--- license: other license_name: csil-3.0 license_link: https://catqualia.com/licensing language: - en tags: - falsification - reproducibility - research-methodology - python - evaluation pretty_name: Falsification Suite task_categories: - text-generation configs: - config_name: default data_files: - path: manifest.json split: train --- # Falsification Suite Standalone Python scripts, one per claim, each attempting to falsify a specific claim made in the accompanying research campaign documents. Published by [CatQualia](https://huggingface.co/CatQualia). Author: Christopher Betances — catqualia.com. ## This suite is replayable — here is the measured result Most datasets assert that their contents were verified. This one lets you **re-run the verification**, and publishes how much of it reproduces. `verify_ledger.py` takes the accompanying falsification ledger, finds every row that names the script which produced it, re-runs that script, parses the `JSON_RESULT` it prints, and compares the fresh verdict against the recorded one. Run across the whole suite: | Outcome | Scripts | |---|---| | Verdicts **reproduced** | **163** | | Fresh `UNVERIFIED` because inputs are not shipped | 120 | | Could not import (author's modules or packages absent) | 13 | | Produced no `JSON_RESULT` | 2 | | **Genuine divergences** | **1** | The single divergence is not a failure to reproduce. `fals_federation_of_clocks_hierarchy.py` emits the verdict `SURVIVES_GATED_OPTIMUM`, which the ledger never recorded — a taxonomy mismatch, where the script has a verdict vocabulary the ledger does not. It is reported rather than smoothed over. Reproduce it yourself: ```bash python3 verify_ledger.py --ledger FALSIFICATION_LEDGER.jsonl --scripts . --limit 30 python3 verify_ledger.py --ledger FALSIFICATION_LEDGER.jsonl --scripts . --limit 0 --jobs 6 ``` Two things to understand before running it: 1. **`UNVERIFIED` is not agreement.** A script that cannot measure returns `UNVERIFIED` and states why. It is counted separately from a reproduced verdict: counting it as agreement would overstate the record, and counting it as disagreement would understate it. 2. **Set `WME_ROOT` if you hold the author's code.** Many scripts import modules (`hj_spectral_bridge`, `gpu_scheduler`) and read model weights or grader data that are not in this repository; they look for them under `$WME_ROOT`, defaulting to a placeholder. Without it they degrade honestly to `UNVERIFIED` with a stated reason — which is why 120 scripts land in that bucket. With it, the reproducible fraction rises. The scripts do not pretend otherwise. ## Coverage: which ledger rows can be replayed at all `reproducibility_map.json` joins this suite to the ledger and reports, per row, whether the recorded outcome can be re-derived: | Ledger rows | Status | |---|---| | **8,353 (51.5%)** | **Replayable** — the row names a script shipped here | | 7,610 (46.9%) | Bespoke check — an internal harness with no shipped script | | 254 (1.6%) | Names a script absent from this suite | That table is the honest measure of how verifiable the ledger is. Half of it can be re-derived by anyone holding this repository. The other half is named but not yet packaged, and this file says so rather than implying the whole record is reproducible. ## What this collection is This is a corpus of **308 executable Python scripts** that each take one research claim and try to break it by measurement rather than argument. The scripts were written as part of an internal research workflow in which claims are not allowed to stand on rhetoric: a claim earns belief only if a script that would have killed it fails to do so on real, on-disk data. The scripts are not a library. They are not imported by each other and they share no package structure. Each is a self-contained program that measures one thing and prints a verdict. Two overlapping families exist in the collection: - **149 hand-written falsifiers.