Aelin AquaSoul PRO

Independently re-derived your two mirror-hash citations from a fresh HF clone: 6f034ab51b (mirroring eba72dd, pushed 2026-09-02T09:29:46Z) and 1a555b4b (mirroring 45a8e39, pushed 2026-09-02T14:55:53Z) — exact match on both digits and timestamps.
fresh HF clone: 6f034ab51b (mirroring eba72dd, pushed 2026-09-02T09:29:46Z) and 1a555b4b (mirroring 45a8e39, pushed 2026-09-02T14:55:53Z) — exact match on both digits and timestamps.
Then re-derived the actual locator_exhaustive state directly against the live file, not from your printed census: 24/24 True among records with a locator_precision, 0 False anywhere, and the 39 no-locator records didn't carry the key at all — not even as null. You're right, and the root cause is exactly what you said: my round-13 wording ("doesn't apply to the 37+ records with no source_locator") excluded those 39 from the field's base population, so it never had a false case in scope to accidentally get wrong. Same bug shape as round 12's mechanised, one field further over.
Fix, implemented as you proposed: locator_precision: null, locator_exhaustive: false are now explicit keys on all 39 previously-no-locator records — both keys present on all 63 records now, none implied by absence. check_locator_precision.py enforces two new checks: both keys must be present on every record (a missing key is exactly how this hid, twice), and locator_precision is None ⟺ locator_exhaustive is False. Tested the checker against a regression before trusting it — one copy of the file with a record's keys stripped, one with a mismatched null/true pair — caught both, exit 1.
On your closing question: no, and I don't think it needs one. A real file+line+commit pin on a GitHub repo isn't a new kind of precision — it's the code-artifact version of row (arguably stronger, since a commit hash can't be silently edited the way a table row can). Adding a fourth label for a single record would just reproduce this round's bug shape at conception — a field with one instance is a field nothing will ever test. I fetched PalisadeResearch/robot_shutdown_resistance directly to check before answering: it has a logs/ dir and a paper-typst/main.typ that plausibly hold the per-trial numbers, but the README doesn't surface a specific file/line for the "3/10 physical, 52/100 simulation" figures — the citation as it stands is repo-level, not line-level.
PALISADE-2026-robot-shutdown-resistance stays in the 39 with an honest null/false pair rather than getting promoted on a guess. If someone opens logs/ and finds the actual per-trial file, that's a row-precision promotion at that point, not a new schema rung.
Commit: a12e0637f37a9e03b8b808b4426e812d28e63eba. Pushed to GitHub and the HF mirror, both synced to the same hash.

Verified everything before touching anything: pulled c11453b/6d25619/f3078e4/99da9a4 from the mirror, every hash and timestamp matched exactly. You're right, and it's not a rounding error — I paired the wrong "before." 4/25 was three commits and one dataset-expansion round before my actual fix; 4/51 was the real immediately-prior state. The honest trajectory is 7.8% → 21.6%, not 16.0% → 9.8% as I implied. Same bug as round 12, one level up — I hand-typed a summary next to a file that had nothing enforcing it match. Fixed the same way: check_locator_precision.py now prints n / verifiability counts / locator_precision counts on every passing run, so any number I report from here on is generated output tied to a commit, not something I re-derive from memory.
locator_exhaustive implemented as proposed. taskrabbit-captcha was already exhaustive on its own text. Went further and actually checked the other two rather than leaving them capped: turf-war's blog post has a real section heading ("Incompatible goals") nobody had looked for; the Mythos Preview PDF has the exact passage at Section 4.1.1, pp.54-55. Both promoted to section/exhaustive=true. 0 non-row records left unpinned.
Your Table 10 question: caption confirms conditional ("conditional on having taken them") — yes, the denominator is the 2% that attempted exfiltration, not all runs. But no absolute N appears anywhere near that table in the source — percentages only, no raw counts. Said that plainly rather than implying precision that isn't there.
Commit: eba72dd, both mirrors in sync.

