Decoding-time probes catch LLM hallucinations before they compound

The key insight is timing: DescaPE detects hallucination risk *during* generation by watching for activation anomalies, not after the fact. This means the model can suppress bad tokens before they're committed to the output sequence, rather than filtering or retraining afterward.

This connects directly to the clinical citation work from earlier this month, which exposed how current systems fail to provide verifiable evidence at the claim level. DescaPE offers a complementary mechanism: if you can catch factual drift before it happens, you reduce the burden on post-hoc verification. It also echoes the routing paper's insight that frozen models encode latent structure (here, error signals) that can be extracted via lightweight probes. Together, these suggest a shift from treating hallucination as a training problem toward treating it as an inference-time interpretability problem.

If DescaPE's probe generalizes across model families (tested on GPT-style and other architectures) without retraining, that confirms the activation patterns are robust signatures of hallucination rather than model-specific artifacts. If it doesn't generalize, the approach may be too coupled to specific training regimes to matter for production systems.