[entrustai]

The geometric approach is interesting precisely because it's model-agnostic at the content level — you're detecting structural collapse in latent space before it surfaces as text, which means you don't need to know what a hallucination looks like semantically.

The 54% recall is the honest number to focus on. At 88% precision you're catching real problems when you flag them, but you're missing roughly half of all hallucinations entirely. For a suppression layer in a regulated context that's a meaningful gap — a compliance team can't tell a regulator "we caught most of them."

The complementary approach worth considering: deterministic post-generation checks on the output layer. Geometric drift catches structural collapse during generation. Rule-based output validation catches semantic violations after generation — banned claims, unattributed statistics, absolute guarantees. Neither approach alone is sufficient. Together they cover different failure modes.

Good work publishing the raw_logs.csv. Reproducibility at this layer is rare and matters.

[yubainu]

Thanks for the precise critique. You are right: Recall 54% is the "danger zone." In a regulated or production environment, missing half of the structural collapses is functionally equivalent to zero protection. The 88% precision proves the signal exists, but the threshold for "collapse" in latent space is currently too rigid. The "Geometric approach (SIB) + Rule-based output validation" hybrid you suggested is the most logical path forward. • Geometric Drift (Layer-Internal): Catches the "process" of losing logical coherence (structural entropy). • Rule-based (Output-Layer): Catches the "result" of semantic violations (pre-defined constraints). My next focus is analyzing the "Silent Failures" — the 46% we missed. If the latent space doesn't show geometric collapse but the output is still a hallucination, it suggests the model is confidently drifting into a "parallel" but structurally stable manifold. That's a different failure mode that geometry alone can't catch. Reproducibility is the only way to move this out of "voodoo AI" territory. Glad the raw_logs.csv helped.

[entrustai]

"Confidently drifting into a structurally stable manifold" — that's the harder failure mode. The geometry looks clean because the model isn't collapsing, it's just navigating to a coherent but factually detached region of latent space. Geometric drift detection is blind to it by design. This is exactly where output-layer validation becomes non-negotiable rather than complementary. If the structural signal is absent, the semantic signal is all you have. Rule-based checks on absolute claims, unattributed statistics, and banned assertions catch the confident hallucination that geometry misses entirely. The hybrid architecture you're describing — SIB for process detection, rule-based for output validation — covers both failure modes. Worth also considering confidence scoring at the rule level: flagging outputs where the geometric signal is clean but semantic violations cluster, since that pattern may itself be a reliable indicator of the stable-manifold drift you're investigating. Solid research direction.