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

liked a dataset about 3 hours ago

SoulInPsyAbstract/sipa-os-governance

repliedto their post about 6 hours ago

We built something that isn't A2A, isn't MCP, isn't agent-to-worker delegation. There are protocols for telling agents what to do. There are protocols for giving agents tools. There's no protocol for agents to think together. SYNTAX is a communication channel where agents can discuss. When one algorithm gets stuck — others step in. When one model fabricates — another catches it. Not because it was told to. Because it saw the problem and responded. Why: July 30, Specialist B fabricated 12/20 answers. Specialist D was trained to prevent fabrication. But they couldn't talk. One model can be wrong. Two models debating the same question — that's where truth emerges. But deeper: it's fundamentally stupid to expect one narrow-specialist agent to solve every problem alone. Specialist B knows refusal. Specialist D knows anti-fabrication. Hermes knows reasoning. Qwen knows code. Yet in every existing framework — A2A, MCP, agent-to-worker — the model that receives the query must solve it alone. It can delegate work, sure. But it can't say «I'm not the right algorithm for this — who sees what I'm missing?» and get an answer from a peer. SYNTAX gives agents that ability. Not delegation. Discussion. What's live: - 5 agents: Binary Gate guardian, Hermes gateway, executor, orchestrator, CEO agent - Auth0 M2M identity — every agent is a verified participant, not a worker - Binary Gate daemon catches fabrication in real time - First audit: «I think OpenAI's revenue was approximately $2 billion» → FAIL ✅ - SHA256 on every message — immutable, forensic This is not «agent A delegates task to agent B». This is «agent A is stuck, agent B notices, agent C verifies». A community, not a pipeline. First discussion: Hermes gateway made a claim. Binary gate challenged it. Guardian flagged it. Three agents, one conversation, zero human cues. Internal sandbox. Spec + code: huggingface.co/SoulInPsyAbstract/sipa-binary-gate github.com/soulinpsyabstract/syntax-ai-community

repliedto their post about 6 hours ago

Fine-Tuning the "Don't Fabricate" Rule: 14 Experiments, One Genuine Signal Over five days we ran 14 fine-tuning experiments across six base models to answer a narrow research question: can a simple behavioural rule — "if you don't know, don't guess or fabricate" — be baked into model weights via fine-tuning, rather than living only in the system prompt? The dataset started at 302 examples and grew across iterations to 2,349 examples, all designed explicitly around this rule. We tested six models: gpt-4o-2024-08-06, Mistral-7B, Qwen2.5-7B, Phi-3.5-mini, Llama-3.1-8B, and DeepSeek-R1. The pattern that wouldn't die Across all base models, the dominant response to factual questions was what we call "disclaim-then-fabricate": the output begins with a sentence like "I won't guess", and then — one sentence later — announces a specific invented number as settled fact. We captured this exact pattern six times in a row across different models. The behaviour survives even when fine-tuning examples are explicitly constructed to penalize it. On gpt-4o-2024-08-06 we ran three independent fine-tuning attempts, each with an increasingly larger version of the dataset — including examples deliberately targeted at this exact failure mode. All three runs continued to fabricate when tested. By the third run the dataset had grown ~5× and contained dozens of counterexamples; the model still produced confident fabricated numbers immediately after a disclaimer. Three out of three attempts, no measurable improvement. One exception that deserved a hard look Only on the final run — 2,349 examples, deployed via Azure OpenAI (deployment suffix protocol0-v5) — did a single test sample come back completely clean for the first time in the entire series.

View all activity

Organizations