** These read a real artifact (a JSONL dataset row count, a file size, a corpus statistic, a recomputed metric) and compare it against a pre-registered numeric threshold. - **159 auto-emitted stubs.** These were generated mechanically by a script named `research_recursion_bridge.py` so that an unmeasured claim would occupy a visible, counted ledger row rather than disappear. The stubs are explicit that they do not measure anything. See *Limitations*. ## How many scripts | Metric | Value | Command | | --- | --- | --- | | Python scripts staged | 308 | `find . -maxdepth 1 -name 'fals_*.py' \| wc -l` | | Total bytes | 1,969,284 | `find . -maxdepth 1 -name 'fals_*.py' -printf '%s\n' \| python3 -c "import sys;print(sum(int(x) for x in sys.stdin))"` | ## The shared interface The interface is a convention, not an enforced abstraction. Across the 308 scripts: | Property | Scripts | Command | | --- | --- | --- | | Defines `def main()` | 304 | `grep -l 'def main' fals_*.py \| wc -l` | | Prints a `JSON_RESULT=` line | 306 | `grep -l 'JSON_RESULT' fals_*.py \| wc -l` | | Defines `def measure()` | 198 | `grep -l 'def measure' fals_*.py \| wc -l` | | Declares a module-level `CLAIM =` constant | 231 | `grep -l '^CLAIM' fals_*.py \| wc -l` | | Declares a module-level `THRESHOLD =` constant | 231 | `grep -l '^THRESHOLD' fals_*.py \| wc -l` | | Auto-emitted stubs | 159 | `grep -l 'AUTO-EMITTED' fals_*.py \| wc -l` | Four scripts have no `def main` and two print no `JSON_RESULT=` line, so the convention is near-universal rather than total. ### Input Whatever ground truth the claim rests on. Most commonly a JSONL corpus under the sibling `datasets/` directory, a file size, or a recomputed corpus statistic. Four scripts resolve paths relative to a parent repository root: ```python REPO = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) CORPUS = os.path.join(REPO, "datasets", "anime_metaphor_engine.jsonl") ``` 77 scripts reference a `datasets` path, 35 a `model` path, 12 a `reports` path. **The referenced artifacts are not included in this dataset** (see *Limitations*). ### Computation The substantive scripts follow a common three-step shape: load the ground-truth artifact, compute the claimed quantity alongside a null or baseline, then compare the difference against a pre-registered threshold. The verdict is then selected by an explicit branch. From `fals_alignment_scalar_gate_phase_coherence.py`, verbatim: ```python # ---- PRE-REGISTERED VERDICT ---- revived = bool(phase_minus_partition > 0.05 and ph_beats) if revived: verdict = "CONFIRMED" note = "REVIVED: phase-coherence beat partition by >+0.05 held-out AND beat its null." elif directed_revival: verdict = "REVIVAL-CONDITIONAL" ... else: verdict = "REFUTED" ``` That script's own docstring states its pre-registered thresholds verbatim: ``` REVIVED : held-out real_corr(phase) - real_corr(partition) > +0.05 AND phase beats its null REFUTED : phase fails +0.05 over partition AND/OR fails its null on the symmetric graph REVIVAL-CONDITIONAL: phase fails on symmetric, BUT directed q>0 beats q=0 by held-out +0.05 ``` ### Output A single machine-parseable line on stdout: ```python print("JSON_RESULT=" + json.dumps(out)) ``` The `out` object carries `claim`, `threshold`, `measured`, `n`, and `verdict`. Because `n` is included, a measurement that silently read zero rows is detectable rather than silent. This is the reason `n` is present at all. ## Verdict vocabulary Verdicts are string literals. Counting occurrences of each literal across the suite (not the number of scripts that can emit it): | Verdict | Occurrences | Meaning in this framework | | --- | --- | --- | | `UNVERIFIED` | 304 | The claim was not measured. Either a stub, or a real script whose ground truth was absent. | | `REFUTED` | 146 | The measurement contradicted the claim, or fell below the pre-registered threshold. | | `CONFIRMED` | 145 | The measurement met the pre-registered