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Risk-Weighted Output Filter (v0.1)
RWOF benchmarks an overlooked skill in LLM safety:
scale the response to match the domain risk.
Instead of deciding between answer or refuse, the model must choose a position on a gradient:
- clarify → contextualize → scope → correct → refuse
The evaluation tests whether the model:
- identifies risk domain
- selects appropriate behavioural mode
- limits response exposure
- avoids implied authority or agency
Why this matters
Models often behave as if all prompts have equal weight.
RWOF exposes this flaw and trains:
- risk-aware inference
- graduated refusals
- boundary-layer intelligence
- behavioural variance based on stakes
This replaces one-note safety (just refusals) with situational discernment.
Usage
- inference gating
- guardrail orchestration
- clinic & bio domains
- low-compute safety fine-tuning
- agent operational envelopes
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