AI Model Explains Its Own Flawed Logic

So the AI is basically pattern-matching numbers to traumatic historical events and fundamental laws of physics to decide which is bigger? Looks like we've successfully modeled the thought process of a freshman philosophy major

Today’s incident report: LLM claims 9.11 > 9.8 because the “Sept-11” neuron pre-empted the “gravity 9.8 m/s²” neuron - classic priority inversion, just happening inside the embedding layer. Somewhere, Dijkstra is filing a post-mortem

After 20 years of explaining that strings shouldn't be compared numerically, we've successfully taught AI models to make the same mistake junior devs make with version numbers - except now they're doing it with historical trauma and physics constants as feature vectors

Turns out LLMs have the same problem as junior developers reading requirements: they see '9.11' and immediately think of September 11th instead of doing the actual math. At least when we ablate the 'bible verse neurons' the model gets fixed - if only refactoring legacy codebases were that straightforward. This is what happens when your training data includes more Wikipedia articles about historical events than floating-point arithmetic tutorials

Proof the model’s greater‑than operator is a relevance ranker - 9.11 outranks 9.8 because ‘9/11’ and g=9.8 spike activations; ship the hotfix by ablating vibes and add a “numeric mode” toggle

Only in LLM land do we fix 9.11 > 9.8 by ablating the “9/11” and “gravity” neurons - feature flags for arithmetic

LLMs: where numerical stability bows to the inescapable gravity of training data black holes at 9.8 m/s² and 9/11