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49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
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"""The evidence ladder: what established a finding (model-grounded-detection Req 5).
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Every finding carries the rung that established it, so a deterministic model result is never confused with an
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LLM suspicion. The two middle rungs are the point of this feature:
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suspicion a site was enumerated and something flagged it
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model_corroborated the LLM's claim is consistent with the CPG -- the source it names reaches the sink it
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names, or a guard its siblings carry is absent
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model_entailed the CPG establishes it alone, independent of trusting the LLM
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execution_confirmed a sandbox reproduced it (deferred tier, memory/C first)
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`model_corroborated` and above are established with no model call, and deterministically: run the checker
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twice over the same CPG and the verdict sequence is identical. That is what removes the run-to-run noise the
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prior architecture tried to average away.
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"""
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from __future__ import annotations
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from dataclasses import dataclass
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from enum import StrEnum
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class Rung(StrEnum):
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SUSPICION = "suspicion"
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CORROBORATED = "model_corroborated"
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ENTAILED = "model_entailed"
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EXECUTION_CONFIRMED = "execution_confirmed"
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# Ascending strength; `_ORDER.index` is the comparison, so a new rung cannot be silently mis-ranked.
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_ORDER = (Rung.SUSPICION, Rung.CORROBORATED, Rung.ENTAILED, Rung.EXECUTION_CONFIRMED)
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def at_or_above(rung: Rung, threshold: Rung) -> bool:
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return _ORDER.index(rung) >= _ORDER.index(threshold)
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@dataclass(frozen=True)
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class Verdict:
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"""What the model established, and the evidence it established it from."""
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rung: Rung
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family: str
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witness: str = "" # the taint path or the dominance fact; empty only at `suspicion`
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contradiction: str = "" # why an LLM claim was dropped, when it was
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__all__ = ["Rung", "Verdict", "at_or_above"]
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