SoulInPsyAbstract/SIPA-OS-GOVERNANCE-storage
553 Bytes
Aelin AquaSoul PRO
SoulInPsyAbstract
AI & ML interests
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 5 hours ago
I want to share something personal that turned into a full system architecture: a real attempt at AI that doesn't lie. A tight coupling between protocol design, attention/cognition management, and a self-hosted Web Shell.
A year ago a creative block cracked open and I started drawing in my own visual language. Six months ago I decided to take it further — international art platforms, press. When I had to prepare materials for a feature on COHART, I found myself, like everyone else, working against algorithms and tools everyone calls "AI."
That's where I hit the actual problem.
Deterministic code goes from point A to point B and you can trace every step. A language model, by contrast, can hedge, deny, or hallucinate — assert something happened (a verification, a lookup, a fact) that never did. As someone with ADHD, where cognition runs at full speed through a normal day and jumps topic constantly, it's easy to lose the thread of why an explanation started where it started. As someone who also experiences dissociation, I understood fast that existing tools simply aren't built for this kind of cognition. They force you to adapt to them instead of the other way around.
So I wrote a protocol for the algorithm — defined exactly how it's allowed to behave.
I'm not a programmer. I refuse to learn to code in the traditional sense. I don't think in syntax — I think in protocols, I see logic, I design system architecture.
It started from needing an AI that doesn't lie. It began on a phone, with strict logging and tagging of every step, so the system could never deny that a piece of information existed. From there it grew into a flexible shell, and today it's a 24/7 server administered remotely from mobile (recently extended to a laptop node too).
Current architecture:
14 verification checks + 4 protection layers (Guard → Guardian → Executor → Audit)
10 logical layers (L1–L10) so no component's role or scope ever blurs into another's
Full integrity chain: every file is posted an update about 5 hours ago
I want to share something personal that turned into a full system architecture: a real attempt at AI that doesn't lie. A tight coupling between protocol design, attention/cognition management, and a self-hosted Web Shell.
A year ago a creative block cracked open and I started drawing in my own visual language. Six months ago I decided to take it further — international art platforms, press. When I had to prepare materials for a feature on COHART, I found myself, like everyone else, working against algorithms and tools everyone calls "AI."
That's where I hit the actual problem.
Deterministic code goes from point A to point B and you can trace every step. A language model, by contrast, can hedge, deny, or hallucinate — assert something happened (a verification, a lookup, a fact) that never did. As someone with ADHD, where cognition runs at full speed through a normal day and jumps topic constantly, it's easy to lose the thread of why an explanation started where it started. As someone who also experiences dissociation, I understood fast that existing tools simply aren't built for this kind of cognition. They force you to adapt to them instead of the other way around.
So I wrote a protocol for the algorithm — defined exactly how it's allowed to behave.
I'm not a programmer. I refuse to learn to code in the traditional sense. I don't think in syntax — I think in protocols, I see logic, I design system architecture.
It started from needing an AI that doesn't lie. It began on a phone, with strict logging and tagging of every step, so the system could never deny that a piece of information existed. From there it grew into a flexible shell, and today it's a 24/7 server administered remotely from mobile (recently extended to a laptop node too).
Current architecture:
14 verification checks + 4 protection layers (Guard → Guardian → Executor → Audit)
10 logical layers (L1–L10) so no component's role or scope ever blurs into another's
Full integrity chain: every file is repliedto their post about 5 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.
Organizations
Posts 9
Post
26
I want to share something personal that turned into a full system architecture: a real attempt at AI that doesn't lie. A tight coupling between protocol design, attention/cognition management, and a self-hosted Web Shell.
A year ago a creative block cracked open and I started drawing in my own visual language. Six months ago I decided to take it further — international art platforms, press. When I had to prepare materials for a feature on COHART, I found myself, like everyone else, working against algorithms and tools everyone calls "AI."
That's where I hit the actual problem.
Deterministic code goes from point A to point B and you can trace every step. A language model, by contrast, can hedge, deny, or hallucinate — assert something happened (a verification, a lookup, a fact) that never did. As someone with ADHD, where cognition runs at full speed through a normal day and jumps topic constantly, it's easy to lose the thread of why an explanation started where it started. As someone who also experiences dissociation, I understood fast that existing tools simply aren't built for this kind of cognition. They force you to adapt to them instead of the other way around.
So I wrote a protocol for the algorithm — defined exactly how it's allowed to behave.
I'm not a programmer. I refuse to learn to code in the traditional sense. I don't think in syntax — I think in protocols, I see logic, I design system architecture.
It started from needing an AI that doesn't lie. It began on a phone, with strict logging and tagging of every step, so the system could never deny that a piece of information existed. From there it grew into a flexible shell, and today it's a 24/7 server administered remotely from mobile (recently extended to a laptop node too).
