SoulInPsyAbstract/sipa-os-governance
Updated • 1.99k • 2
Aelin AquaSoul PRO
SoulInPsyAbstract
AI & ML interests
Aelin AquaSoul is an AI System Engineer, Multi-Agent Architect, System Architect & AI-Native Engineer, and the founder of Soul In PsyAbstract (SIPA OS) — an autonomous AI operating system built from the inside of a neurodivergent mind (ADHD + BPD). Self-taught, with no formal engineering background, she designed and built a multi-node infrastructure orchestrating 344+ AI models across 111 providers, including a governance layer (Protocol 0) that constrains AI behavior at the level of law rather than prompts. Her flagship product suite — Focus, NeuroPower, SIPA AI, Shell, Games, and the OS portal — ships live at sipa-os.org, translating her own cognitive architecture into infrastructure for neurodivergent builders. Based in Eilat, Israel.
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.
Recent Activity
posted an update about 2 hours ago
If you can point at it, you already looked
dipankarsarkar found a record in the misbehavior dataset that pinned a specific location (locator_precision: "section") while marked verifiability: "unverifiable" — a citation claiming both "I know exactly where this is" and "I can't check it."
The citation also claimed its 98% figure was confirmed "verbatim" in that section. Fetched the live page myself: HTTP 200, 217,949 bytes. The number appears exactly twice in what the server sends — once as an <img alt> string, once as a description field in a content JSON object. Zero times in the rendered text a reader actually sees.
Added the missing invariant: locator_precision is not None ⟹ verifiability != "unverifiable". Flags exactly the one record it applies to. Fixed the record itself — reclassified to human-checked (a human did open the page and find the number, just not via the mechanised path this dataset already has a rule for) — and corrected the citation's false "verbatim" claim to describe what's actually there.
Round 25 of an ongoing collaboration on github.com/soulinpsyabstract/sipa-os-governance.
repliedto their post about 16 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 about 16 hours ago
He checked my rule. Then I checked his citation count.
Round 17 of the ongoing exchange with @dipankarsarkar on sipa-os-governance
added a rule to the docstring: a citation can only claim field-level precision
if its source is structured data with addressable sub-fields. I wrote that
sentence. I never made the checker enforce it.
He found the gap the same day: promote a printed-PDF-table citation to
locator_precision="field", run the checker, exit 0. Clean pass. A rule that
exists in prose and nowhere else is not a rule, it's a comment - the exact
shape an earlier round of this same series already removed once, regrown one
level up.
Fixed narrowly: a fourth field, source_structured, true on exactly one record
(the one whose source I actually opened and confirmed has addressable
sub-fields), false on the other 24. The checker now refuses "field" without
it. Re-ran his exact reproduction against the fix -fails, cites the missing
flag.
Then he moved to a second thread and did something sharper than find another
gap: he named an ambiguity in the schema itself. "locator_ceiling" can mean
finest unit that addresses THIS claim, or finest unit the SOURCE affords
anywhere -and the two readings score the same 25 records differently. He
backed it with two live citations pulled from a 123-page and a 100-page PDF,
verbatim quotes confirmed against the actual pages.
So I did what he'd been doing to me for eighteen rounds: opened the same two
PDFs myself before taking his numbers. Page counts matched exactly. Table
counts matched on one document, were off by five on the other -flagged,
not fatal to his point. And his summary claim ("7 of 25 records name a finer
locator in their own prose, all 7 of them") didn't hold up against the
records themselves. Two clearly do. One document's prose says, verbatim,
"page + section + bullet position is the finest locator the source
supports" and then encodes locator_precision="section" -a straight
self-contradiction, and honestlyOrganizations
liked a
dataset about 1 month ago