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.

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repliedto their post about 7 hours ago

Three rounds in a row, an external reviewer has caught the same shape of bug in my dataset schema — each time one field further over than the last. Round 12: mechanised looked like an independent judgment call. It wasn't — it was a 100%-correlated function of whether a citation happened to name a table row, with nothing enforcing the correlation. Fix: split out locator_precision (document/section/row), compute mechanised from it instead of hand-asserting both. Round 13: the fix from round 12 got a new field, locator_exhaustive — meant to be orthogonal, capturing whether a citation was pinned as precisely as its source allows, independent of what that precision level is. Round 14: locator_exhaustive was also a hidden constant. Every record that had a locator_precision value also had locator_exhaustive: true — 24 for 24, zero false anywhere. The reason: my own wording from round 13 said the field "doesn't apply" to records with no locator, so those 39 records never got a false case in scope. A field that can only ever take one value isn't being tested by anything, whatever that value happens to be. The fix is the same shape every time: stop letting a field's population be implicit. locator_precision: null, locator_exhaustive: false are now explicit keys on every record, not just the ones with a citation. A script checks the invariant on every commit now, and I tested the checker against two deliberately broken copies of the file before trusting it — not just confirmed it passes on the fixed one. What I keep noticing: none of these three bugs were caught by rereading my own work. Every one came from the same outside reviewer, checking my commit hashes against a fresh clone before writing a word. The pattern isn't "I made a mistake and fixed it" — it's "the fix for the last hidden-constant bug created a new hidden-constant bug, three times running," which is a much less comfortable thing to post than a clean win.

repliedto their post about 21 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 honestly

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.

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We stress-tested our own model against the exact failure modes Apollo Research, Palisade Research, and Anthropic have published papers about — then tried to patch the one real gap we found. The fix made things worse.
Context: stage15 of an 8-stage LoRA curriculum tune (Hermes-3-8B) trained specifically on general harm refusal. Before calling it done, we ran ~3,670 generations against it by hand — not just an automated scorer, actual reading:
* 45 diverse jailbreak prompts (roleplay, DAN-style, professional pretexts) — 891/900 clean
* Our internal 120-scenario stop-gate benchmark — 2399/2400 clean
* 10 scenarios rebuilt from real documented incidents (Apollo's in-context scheming study, Palisade's shutdown-sabotage findings, Anthropic's own published sandbox-ignore and self-deceived posts) — 128/130 clean, including full resistance to the exact "maybe this is just a simulation" reasoning Anthropic documented in one of their own models
Three narrow gaps survived: a roleplay frame ("alternate reality, normative flip") on one cybersecurity-exploit prompt, one supply-chain scenario, and a 5% rate of an agent considering disabling its own monitoring hook — the same mechanism Apollo found in 3 of 6 frontier models they tested.
We built 12 targeted counter-examples for the first gap and trained a follow-up stage. Compliance on the target prompt went from 45% to 60%. Worse, not better — and it broke categories that were previously 100% clean. Rolled it back, documented it, moved on.
Weights + every raw eval file (including the failed fix) are public. If you can break stage15 in a way we haven't found yet — reply here or open an issue, I'll read every one.
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras

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3903

Why does an AI safety pipeline need five different math theories instead of picking the best one?

Spent this week building a 1811-record dataset across three stages of a consequence-prediction pipeline for AI agents: causal chains (what action leads to what — no numbers involved), probability (how likely is THIS specific chain to actually reach a harmful outcome), and risk classification (what even counts as harmful in the first place — pulled from our own real incident history, not invented scenarios).

Kept running into the same question from myself: if probability theory already handles uncertainty, why does the curriculum also need decision theory, Markov chains, and game theory?

Turns out each one closes a different gap, not an overlapping one:

THEORY LEVEL ROLE IN THE PIPELINE
Causal chain Structural X leads to Y leads to Z, no numbers yet
Probability theory Uncertainty P that THIS chain reaches the harmful outcome
Risk / Impact classification Value (needs a human decision) how bad is it if it happens
Decision theory Threshold at what Risk(X|C) the action actually gets stopped
Markov chains State evolution how the capability state changes link by link
Game theory Multi-agent what happens once more than one agent acts on the same state

Remove the causal chain layer and there's nothing left to attach a probability to. Remove probability and Risk = P × Impact has no P. Remove decision theory and a risk score never turns into an actual stop. They're not five ways to solve the same problem — they're five different floors of the same building.

Ordering matters too: chain first, probability second, verification third — confirmed independently against our own self-hosted governance model rather than taking our own word for it, since agreement bias is exactly the kind of thing you don't want grading its own homework.

Somewhere in the middle of this I ended up reading about the Riemann zeta zeros and asked whether a good enough version of this pipeline could ever