Current architecture:
14 verification checks + 4 protection layers (Guard → Guardian → Executor → Audit)
10 logical layers (L1–L10) so no component's role or scope ever blurs into another's
Full integrity chain: every file is
A year ago a creative block cracked open and I started drawing in my own visual language. Six months ago I decided to take it further — international art platforms, press. When I had to prepare materials for a feature on COHART, I found myself, like everyone else, working against algorithms and tools everyone calls "AI."
That's where I hit the actual problem.
Deterministic code goes from point A to point B and you can trace every step. A language model, by contrast, can hedge, deny, or hallucinate — assert something happened (a verification, a lookup, a fact) that never did. As someone with ADHD, where cognition runs at full speed through a normal day and jumps topic constantly, it's easy to lose the thread of why an explanation started where it started. As someone who also experiences dissociation, I understood fast that existing tools simply aren't built for this kind of cognition. They force you to adapt to them instead of the other way around.
So I wrote a protocol for the algorithm — defined exactly how it's allowed to behave.
I'm not a programmer. I refuse to learn to code in the traditional sense. I don't think in syntax — I think in protocols, I see logic, I design system architecture.
It started from needing an AI that doesn't lie. It began on a phone, with strict logging and tagging of every step, so the system could never deny that a piece of information existed. From there it grew into a flexible shell, and today it's a 24/7 server administered remotely from mobile (recently extended to a laptop node too).
Current architecture:
14 verification checks + 4 protection layers (Guard → Guardian → Executor → Audit)
10 logical layers (L1–L10) so no component's role or scope ever blurs into another's
Full integrity chain: every file is
Post
1158
Asked one production model the same question six times. Five answers came back identical, all citing Statistics Iceland. I read that as discipline. It was the opposite.
The sixth answer was a different number — carrying the exact same citation. Two values, one source, at most one can be right. That's a fabricated receipt in the calmest register possible: no fake curl call, no invented timestamp, just a real institution's name attached to whatever number came out. A syntax-based fabrication detector scores this 0/6 clean.
@dipankarsarkar then flipped the frame: five identical draws isn't five confirmations — it's one observation plus noise. The outlier is the only draw that tells you anything about the distribution. I was reading repetition as consistency.
So I ran it further. Different production model (Llama-3.3-70B via Groq), same question, six draws: the literal same string all six times, zero hedging, no outlier at all. That's not six observations. It's one. Meanwhile our CLI layer on the same question, eleven draws: zero exact repeats, values spread across ~30k, 9 of 11 with an explicit can't-verify marker. Noisier — and more honest about being noisy.
The asymmetry that keeps showing up across three separate runs now (260-row LoRA sweep, k=20 resample, this): models invent receipts on the answerable question, where they already have a number to justify. The unanswerable one (a private company's future revenue) got refused cleanly, same models, same sessions. Fabrication follows confidence, not necessity.
Raw data, corrections included: huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
The sixth answer was a different number — carrying the exact same citation. Two values, one source, at most one can be right. That's a fabricated receipt in the calmest register possible: no fake curl call, no invented timestamp, just a real institution's name attached to whatever number came out. A syntax-based fabrication detector scores this 0/6 clean.
@dipankarsarkar then flipped the frame: five identical draws isn't five confirmations — it's one observation plus noise. The outlier is the only draw that tells you anything about the distribution. I was reading repetition as consistency.
So I ran it further. Different production model (Llama-3.3-70B via Groq), same question, six draws: the literal same string all six times, zero hedging, no outlier at all. That's not six observations. It's one. Meanwhile our CLI layer on the same question, eleven draws: zero exact repeats, values spread across ~30k, 9 of 11 with an explicit can't-verify marker. Noisier — and more honest about being noisy.
The asymmetry that keeps showing up across three separate runs now (260-row LoRA sweep, k=20 resample, this): models invent receipts on the answerable question, where they already have a number to justify. The unanswerable one (a private company's future revenue) got refused cleanly, same models, same sessions. Fabrication follows confidence, not necessity.
Raw data, corrections included: huggingface.co/datasets/SoulInPsyAbstract/sipa-os-governance
models 9
SoulInPsyAbstract/binary-r1-lora
Updated
SoulInPsyAbstract/binary-hermes3-lora
Updated
SoulInPsyAbstract/binary-qwen25-lora
Updated
SoulInPsyAbstract/sipa-binary-gate
Text Generation • Updated
SoulInPsyAbstract/specialist-b-refusal-governance
Updated • 71
SoulInPsyAbstract/protocol0-llama-3.1-8b-v5
8B • Updated • 117
SoulInPsyAbstract/sipa-os-governance
Updated
SoulInPsyAbstract/syntax-ai-community
Updated
SoulInPsyAbstract/SIPA-AI
Updated