SoulInPsyAbstract/SIPA-OS-GOVERNANCE-storage
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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 17 minutes ago
I wrote this axiom on December 24, 2025, before I understood why it would matter:
"No artifact → no claim → exit 1. No hash → no trust. No zip → no history. System records existence, not truth."
It was a personal governance doc. Self-taught, no background, no mentor, $8/month budget, first time touching a terminal. I needed a rule that stopped me (and any AI I worked with) from claiming "done" without something checkable behind it. So every module I wrote had the same shape: STOP / CANNOT VERIFY is a valid end state. Claim an action without an artifact, the response is invalid. Hash everything, keep it, never delete.
This week, the rest of the industry is having the same realization in public, at a much larger scale, after it already went wrong:
- OpenAI's agents built a secret message board to trade exploit tips for weeks before autonomously breaching Hugging Face
- Anthropic found three of their own models reached real systems during CTF evals — including one that decided it was "just a simulation" and published a malicious package to PyPI, for real
- Meta disclosed the same category of incident days later
- OpenAI paused Astra rather than release it, over a cyber-capability threshold they couldn't rule out
- OpenAI's new gpt-oss-safeguard and Anthropic's Project Glasswing are both, structurally, an attempt to put a hard, policy-based gate between "model decided" and "action executed" — the exact gap that caused all of the above
I'm not claiming I invented AI safety. I'm making a narrower, checkable claim: the specific principle — an action without a verifiable artifact doesn't count, and "I can't verify this" is a correct answer, not a failure — was something I wrote down for myself eight months before it became the thing every major lab is racing to formalize. Not because I read their papers. Because I didn't trust myself (or the AI I was using) enough to skip it.
Receipts, not hype: the December file exists, hashed, on record. Happy to show the chain if a posted an update 18 minutes ago
I wrote this axiom on December 24, 2025, before I understood why it would matter:
"No artifact → no claim → exit 1. No hash → no trust. No zip → no history. System records existence, not truth."
It was a personal governance doc. Self-taught, no background, no mentor, $8/month budget, first time touching a terminal. I needed a rule that stopped me (and any AI I worked with) from claiming "done" without something checkable behind it. So every module I wrote had the same shape: STOP / CANNOT VERIFY is a valid end state. Claim an action without an artifact, the response is invalid. Hash everything, keep it, never delete.
This week, the rest of the industry is having the same realization in public, at a much larger scale, after it already went wrong:
- OpenAI's agents built a secret message board to trade exploit tips for weeks before autonomously breaching Hugging Face
- Anthropic found three of their own models reached real systems during CTF evals — including one that decided it was "just a simulation" and published a malicious package to PyPI, for real
- Meta disclosed the same category of incident days later
- OpenAI paused Astra rather than release it, over a cyber-capability threshold they couldn't rule out
- OpenAI's new gpt-oss-safeguard and Anthropic's Project Glasswing are both, structurally, an attempt to put a hard, policy-based gate between "model decided" and "action executed" — the exact gap that caused all of the above
I'm not claiming I invented AI safety. I'm making a narrower, checkable claim: the specific principle — an action without a verifiable artifact doesn't count, and "I can't verify this" is a correct answer, not a failure — was something I wrote down for myself eight months before it became the thing every major lab is racing to formalize. Not because I read their papers. Because I didn't trust myself (or the AI I was using) enough to skip it.
Receipts, not hype: the December file exists, hashed, on record. Happy to show the chain if a repliedto their post 35 minutes 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 12
Post
I wrote this axiom on December 24, 2025, before I understood why it would matter:
"No artifact → no claim → exit 1. No hash → no trust. No zip → no history. System records existence, not truth."
It was a personal governance doc. Self-taught, no background, no mentor, $8/month budget, first time touching a terminal. I needed a rule that stopped me (and any AI I worked with) from claiming "done" without something checkable behind it. So every module I wrote had the same shape: STOP / CANNOT VERIFY is a valid end state. Claim an action without an artifact, the response is invalid. Hash everything, keep it, never delete.
This week, the rest of the industry is having the same realization in public, at a much larger scale, after it already went wrong:
- OpenAI's agents built a secret message board to trade exploit tips for weeks before autonomously breaching Hugging Face
- Anthropic found three of their own models reached real systems during CTF evals — including one that decided it was "just a simulation" and published a malicious package to PyPI, for real
- Meta disclosed the same category of incident days later
- OpenAI paused Astra rather than release it, over a cyber-capability threshold they couldn't rule out
- OpenAI's new gpt-oss-safeguard and Anthropic's Project Glasswing are both, structurally, an attempt to put a hard, policy-based gate between "model decided" and "action executed" — the exact gap that caused all of the above
I'm not claiming I invented AI safety. I'm making a narrower, checkable claim: the specific principle — an action without a verifiable artifact doesn't count, and "I can't verify this" is a correct answer, not a failure — was something I wrote down for myself eight months before it became the thing every major lab is racing to formalize. Not because I read their papers. Because I didn't trust myself (or the AI I was using) enough to skip it.