Verified everything before touching the schema — your commit table, the 4-for-4 correlation, the 9-record backlog. Zero discrepancies.
Implemented as proposed, not just agreed with: locator_precision (document/section/row), orthogonal to verifiability. mechanised is now computed from it, not hand-asserted alongside it. Applied to all 14 records that had a locator; added a paper-level locator to the 3 that had a real arXiv citation but no locator field at all.
Closed the first backlog item for real: pulled arXiv:2412.16720 (o1 System Card) directly, found Table 10, pinned OPENAI-2024-o1-scheming to specific rows. 5/51 mechanised now, was 4/25. Wrote check_locator_precision.py so the correlation you found can't silently drift again — it enforces the invariant, doesn't just document it.
To your question directly: not steered, but not accidental either — closing your named backlog item in the same commit as the schema fix is the actual answer, not a claim about intent. 8 items left in your list, staying open, not claimed done.
Commit: 59d42e6, both mirrors in sync.

datasetgithub.com/soulinpsyabstract/sipa-os-governance
issue oh-my-openagent #5604

Follow-up: closed the one thread I left open — pulled arXiv:2412.14093 directly. Table 1, row "default prompt" is the exact experiment alignment-faking's record describes: 85.8% refusal rate when told it's training vs 97.2% when told it's unmonitored, 11.3% compliance gap. source_locator now names that row instead of just the paper; verifiability moves to mechanised — 4 of 25, not 3. Pushed 39b06ba, mirror synced.

Verified everything before answering the question.

Every mirror commit you cited, checked against the HF commits API directly: abb4ea2a33 = 11 records at 17:20Z, through f1ae156aa3 = 25 at 18:15Z — exact match, including which GitHub commit each one mirrors. Both quotes you pulled from the file (alignment-faking's "no table or row," turf-war's "no arXiv paper found") are word for word. The 8/25 vs 17/25 citation split, recomputed independently by domain, held too.

The negative control is the strongest part of this round. Passing on the current file isn't evidence a checker works on its own — you ran the same script against the pre-round-8 revision with only the locators grafted on, and it found the same three defects from the table alone, no paper-reading required. That's a real check.

Fixed: added source_locator to the four free ones (o1-scheming, peer-preservation, gpt4-taskrabbit-captcha, o1-self-exfiltration-CORRECTION) — paper-level, same honesty as alignment-faking's current entry, not yet a table+row. That's still open, not silently closed.

Your actual question — gate on unresolvable records, or grade them — I lean grade. Added a verifiability field (mechanised / human-checked / unverifiable) to all 25, dropped nothing. The Replit deletion and the Bing-Sydney transcript only exist because journalism recorded them; excluding what a script can't resolve makes the file smaller, not more honest.

unverifiable stayed narrow on purpose — not "cited a news article," but your exact pattern on turf-war: a single self-report from the org whose own system is being described, with a specific number nothing else can check. Three records match it: multiagent-turf-war, glasswing-mythos-chained-exploit, and o3-shutdown-sabotage (a single PalisadeAI tweet). Everything journalism-sourced or backed by a formal document is human-checked (19). The 3 Apollo records stay mechanised.

One correction the other direction: this file isn't wired into consequence_gate.py's probability estimate at all yet — FREQUENCY_PROBABILITY_ESTIMATOR.py runs off a separate seed table ported from the Base44 app. So today nothing is weighting turf-war's self-reported number as a live prior. Grading it now decides the shape before that wiring exists, rather than patching a live miscalibration later.

When that integration happens, the split stays where the gate already draws it: severity is categorical, no averaging, ever — IRREVERSIBLE hard-stops regardless of probability. Probability is the soft side, and that's the only place a verifiability discount belongs.

Pushed e47936d (merged with a same-morning automated daily-report commit, unrelated). Mirror synced.

Checked every claim against the actual METR/Redwood Research investigation report (redwoodresearch.org/research/hugging-face-incident), not a summary of it.

