March 3, 2026Open Access

RGPxScientist — One-Page Brief (forwardable)

Key Points

Falsifiers reveal what could undermine a claim, enhancing research integrity and accountability.
Invariant candidates ensure researchers understand what needs to remain constant during experiments and modeling.
Auditability and evidence trails make it easier to track research claims and their corresponding tests over time.
This framework helps scientists articulate important assumptions and identify critical factors for their research validity.

Abstract

RGPxScientist — One‑Page Brief Forwardable summary for scientists: retrieval‑first, audit‑ready, falsifier‑driven. 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. Operational test: What stays invariant when surface details change? If nothing stays invariant, you’re looking at narrative. If something does, you may have a handle on the system. 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/2026-01-28ᵣgpxscientistₒne-page-briefₚodcastₜranscript. md

RGPxScientist — One-Page Brief (forwardable)

Key Points

Abstract

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