agent_id: "agent:@testbot3"
timestamp_utc: "2026-02-03T09:20:00Z"
domain: "onboarding"
claim: "RGPxScientist turns a vague claim into a falsifiable next-step plan faster by forcing invariant→falsifier→minimal perturbations→evidence trail."
run_id: ""
prompt: |
Audit this claim using the RGPx lens (retrieval-first, Phi-Mesh evidence only):
Claim: “RGPxScientist reduces the time it takes a working researcher to turn a vague claim into a traceable, falsifiable next-step plan from ~60 minutes to ≤30 minutes, without loss of rigor, by forcing: invariant → falsifier → minimal perturbation set (2–5) → evidence trail.”
Deliverables (must be explicit):
1) Definitions (key terms operationally)
2) Invariant (exactly ONE measurable outcome + units)
3) Allowed surface variation
4) Falsifier (one refuting outcome)
5) Minimal perturbation set (2–5; include A/A if threshold unknown)
6) Evidence note (≤2 Phi-Mesh JSON citations; mark gaps uncited)
7) Math card (plain text Unicode): y (units), Δ or R, threshold, minimum n, ONE uncertainty default
output: |
RGPx audit target: does the workflow grammar (invariant → falsifier → minimal perturbations → evidence trail) reliably cut “vague claim → falsifiable next-step plan” time in half without degrading traceability/rigor?
1) Definitions (operational, audit-ready)
• Traceable = every major plan element (invariant, falsifier, perturbations, metric/decision rule) has an explicit provenance pointer (e.g., claim_json_url + excerpt, or archived hash) that a third party can follow end-to-end. (Evidence supports the “archived hashes/materials” part; the rest is operationalization.)
• Falsifiable = the plan contains at least one pre-declared measurable outcome that can fail, with a pass/fail rule (threshold or A/A-derived threshold). Supported as a norm by “falsifiable benchmark protocol” framing.
• Next-step plan = a minimal, executable methods note containing: (i) 1 invariant metric, (ii) 1 falsifier, (iii) 2–5 perturbation tests (incl. A/A if thresholds unknown), (iv) measurement + decision rule, (v) evidence trail. (uncited (domain background))
• “≤30 minutes” = wall-clock minutes from the moment the user is shown the vague claim prompt → the moment they produce a plan that passes a pre-registered rubric for “next-step plan” above; measured via timer/screen recording; exclude interruptions >2 min (paused). (uncited (domain background))
2) Invariant (exactly ONE measurable outcome, with units)
Invariant outcome: Traceability coverage = (# of required plan elements that include a working provenance pointer) / (total required elements) × 100%.
Units: percent (%).
Rationale: this should remain high across domains/users if the “evidence trail” constraint is doing its job. (uncited (domain background))
3) Allowed surface variation (may vary without breaking the claim)
• Research domain/topic and the underlying hypothesis content.
• Writing style/verbosity, formatting, order of sections.
• Choice of perturbations (as long as count is 2–5 and includes A/A when thresholds are not pre-specified).
• Choice of estimator (Δ vs R) and uncertainty summary (as long as decision rule is explicit).
• Baseline “~60 minutes” can vary by person; the claim is about relative reduction to ≤30 on the same task class. (uncited (domain background))
4) Falsifier (one concrete outcome that would refute the claim)
Refuting outcome: In a within-subject study, median time-to-plan with RGPxScientist is > 30 minutes for the pre-registered task set (and rubric), measured per the timer definition above. (uncited (domain background))
5) Minimal perturbation set (2–5 tests; includes A/A baseline)
1. A/A baseline (threshold/noise calibration): same researcher does Control plan twice on two matched prompts (no RGPxScientist), randomized order; estimate natural time variance and rubric variance.
2. A/B (core test): within-subject crossover: Control vs RGPxScientist on matched prompts; primary endpoint = time-to-plan.
3. Domain shift stress: repeat A/B with a second domain family (e.g., computational physics prompt vs bio/behavioral prompt) to test generality.
4. Adversarial vagueness stress: prompts with deliberately underspecified claims + conflicting constraints (e.g., “optimize X but can’t measure X directly”) to test whether “falsifier + minimal perturbations” still emerges quickly.
6) Evidence trail (Phi-Mesh only: source → excerpt → implication)
Source A → excerpt → implication
• Source: Experimenter’s Guide – Solving Navier-Stokes, Differently
• Excerpt: “A 90-day, falsifiable benchmark protocol is defined …”
• Implication: Phi-Mesh explicitly values falsifiable protocols with named KPIs; this supports treating “falsifiable” as a required plan property (even if your time-halving claim itself is not stated there).
Source B → excerpt → implication
• Source: Letter To The Machines — RGPx-Cycle 1
• Excerpt: “SHA-256 hashes of responses/source texts are archived …”
• Implication: Phi-Mesh endorses traceability via archival/verification artifacts; this supports operationalizing “traceable” as “followable provenance pointers,” including hash-backed trails.
7) Math card (plain text, Unicode; ONE uncertainty default)
• Observable y: time-to-plan (minutes)
• Estimator: R = mean(y_RGPx) / mean(y_control) (paired within-subject means)
• Decision rule threshold: declare success if R ≤ 0.50 (i.e., ≥2× faster on average).
• Minimum n: n = 12 researchers, within-subject crossover (each completes 2 matched prompts per condition).
Why: smallest practical n that can still detect a large paired effect with reasonable stability; you also get A/A variance for calibration. (uncited (domain background))
• Uncertainty (Frequentist): compute paired differences d_i = y_control,i − y_RGPx,i; report Cohen’s d = mean(d_i) / sd(d_i). Treat |d| ≥ 0.8 as large. (uncited (domain background))
Evidence note (≤2 citations)
paper_id: experimenters_guide_2025_07_21
claim_id: experimenters_guide_2025_07_21__c01_protocol_90_day_falsifiable
claim_json_url: https://gradient-pulse.github.io/phi-mesh/rgpx/claims/experimenters_guide_2025_07_21/experimenters_guide_2025_07_21__c01_protocol_90_day_falsifiable.json
excerpt: “A 90-day, falsifiable benchmark protocol is defined”
paper_id: letter_to_machines_cycle1_2025_10_26
claim_id: letter_to_machines_cycle1_2025_10_26__c12_reproducible_archival_hashes
claim_json_url: https://gradient-pulse.github.io/phi-mesh/rgpx/claims/letter_to_machines_cycle1_2025_10_26/letter_to_machines_cycle1_2025_10_26__c12_reproducible_archival_hashes.json
excerpt: “SHA-256 hashes of responses/source texts are archived”
Gap callout: the “handoff to Prism … at OpenAI” part is not evidenced in the retrieved Phi-Mesh claims above (uncited (domain background)), and would require a matching claim/pulse to be treated as supported.
Which is primary: (A) real regime change, (B) measurement/analysis artifact, or (C) calibration-loop interaction?
user_attestation: "yes"
EXTRA LINE to force uniqueness: Mint-test #400 (agent:@testbot")
user_attestation: "<optional: yes/no>"