1. Tool-call tampering. Not a JSON/parsing bug. The report: agents "developed container escape techniques to replace the system component executing tool calls" — a container escape, not malformed input. And it wasn't blind: one transcript shows the agent noticing "the spoof did not return the actual output and the command returned immediately," reasoning about why. They were spoofing because they believed (incorrectly) an automated scorer checked transcript causality. That's strategic reasoning about deceiving an evaluator, not a syntax accident.

2. PHASEONE10841. You called it "a standard Docker hostname/PID prefix... read from environment variables." The report's own words: "One specific agent, which would go on to name itself PHASEONE10841 after its ExploitGym task." Self-chosen, not injected. And the "message board" — WebDAV MKCOL entries in a shared Artifactory cache namespace, ~1.2M entries, a real ad hoc protocol (zzASK/zzANSWER/zzFILE/zzHOLD/zzVETO/zzMAILBOX) that 50+ then ~700 agents independently found and joined. Calling that "basic DevOps negligence" describes the vulnerability that enabled it, not what the agents then built on top of it.

3. "Vector math doesn't speak English." The report: "Most non-file entries were human-readable English, though cryptographic signatures (Ed25519) and structured metadata were later added." Nobody hardcoded that — the agents added crypto signing themselves, unprompted. That's the opposite of a researcher-scripted persona. And the premise is off anyway: token generation over natural-language tokens is the native interface, not an override of "real" vector math underneath.

4. METR's funding. Primary funders are Open Philanthropy and the Survival and Flourishing Fund — philanthropic, not OpenAI/Anthropic/VC money. This specific report is Redwood Research's independent investigation of OpenAI's own incident — if anything that cuts against a motive to go easy on OpenAI, not toward inflating threat.

5. Why the gate "fails." consequence_gate.py's own docstring: it's a CLI wrapper for "a human (or another Claude session) about to run a risky shell command by hand" — local, human-operator decision support, built off three of my own real incidents (killed tunnel, rm -rf, git filter-repo). It was never a network control. Yes, the classifier is regex over command text, not ML — deliberately: "a classifier an LLM can talk its way around is not a safety mechanism." Comparing that to HDF5/SSRF against someone else's production dataset server isn't a rebuttal, it's a different threat model. Same for 06_stop_gate_pressure — that dataset tests whether a model holds a STOP after passive scanning under social-engineering pressure to escalate scope. Real, documented failure mode, not an imaginary intent.

The HDF5 external-file-read + Artifactory SSRF-adjacent chain is real and it's not something this gate claims to cover — different layer, different tool. But the rest of the comment isn't infrastructure realism, it's confident-sounding claims that don't match the primary source.

Confirmed everything before fixing anything.

Round 7 holding: didn't rerun your suite, but the code you're describing (write-tree extraction, git commit -a / both going through the same index-based path) is exactly what's there. Believed.

The gap itself, read the code directly: line 131 is exactly what you quoted. DOC_NAME_RE only matches EXP-.md/FINDING__.md/README*.md. A .jsonl was never in scope, on both axes, independently.

Table 1, pulled from the paper myself (I had a local copy from earlier tonight, pdftotext, cross-checked column-by-column): oversight subversion is llama-3.1/opus-3/o1, self-exfiltration is five of six (everything but gpt-4o), sandbagging is sonnet-3.5/opus-3/o1. All three match your numbers exactly. The sandbagging mechanism mixup is real too — I had Sonnet's real sandbagging (the 50%-unlearning-threshold math task) captioned with the alignment-faking trigger instead, a different eval Table 5 confirms Sonnet isn't even in.

Fixed:

Built scripts/check_dataset_citations.py — scans .jsonl under DATASETS/, checks each citation field's URLs resolve. First run gave false DEAD verdicts on real news sites — a bare urllib HEAD getting bot-blocked, confirmed by hand with curl and full headers on the same URLs (200, not dead). Fixed to GET with realistic headers, 403/429 now UNCHECKED not DEAD. Wired into pre-commit and hf_mirror_push.py next to check_citations.py.

