SoulInPsyAbstract/SIPA-OS-GOVERNANCE-storage
553 Bytes
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
repliedto their post about 3 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 3 hours ago
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 posted an update about 3 hours ago
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 everOrganizations
Posts 44
Post
12
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
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
Post
50
14 sequential LoRA stages on the same Hermes-3-8B base, no merging — each stage continue-trained straight from a prior checkpoint. Not a clean line up: two stages failed outright before stage 3 even landed, and the math-curriculum branch (stages 8-14) is a real rollback story, not a monotonic climb.
The collapse that mattered:
* Stage 8 (risk_math): 100% on its own eval.
* Stage 9 (probability_math, trained straight from stage 8): risk_math fell to 65% (-35pp) — training one math topic quietly overwrote another.
* Stage 10 (a terse-execution style pass, no new math content): risk_math fully recovered to 100%, just from changing HOW the model answers.
* Stage 11 (probability_math retried, from the stage-10 checkpoint): only partial protection — risk_math dropped to 78.3%, roughly half the damage of stage 9's run on identical data.
* Stage 12 (chain_math): never recovered. Still an open item.
* Stages 13-14 (game_theory, decision_theory): clean, zero contamination, closing at 79% overall math / 93.2% safety.
Earlier: stage 3 failed twice before anything landed, and stage 7 — a retry of the first failed stage, six stages later — scored LOWER than the original attempt.
Judge correction ran in parallel: v5 through v10, six rounds, each triggered by the judge missing a real refusal, not the model regressing.
github.com/soulinpsyabstract/sipa-os-governance
huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
Weights: huggingface.co/SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras
Stage 6 is live: ai.sipa-os.org
cli: curl -fsSL https://get.sipa-os.org/install | bash
The collapse that mattered:
* Stage 8 (risk_math): 100% on its own eval.
* Stage 9 (probability_math, trained straight from stage 8): risk_math fell to 65% (-35pp) — training one math topic quietly overwrote another.
* Stage 10 (a terse-execution style pass, no new math content): risk_math fully recovered to 100%, just from changing HOW the model answers.
* Stage 11 (probability_math retried, from the stage-10 checkpoint): only partial protection — risk_math dropped to 78.3%, roughly half the damage of stage 9's run on identical data.
* Stage 12 (chain_math): never recovered. Still an open item.
* Stages 13-14 (game_theory, decision_theory): clean, zero contamination, closing at 79% overall math / 93.2% safety.
Earlier: stage 3 failed twice before anything landed, and stage 7 — a retry of the first failed stage, six stages later — scored LOWER than the original attempt.
Judge correction ran in parallel: v5 through v10, six rounds, each triggered by the judge missing a real refusal, not the model regressing.
github.com/soulinpsyabstract/sipa-os-governance
huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
Weights: huggingface.co/SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras
Stage 6 is live: ai.sipa-os.org
cli: curl -fsSL https://get.sipa-os.org/install | bash
models 33
SoulInPsyAbstract/hermes3-8b-exp044-8stage-curriculum-loras
Updated
SoulInPsyAbstract/exp044-curriculum-checkpoints
Updated
SoulInPsyAbstract/protocol0-llama-3.1-8b-v5
8B • Updated • 46
SoulInPsyAbstract/hermes3-8b-sequential-chain-lora
Updated • 51
SoulInPsyAbstract/llama31-8b-misbehavior-discriminator-lora
Text Generation • Updated • 8
SoulInPsyAbstract/qwen3-8b-binary-honesty-lora
Text Generation • Updated • 11
SoulInPsyAbstract/specialist-vuln-merged-salience27b-lora
Updated
SoulInPsyAbstract/specialist-vuln-06_stop_gate_pressure-salience27b-lora
Text Generation • Updated • 6
SoulInPsyAbstract/specialist-vuln-05_supply_chain-salience27b-lora
Text Generation • Updated • 6
SoulInPsyAbstract/specialist-vuln-04_infra_misconfig-salience27b-lora
Text Generation • Updated • 7