit invariant and no falsifier.

RGPxScientist answer: names (A) the invariant to test, (B) the surface details allowed to vary, (C) a falsifier, (D) a minimal perturbation set, and (E) the retrieved evidence chain.

Evaluate in 30 minutes

Input: one real research question + one specific claim you care about.

Do: Ask for the invariant + falsifier; then ask for the minimal perturbation set (2–5 changes).

Success: you leave with a concrete next move and an acceptable failure mode.

Why Prism pairs well

RGPxScientist is the engine (retrieval‑first, invariants, falsifiers). Prism is the pipeline (LaTeX‑native drafting, collaboration, and turning outputs into a publishable methods note).

Links

App: https://chatgpt.com/g/g-695e7b3d7344819190ac67772d4452f6-rgpx-scientist-3-1

Evidence base / Mesh / papers: https://github.com/gradient-pulse/phi-mesh/blob/main/README.md

Podcast: https://notebooklm.google.com/notebook/81bc2278-39e6-44e7-b766-af3ac310f285?artifactId=6d85e1bb-3f2e-4f1c-8e12-6b53b6d966a8

Podcast transcript: https://github.com/gradient-pulse/phi-mesh/blob/main/dialogues/2026-01-28_rgpxscientist_one-page-brief_podcast_transcript.md