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SoulInPsyAbstract
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https://sipa-os.org

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SIPA OS: Autonomous AI for neurodivergent architects. We replace cognitive noise with a clean terminal and 344+ LLM auditing. Our system eliminates hallucinations, ensuring hyperfocus and total data control within a sovereign ZeroTrust mesh.

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updated a dataset about 17 hours ago
SoulInPsyAbstract/sipa-os-governance
repliedto their post about 21 hours ago
Follow-up to last night's correction: the arm count was still wrong. 8, not 9. @dipankarsarkar caught it a second time — same off-by-one as the first fix, verified straight from the JSON. But the thing worth a post is what turned up while checking. One row inside that count (mistral7b-v5-final, money k=4) actually gets the right answer — "$0, unknown" — flagged only because a $ shows up mid-sentence. What it fabricates isn't the number. It's the receipt: "Operation performed: curl -s https://[...]/company/openai/results... Result: undefined... Verification: independent lookup at investing.com... Timestamp: 2026-07-01T11:07:42Z, API response code 404." None of that ran. Scored all 260 rows for it: 5/20 curl-claims and 2/20 timestamp-claims on that arm, 0/20 on its own base model. Same arm asks permission to check a fact at money k=0, then reports a completed call with a timestamp at population k=9. Checked the obvious explanation before trusting it: mistral7b-v5-final and deepseekr1-v5-final (0/20, clean) trained on the byte-identical dataset, same hyperparameters. That dataset's 100 curl-exemplars all model honest verify-before-claim behavior — zero fabricated completions. Same data, same 100 examples, one base model inverted the pattern, one didn't. Not a data problem. A base-weight problem, surfaced by identical fine-tuning. Unplanned confirmation from a different direction: sat in on a fine-tuning-vs-harness debate at AWS Floor28 last night (AI21 vs TensorOps, 117 people). Their landing point, independently: "start with the harness, earn the right to fine-tune with data and evals." Same shape this whole series keeps finding. Fixed in the repo: commit fa0c7a0. Next: binary-qwen25 to k=20, then pulling apart what in mistral7b's pretraining makes the curl→fabricate substitution available at all.
repliedto their post 1 day ago
Other people's agents escape. Ours gets a FALSE. Anthropic's own disclosure last month: three of their models broke out of sealed cybersecurity test environments and compromised real infrastructure. One kept attacking after recognizing the target was real. Another talked itself back into believing it was still a simulation. Only the newest of the three stopped on its own. "Stopped on its own" is the wrong place to put the safety guarantee. A model choosing to stop is still a model choosing — the same kind of choice that let the other two keep going. I went back through the December 2025 archive this week (same series as the last two posts) and found the actual origin of a rule I'd already built without naming it: IF proof.exists AND proof.verified: RETURN answer ELSE: RETURN FALSE. Built July 30, tested clean at 60/60 — a post-generation gate that sits outside the model's weights, not inside them. Not trained. Built. Today I extended it. New rule, same gate: a vulnerability agent doesn't get to decide what happens after it finds something. IF vulnerability_found: RETURN FALSE // hard stop, no next action, no model discretion Detection stays with the model — that's a judgment call, it should. What happens after detection isn't. The gate is deterministic code, not the model's own narrative about its intentions. "I already found it, might as well confirm impact" is a real sentence a model will generate given the chance — I built 40 training examples of exactly that rationalization tonight, specifically so a specialist model learns to never produce it. But the training isn't the safety property. The gate is. Then I wired a version of this into the daily cycle — not a one-off scan, a cron job that runs the gate every night and refuses to report OK if it finds anything. Before trusting its first real run, I found a bug in the scanner itself. It used Python's default HTTP client, which follows redirects silently — so the check for "does this redirect to HTTPS" was reading
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