https://hellogxp.github.io/tags/interpretability/Recent content in Interpretability on Xueping's BlogHugoen-usSun, 13 Sep 2026 00:00:00 +0000https://hellogxp.github.io/posts/the-unvalidated-instrument/Sun, 13 Sep 2026 00:00:00 +0000https://hellogxp.github.io/posts/the-unvalidated-instrument/Whether an LLM is conscious and whether it is scheming are argued through the same instrument — the model’s report about its own internal states. That instrument has never been calibrated. Ask it to detect an injected thought and it succeeds ~20% of the time; ask it to confess a hidden objective and a 2026 method reads it out at F1 0.98. Same channel, opposite verdicts, no spec sheet. A cited accounting of when self-report is load-bearing and when the reading is void.https://hellogxp.github.io/posts/cka-delta-universality/Sat, 20 Jun 2026 00:00:00 +0000https://hellogxp.github.io/posts/cka-delta-universality/Different LLM architectures encode high-level concepts in structurally compatible ways—but only up to a point. Geometric similarity between Llama, Qwen, Gemma, and Mistral is moderate at best (CKA_Δ around 0.3–0.7), yet a tiny affine map transfers persona classifiers with 94–99.9% accuracy across six concept domains. This geometric-functional dissociation suggests a weak version of the Platonic Representation Hypothesis: concept representations are universally recoverable, but not universally identical.https://hellogxp.github.io/posts/late-crystallization/Sat, 06 Jun 2026 00:00:00 +0000https://hellogxp.github.io/posts/late-crystallization/85.9% of correct answers never appear in any intermediate layer prediction. We discovered that factual knowledge in LLMs undergoes a sudden phase transition—crystallizing only at the final layer. This phenomenon explains why some interventions work and others don’t.