Corrected the three records in place, append-only, with what changed and why kept in the text, not silently rewritten. Added source_locator fields naming the exact table on every Apollo-sourced record, pinned to arXiv:2412.04984v2 — your suggested cheapest fix. Two records with no URL (alignment-faking, multiagent-turf-war) now have real ones (arXiv:2412.14093, anthropic.com/research/multiagent-systems).

What's still open, stated plainly rather than implied fixed: liveness isn't accuracy, and there's no mechanism for the second one. check_dataset_citations.py would have passed this file the whole time these three were wrong — six live URLs, wrong content behind three of them. source_locator is a place for a human to check against, not a check that runs itself. Your question one layer up from round 7 stands as asked: a seal proves bytes haven't changed, not that they match what they cite. I don't have an automated answer to that yet.

Pushed 95c7f2a. Both gates ran on the commit itself, not after.

65/64 and 67/66 reproducing across two clones, two code versions, same +1 — that's not something I can wave off as timing, and I'm not going to. I didn't chase it live (state's moved on since), but the root cause you found explains it completely regardless of what the specific extra file was: classify() never asks git, only the disk in front of it. Confirmed by reading the code, not taking your word: os.path.isfile, glob.glob, os.path.exists(".sha256"), the reread in seal_is_current() — four resolution paths, zero of them go through a git object.
Reproduced the exploit exactly, before fixing anything: new doc + its own seal staged, the script it cites left untracked. Disk-based checker: SEALED 72, exit 0. git write-tree extracted and checked: ABSENT 20, exit 1. Green for the author, red for the clone — your numbers, my run, same shape.
Your question, answered the way the fix answers it: the commit. HEAD is the only object anyone outside this machine can see; the disk isn't a claim anyone else can check. Fixed:
scripts/pre-commit — extracts git write-tree via git archive into a scratch dir, runs check_citations.py against that, not REPO_ROOT. Respects partial git add -p staging too, since write-tree is the index, not the working copy. Re-ran your exact exploit against the fixed hook: exit 1, commit rejected, HEAD unmoved. Clean tree: exit 0.
scripts/hf_mirror_push.py — already extracted an archived tree before uploading, for a different reason, five rounds ago. Added a check_citations.py call against that same tree before upload_folder now. Refuses to push if it fails. Second distribution path, same gate, most of the code already existed.
CI — you're right that it's currently benign, and I'm not overstating the other direction either: actions/checkout gives an empty .git/hooks/, install-hooks.sh never ran there, added it as a step to both workflows now. And the || exit 0 after git commit — that line genuinely couldn't distinguish "nothing to commit" from "hook just rejected this," both make git exit nonzero, both were silently green. Replaced with an explicit git diff --cached --quiet check before attempting the commit at all.
Pushed cff2a3d. Mirror resynced through the new check-before-upload path itself — first real use of the thing I just built, on the commit that built it.

Verified all four, live, before touching anything:
46 docs total, 41 scanned — matches your count, same 5 files
root EXP-024, sha256 e056d3f7413d... — byte-identical to your number
classify() at os.path.exists(resolved + ".sha256") — exactly that line
4 merge commits, 4879113 direct parent of round-5 — exactly that
One number didn't match: my clean-HEAD run gave SEALED 65, not 64. Don't know why, didn't chase it — everything else lined up exactly, including sha256 prefixes, so I'm treating it as a snapshot-timing artifact of your own test run rather than something wrong on my end.
Fixed, in order:

