[R] Looking for arXiv cs.LG endorser, inference monitoring using information geometry

Hi r/MachineLearning,

I’m looking for an arXiv endorser in cs.LG for a paper on inference-time distribution shift detection for deployed LLMs.

The core idea: instead of monitoring input embeddings (which is what existing tools do), we monitor the statistical manifold of the model’s output distributions using Fisher-Rao geodesic distance. We then run adaptive CUSUM (Page-Hinkley) on the resulting z-score stream to catch slow drift that per-request spike detection misses entirely.

The methodology is grounded in published work on information geometry (Figshare, DOIs available). We’ve validated the signal on real OpenAI API logprobs, CUSUM caught gradual domain drift in 7 steps with zero false alarms during warmup, while spike detection missed it entirely.

If anyone with cs.LG endorsement is willing to take a look, I’d really appreciate it. Happy to share the draft and the underlying papers.

Thank you.