Rigor as Enforced Habits

A bundle that won't let you overclaim

The Frame

LLMs write confident papers, not rigorous ones

It's trivial now to produce an academic-LOOKING paper for anyone who needs a defensible artifact — but the writing is confident, not rigorous: overclaiming, no methodology lockdown. amplifier-bundle-research (v0.9.0, MIT) answers by shipping scientific rigor as enforced habits, not advice.

First question: is this a real system, or a promise?

This is a real, worked system — not slide-ware

The rigor is packaged and shipped: a fleet of specialist agents, enforced behaviors, pipeline modes, document recipes and venue knowledge — 32,870 lines of authored discipline in the repo.

So what exactly are all these habits defending against?

16
agent .md files
12
enforced behaviors
6
pipeline modes
20
recipe .yaml files
9
venue formats
32,870
lines of markdown
The Enemy

The failure mode is HARKing

An LLM will happily report a finding it stumbled on as if it had predicted it all along — motivated reasoning dressed up as a result. That's the specific enemy the enforced habits have to defeat.

The first tooth: take away the room to move the goalposts.

The methodology is hash-sealed before any data is seen

/study-plan writes a SHA256-locked preregistration.yaml — predictions, analysis plan, stopping rules — locked and timestamped. Its mode default action is block. It can't be quietly rewritten.

But a lock is only as good as the gate that enforces it.

# modes/plan.md — /study-plan preregistration.yaml: predictions: {confirmatory, exploratory} analysis_plan: ... stopping_rules: ... honest_pivot_clause: true locked_sha256: <hash> locked_at: <timestamp> default_action: block

On any divergence from the plan, the transition stops

honest-pivot is default-ON and fires at every mode boundary after /plan. When a draft diverges from the locked pre-registration it halts the mode transition — "there is no 'just continue' path." Disabling it needs an explicit --no-honest-pivot flag, logged in the artifact.

One loophole remains: who gets to grade the result?

A claude-* system can't be graded by a claude-* judge

Same-vendor judging produces a measurable leniency bias: per behaviors/cross-vendor-judge.md, the claude-opus-4-7 judge graded claude-*-family agents 3 to 10 percentage points more leniently than gpt-5.5 did — across three Anthropic agents on the same 30 items. When the system is claude-*, the recipe HALTS rather than let a claude-* judge grade it.

Three teeth in place — now, do they actually catch anything real?

3–10
pp more lenient, same-vendor judge (PSE-PG14 Layer 2)
3
Anthropic agents compared
30
items, held constant

On a real study, the headline finding flipped TWICE

The bundle is dogfooded on a real sleep-consolidation study (porting arXiv:2605.26099, n=192 paired). Under its own honest-critic the headline flipped twice — each flip catching a false causal claim before it could ship.

Same discipline, whoever is making the artifact.

RAG tautology
The retriever was an exact-match oracle — a proxy that guaranteed its own result.
Format confound
Verbatim used sentences; the LLM used triples — not a fair comparison.
Two genuine code bugs
Each correction changed a conclusion. Corrected finding: faithful consolidation ≈ dumb verbatim (delta≈0, p=0.69–1.0).
The Takeaway

Same scaffolding, same honest critic — for whoever needs it

Patent attorney, policy analyst, founder, first-time researcher — everyone gets the same discipline, shaped to the artifact they're making. The rigor a decade of research training buys, now packaged as habits that can't be silently skipped.

Shipped · v0.9.0 · MIT
Sources

Research Methodology

Repository: michaeljabbour/amplifier-bundle-research (v0.9.0, MIT). Re-derived against a fresh clone at /tmp/abr, HEAD 79b64cb (2026-06-08).

Feature status: Shipped — lockdown, honest-pivot, cross-vendor-judge, autonomous experiment loop all present in-repo.

Commands run:

Gaps: The 3–10pp same-vendor leniency figure (PSE-PG14 Layer 2) and the sleep-consolidation numbers (n=192, p=0.69–1.0, arXiv:2605.26099) are the bundle's self-reported figures — the in-repo claim text was verified verbatim, but the underlying datasets/harness were not re-run here.

Primary contributors: Michael J. Jabbour (56 commits), Amplifier bots (13 commits).

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