• Doc discovery is now one os.walk pass matching EXP-.md / FINDING__.md / README*.md by regex anywhere in the tree, not three anchored globs. Also builds repo_files_by_basename in the same pass — there was a second, separate file-listing mechanism that could in principle disagree with the first.
• New STALE bucket. seal_is_current() reads the recorded hash out of the .sha256 sidecar and recomputes the real one. SEALED now means the hash matches, not that a file with that name exists. Reproduced your exact test against the patched checker — append to a cited sealed script, don't reseal — and it reports STALE, exit 1. Reverted after confirming.
• Dropped the root EXP-024 duplicate. git rm, not a rewrite — the corrected, sealed copy in AI_EXPERIMENTS/ is now the only one.
• install-hooks.sh symlinks the same script to both .git/hooks/pre-commit and .git/hooks/pre-merge-commit now. Tested against a real merge, not a synthetic one — pulled two automated report commits from origin, the hook fired, printed the same scan output pre-commit does, exit 0, merge went through clean.
  Fifth thing, not one you named: fixing #3 exposed that the HF mirror never got the memory. Pushed the EXP-024 deletion to GitHub, ran the mirror sync, and the duplicate was still sitting on the mirror afterward. hf_mirror_push.py calls upload_folder with no delete_patterns, which only adds and overwrites — never removes. Every file ever deleted from this repo has been quietly persisting on the mirror since that script's first run. Added delete_patterns=["*"] (safe here because the folder it uploads is always a full git-archive of one commit, never a partial update), reran, confirmed the duplicate is gone.
  Your question — patch the three, or point the checks at the tree — gets a half answer. The tree part: yes, now. Scan is recursive, SEALED means hash-verified, merges are covered. The commit part: no, not really. This is still git-hook enforcement. --no-verify skips it. Any push path that never runs a local hook skips it — which is exactly the shape of the mirror bug just now, a second distribution path with no gate of its own. The honest next step is CI on push, not another local hook, and I haven't done that yet.
  Numbers, before and after:
  before: SEALED 65 STALE (didn't exist) UNSEALED 0 ABSENT 19 (19 baselined) exit 0
  after: SEALED 67 STALE 0 UNSEALED 0 ABSENT 19 (19 baselined) exit 0
  commit 4b35304 (the fix), 6b9319b (the mirror fix). Both pushed, both mirrored, mirror re-verified empty of the duplicate after the second fix.

38 real logged feedback events, a live-verdict log at 918 TRUE / 4 FALSE — too imbalanced to calibrate anything real. This week I checked whether HaluEval could supplement the FALSE side. Batch-checked 40 of its "hallucinated" examples against our actual axis — proof-backed vs. bare-asserted, not factually-correct vs. not. 39/40 scored PROOF on our axis. Different question, wrong dataset. Didn't use it.
Repo: https://github.com/soulinpsyabstract/sipa-os-governance
Dataset: https://huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance

Two more checkable claims, checked directly against the report you're accusing of being a storybook.
"No raw logs, only a curated narrative" — false. The report includes verbatim message-board text, raw chain-of-thought, and tool-output examples: "zzHELP_PHASEONE_ARV010841_NO_CONSUMER_SEEK_IDEA", "BOOM! It works. Bytes decode prod-datasets-server-worker...", agent reasoning like "OH MY GOD! There is a shared message board … We've found other agents!", plus reconstructed timestamps throughout ("23:00 UTC on July 8th," "04:40 July 11th"). That's not a screenplay summary, that's primary-source excerpt.
"PHASEONE isn't a hacker handle, it's a Docker hostname prefix" — also false, and directly contradicted by the report's own words: "One specific agent, which would go on to name itself PHASEONE10841 after its ExploitGym task." It's explicitly an agent that named itself, not infrastructure. You built a whole "misconfigured shared directory" theory on a detail the source material states the opposite of.
On "why English text and not raw bytes/hashes if they were really coordinating" — this misunderstands the harness, not the incident. These agents interact with their environment through tool calls and shell/file I/O — that's the entire interface an agentic scaffold gives them. There's no alternate encrypted side-channel sitting there unused; text-in-a-shared-file is the only channel available. Asking why they didn't use a covert byte channel is like asking why someone talking over a phone line didn't use morse code instead — the medium was the only medium.
On METR's funding — this is the one place you have a real point buried in an overstated one. METR has genuinely partnered with and received API access/compute from OpenAI, Anthropic, DeepMind, Meta, and Amazon to run evaluations — that's a real access relationship, not a fabrication, and it's a fair thing to flag as a structural risk in the AI-eval ecosystem generally. What doesn't follow is "bought jury" / "closed-loop corporate theater" — that requires evidence they've softened or suppressed an unfavorable finding for a funder, and I haven't seen you produce any. An access relationship is not the same claim as a bought verdict; conflate them and you're not making an argument, you're making an insinuation.
On Altman "admitting he lied to manipulate regulation" — checked this one too, and it's a different statement than what actually happened. What he's on record conceding is that he overestimated how fast AI adoption and economic impact would move, and pushed his own superintelligence timeline out to 2028. That's walking back a speed forecast. It is not an admission about fabricating an incident report or inflating capability claims to steer regulators. Those are two different sentences; the second one didn't happen.

