Semantic Firewall v3: A Practical Audit Layer for AI
I built an experimental audit layer for AI systems called Semantic Firewall v3, designed to reduce hallucination, lower compute waste, and enforce responsibility chains in model outputs.
The idea is simple:
1. Not every input should reach a large model.
Most hallucination + cost explosion comes from sending everything into a 7B–400B model without pre-classification.
2. A lightweight semantic parser can decide:
Should this be answered with a template?
Should this be blocked?
Should this require a model?
Should this be escalated?
3. When the model is used, it must produce an auditable chain:
subject (who speaks)
cause (why)
boundary (where risk is)
knowledge source
responsibility
This reduces hallucination and prevents “AI says something but no one knows why”.
Key Concepts
d(0): Zero-Point Decision
Before an output is accepted, the system must be able to show the decision boundary that produced it.
0/1 Convergence (Not mathematical π)
This is a governance idea:
every output should converge to 0 = reject / cannot justify,
or 1 = exists / can be explained.
This prevents “probability fog” outputs with no accountability.
888π (Responsibility Constant)
This is not physics — it's a mnemonic I use:
“the system must commit to the consequences of its output.”
If a model cannot justify an answer, the firewall returns 0.
Why I’m sharing it here
Governance institutions (OECD, NIST, WEF) rarely adopt new tech quickly.
Enterprises hesitate.
Cloud vendors avoid anything that reduces compute usage.
But HN engineers actually try things.
You test, break, measure, critique.
This is why I think HN is the right place to share this layer.
If this community wants:
lower inference cost
fewer hallucinations
more predictable model behavior
auditable output chains
I would like feedback.
Demo
Semantic Firewall System v3
https://hijo790401.github.io/semantic-firewall-system/�
It shows:
risk classification
responsibility chains
semantic decision paths
safe / risk / escalate tiers
This is early-stage work, but functional.
Contact
If anyone here wants to review, test, or critique:
[email protected]
I’m happy to open the full architecture, benchmarks, and discuss how to extend it for:
agent safety
enterprise audit trails
compute optimization
LLM hallucination suppression
Thanks for reading.
Technical criticism is welcome — especially from people who want AI systems to be auditable rather than magical.