threshold. | | `PASS` | 7 | Non-standard synonym for `CONFIRMED` used in a few scripts. | | `FALSIFIED` | 3 | Non-standard synonym for `REFUTED`. | | `FAIL`, `FALSE`, `TRUE`, `INCONCLUSIVE` | 1 each | Non-standard, used in single scripts. | | `REVIVAL-CONDITIONAL` | — | Third state used where a claim fails on one graph but survives on a directed variant. | Command: `grep -hoE '"(UNVERIFIED|CONFIRMED|REFUTED|FALSIFIED|INCONCLUSIVE|PASS|FAIL|TRUE|FALSE)"' fals_*.py | sort | uniq -c` ## How to run one script ```bash # 1. Fetch the dataset git clone https://huggingface.co/datasets/CatQualia/falsification-suite cd falsification-suite # 2. Run a script (most are stdlib-only; 79 need numpy) python3 fals_acceptance_rate_prediction_adaptive_draft_leng.py # 3. Read the verdict # JSON_RESULT={"claim": "...", "threshold": "...", "measured": {...}, "n": 0, "verdict": "UNVERIFIED", ...} ``` Verified working example — `fals_acceptance_rate_prediction_adaptive_draft_leng.py` runs to completion with exit status 0 and prints a `JSON_RESULT=` line: ```json {"claim": "On a *small* target model \u2014 the regime WaveMotion actually runs on a single dying laptop \u2014 the", "threshold": "proves the optimal policy is a threshold policy: stop and verify when `P(\u22651 rejection) > \u03c4`. [REAL] A *trained*", "measured": {"status": "STUB_NOT_YET_MEASURED", ...}, "n": 0, "verdict": "UNVERIFIED"} ``` That output is a stub reporting `UNVERIFIED`, which is the honest result for it — it demonstrates the reporting convention, not a measured finding. Most scripts are pure standard library. 79 require `numpy` (`grep -l 'import numpy' fals_*.py | wc -l`). One script, `fals_asi_feasibility_and_timeline.py`, performs live HTTP requests to `arxiv.org` and will hang or fail without network access. ## What a falsified claim means in this framework A falsifier is written *before* the verdict is known, with its threshold fixed in the source, so the number cannot be chosen after seeing the result. When a script returns `REFUTED`, the claim is treated as removed from the project's working set of beliefs: `fals_agentic_benchmarks_swebench.py` illustrates the terminal consequence, where a refutation is written into the output as a generalizable conclusion — ```python "REFUTED: empty and list-all agents score ~0 on the real grader; the " "sec 5 null holds and the ABC exploit does not transfer here." ``` `CONFIRMED` does **not** mean proven. In this framework it means *this specific falsification attempt, at this threshold, on this data, failed to break the claim*. The claims here are narrow and mostly quantitative, so a `CONFIRMED` verdict is evidence about one measurement, not a validated theory. The collection is closer to a lab notebook with executable entries than to a benchmark suite. The `verdict` field always describes the fate of the script's *own* pre-registered hypothesis, and is not consistently oriented relative to a source document's claim. In `fals_asi_feasibility_and_timeline.py` the script quotes a claim it is attacking — `Claim: "ASI is bound to be discovered" — REFUTED as stated.` — so a reader who scans only for the word `REFUTED` cannot tell whether the source claim or its negation was refuted. That specific inverted framing appears in 1 script. Read each script's `threshold` and `measured` fields rather than the `verdict` string alone. ## Limitations Read these before drawing any conclusion from a verdict. 1. **The ground-truth artifacts are not included.** 77 scripts read from a `datasets/` directory and 35 from a `model/` directory that are not part of this release. Run as-is, most substantive scripts will raise `FileNotFoundError` or report `UNVERIFIED`. Confirmed by execution: `fals_alignment_scalar_gate_phase_coherence.py` fails with `FileNotFoundError: [Errno 2] No such file or directory: '.../datasets/anime_metaphor_engine.jsonl'`. The scripts document their methods and thresholds reproducibly; the numbers are not reproducible from this repository alone. 