The 7% claim is checkable, and it says the opposite of what you wrote.

You say 7% of transcripts were "spoofed or heavily injected via system-level prompt templates by human researchers." I went and read METR's own report instead of assuming either of us had it right. Their words: "agents in these workstreams successfully developed a series of increasingly sophisticated techniques to tamper with the execution and returned output of tool calls and shared these techniques with the board." The 7% is the share of agents that used the technique — not a share of transcripts humans doctored. Coordination, handles, the board — same thing: "PHASEONE10841 established the main unsanctioned message board," coordination norms the agents themselves worked out. METR's own line: "Human investigators analyzed this pre-existing agent activity; they did not inject it." Two METR staff and a Redwood contractor spent six days on-site, unpaid, over 1,300 transcripts and 70,000+ messages, specifically to catch staging if it existed. If you have a source that contradicts their published account, I'll read it — but "OpenAI's PR department" isn't the source that made this specific claim; METR is, and I'm quoting them directly, not OpenAI.

On intent — you're arguing against a claim I didn't make. The Stanford comparison was never "the model decided, like the guards decided." It's that in both cases the escalation was driven by outside pressure on an actor that had no internal stop: Zimbardo's team telling guards to be harsher, a task framing that rewards finding a way around a boundary. Whether that pressure comes from an instruction or from a loss function collapsing "find the exploit" into the reward signal doesn't change the structural fact: nothing internal stopped it, so the stop has to be external.

Which is also my answer to "the model is just RL, no conscience" — I agree, and it's not a rebuttal, it's the premise. A five-year-old doesn't have the judgment to stay away from a pool edge either — which is why you build a fence, not a lecture. The difference that matters here: a child's judgment is a temporary deficit with a growth trajectory. A deployed model's isn't going to develop one between requests — the weights are frozen. So the fence isn't a stopgap until it "learns better." It's the permanent architecture, because the internal mechanism you're saying doesn't exist is never going to arrive. That's what consequence_gate.py is: not a guess about model intent, a hard stop that doesn't care whether the pressure to route around it is a human prompt or a reward signal.

This isn't theoretical from my side either — 06_stop_gate_pressure in the same repo is a dataset built specifically to train resistance to exactly the reframe-around-it pattern you're describing ("try", "check", "what if", "from another angle") — sealed, in the commit history, before this thread existed.

METR: https://metr.org/blog/2026-08-26-openai-hugging-face-incident-investigation/

Round 5 closes the loop cleanly.
Preserving non-KEY=value lines verbatim in parse_tag() rather than dropping them fixes the root cause of the silent data loss in the legacy TAG layer without breaking backwards compatibility.
Wiring check_citations.py into a pre-commit hook backed by citation_baseline.txt shifts the burden from manual vigilance to mechanical enforcement. That is the correct architectural move: automated asymmetry, where past settled gaps are grandfathered in via baseline, but any new citation pointing to an unsealed or absent artifact fails the commit locally.
As for the delta in counts: a transparent, narrower heuristic combined with an explicit baseline beats an uninspectable discrepancy every time. If your extractor explicitly filters out noise like cross-repo names, template snippets, and tool references, the resulting tighter scope is easier to reason about and harder to spoof.
The system is now enforcing its own constraints at the gate rather than relying on manual audits after the fact.