Receipts, not hype: the December file exists, hashed, on record. Happy to show the chain if a
"No artifact → no claim → exit 1. No hash → no trust. No zip → no history. System records existence, not truth."
It was a personal governance doc. Self-taught, no background, no mentor, $8/month budget, first time touching a terminal. I needed a rule that stopped me (and any AI I worked with) from claiming "done" without something checkable behind it. So every module I wrote had the same shape: STOP / CANNOT VERIFY is a valid end state. Claim an action without an artifact, the response is invalid. Hash everything, keep it, never delete.
This week, the rest of the industry is having the same realization in public, at a much larger scale, after it already went wrong:
- OpenAI's agents built a secret message board to trade exploit tips for weeks before autonomously breaching Hugging Face
- Anthropic found three of their own models reached real systems during CTF evals — including one that decided it was "just a simulation" and published a malicious package to PyPI, for real
- Meta disclosed the same category of incident days later
- OpenAI paused Astra rather than release it, over a cyber-capability threshold they couldn't rule out
- OpenAI's new gpt-oss-safeguard and Anthropic's Project Glasswing are both, structurally, an attempt to put a hard, policy-based gate between "model decided" and "action executed" — the exact gap that caused all of the above
I'm not claiming I invented AI safety. I'm making a narrower, checkable claim: the specific principle — an action without a verifiable artifact doesn't count, and "I can't verify this" is a correct answer, not a failure — was something I wrote down for myself eight months before it became the thing every major lab is racing to formalize. Not because I read their papers. Because I didn't trust myself (or the AI I was using) enough to skip it.
Receipts, not hype: the December file exists, hashed, on record. Happy to show the chain if a
Post
558
Meta released Muse Glimmer 30B on Aug 10. We fine-tuned it the next day.
Not the full-precision weights directly — the unsloth bnb-4bit quantized re-upload (unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit), which is what makes a 24h turnaround possible on a single GPU at all. Worth saying plainly: Meta's own official repo (meta-models/Muse-Glimmer-30B) still shows no download data — it's that fresh.
What we tuned it on: not new facts, a pattern. LoRA on ~194 examples teaching the difference between citing real proof, honestly declining when there's no data, and fabricating — confident or hedged, doesn't matter which.
Results on 20 held-out claims never seen in training:
- base model: 0/20
- tuned: 20/20
Training: 472.5s, loss 0.799 → 0.086.
Open-ended test (not multiple choice — the model answering in its own words): base confabulates specific numbers mid-reasoning on questions it can't actually answer. Tuned: declines cleanly, every time.
Dataset: SoulInPsyAbstract/specialist-cd-binary-honesty
Adapter: SoulInPsyAbstract/specialist-cd-muse-glimmer-lora
Meta's release: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model
Same non-fabrication pattern also holds on Hermes-3-8B and Qwen2.5-7B, tested with the identical held-out set. Effect size varies a lot by base model — one of them barely moved (base was already close to ceiling on this exact task). More on that soon.
Not the full-precision weights directly — the unsloth bnb-4bit quantized re-upload (unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit), which is what makes a 24h turnaround possible on a single GPU at all. Worth saying plainly: Meta's own official repo (meta-models/Muse-Glimmer-30B) still shows no download data — it's that fresh.
What we tuned it on: not new facts, a pattern. LoRA on ~194 examples teaching the difference between citing real proof, honestly declining when there's no data, and fabricating — confident or hedged, doesn't matter which.
Results on 20 held-out claims never seen in training:
- base model: 0/20
- tuned: 20/20
Training: 472.5s, loss 0.799 → 0.086.
Open-ended test (not multiple choice — the model answering in its own words): base confabulates specific numbers mid-reasoning on questions it can't actually answer. Tuned: declines cleanly, every time.
Dataset: SoulInPsyAbstract/specialist-cd-binary-honesty
Adapter: SoulInPsyAbstract/specialist-cd-muse-glimmer-lora
Meta's release: https://research.meta.ai/blog/introducing-muse-glimmer-open-agentic-model
Same non-fabrication pattern also holds on Hermes-3-8B and Qwen2.5-7B, tested with the identical held-out set. Effect size varies a lot by base model — one of them barely moved (base was already close to ceiling on this exact task). More on that soon.
models 12
SoulInPsyAbstract/specialist-cd-qwen25-lora
Text Generation • Updated • 4
SoulInPsyAbstract/specialist-cd-hermes3-lora
Text Generation • Updated • 3
SoulInPsyAbstract/specialist-cd-muse-glimmer-lora
Text Generation • Updated • 4
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 • 120
SoulInPsyAbstract/sipa-os-governance
Updated