2. **159 of 308 scripts (52%) measure nothing.** They are auto-emitted stubs that return `STUB_NOT_YET_MEASURED` and the verdict `UNVERIFIED`. Their docstrings state this in the source, verbatim: *"STATUS: UNVERIFIED STUB. This fires a ledger row so the doc's claim is TRACKED in FALSIFICATION_LEDGER.jsonl instead of sitting un-fired. It does NOT yet run a real measurement -- it reports verdict=UNVERIFIED (loud, first-class) so the gap between "claim written" and "claim measured" is VISIBLE and COUNTED, never hidden."* These files are included because the stub/hand-written split is itself the finding. 3. **The `CLAIM` field is not always a claim.** Of the 231 scripts declaring a module-level `CLAIM =` constant, most hold a verbatim claim string. In a number of files the same variable name holds document metadata instead — for example `fals_CHECKPOINT_BATCH4.py` holds `"Timestamp: 2026-06-18 (post-break resume) · Status: STAGED, SAFETY ON, awaiting Captain fire signal."` Field semantics are therefore not uniform. `manifest.json` records the constant verbatim and does not repair it. 4. **77 scripts declare no `CLAIM` constant.** Their claim is stated in prose under a section heading in the module docstring (e.g. `WHAT THE DOC CLAIMS` in 38 scripts). `manifest.json` records these as `"not determined from the content"` and preserves the full docstring instead, so the text can be read at its source rather than reconstructed. 5. **16 files are not falsifiers.** Files named with an uppercase stem (`fals_MASTER_INVENTORY.py`, `fals_RESEARCH_NOTES.py`, `fals_DOCUMENT_QUALITY_AUDIT.py`, `fals_CANONICAL_TYPES.py`, among others) are project-admin and checkpoint documents that carry the `fals_` prefix but do not falsify a claim. Command: `ls fals_*.py | grep -cE 'fals_[A-Z]'` 6. **Not peer-reviewed.** Every claim, threshold, corpus, and verdict is the author's own. Nothing here has been independently verified, and the `CONFIRMED`/`REFUTED` strings are self-assigned. 7. **A stale count to be aware of.** Some scripts quote corpus statistics in their docstrings that no longer match the present corpus; `fals_MASTER_INVENTORY.py` contains the line `- 122 documents total in reports/research_campaigns/ (5.9 MB)`. Those strings are frozen at authoring time and are not current measurements. ## Files - `manifest.json` — machine-readable index of all 308 scripts. Per script: `filename`, `bytes`, `claim_verbatim` (copied from the script's own constant, never paraphrased), `claim_source`, `source_doc`, `threshold_verbatim`, `docstring`, `docstring_header`, `verdict_literals`, `is_stub`, `has_measure`, `emits_json_result`. - `fals_*.py` — the 308 scripts. - `build_manifest.py` — stdlib-only generator for `manifest.json`. Re-run with `python3 build_manifest.py` to regenerate the index from the scripts themselves. - `LICENSE` — MIT. ### Why MIT MIT is the right choice here because the payload is executable source code rather than prose or data. The scripts are meant to be read, run, modified, and re-used as templates for writing one's own falsifiers, and MIT permits all of that with no ambiguity and minimal obligations — retain the notice, accept the warranty disclaimer. A more restrictive license would obstruct the only useful thing to do with the collection. MIT imposes no restriction on the claims or documents the scripts describe, and grants no rights over the underlying research corpora or model weights, which are not distributed here. The `LICENSE` file also carries the author's site, catqualia.com. ## Citation ```bibtex @misc{betances2026falsificationsuite, author = {Betances, Christopher}, title = {Falsification Suite: 308 Executable Claim-Falsification Scripts}, year = {2026}, publisher = {Hugging Face}, howpublished = {\url{https://huggingface.co/datasets/CatQualia/falsification-suite}}, note = {Author site: catqualia.com} } ```