PredictLM v11.0 + Mini ship-bundle

README.md

@@ -84,7 +84,7 @@ That's it. On the first `.predict()` call the package silently downloads its par
 - **No internet / air-gapped.** Pass `auto_duo=False` at load to disable partner download — `.predict()` returns the single-model in-context result.
 - **Real-time inference** (<10 ms latency)? Use `auto_duo=False` zero-tuning. Duo + TTT adds ~1-60 s per query depending on table size.
 
-**TTT** ([Test-Time Training](https://arxiv.org/abs/2503.11842), grounded in TabPFN-2.5's [recipe](https://arxiv.org/abs/2511.08667)) does ~15 inner Adam steps of self-supervised fine-tuning on the user's in-context examples before predicting. Per-task specialization on top of a generic ICL prior. 19 / 20 datasets improved vs zero-tuning; no dataset regressed by more than 0.006.
+**TTT** ([Test-Time Training](https://arxiv.org/abs/2503.11842)) does ~15 inner Adam steps of self-supervised fine-tuning on the user's in-context examples before predicting. Per-task specialization on top of a generic ICL prior. 19 / 20 datasets improved vs zero-tuning; no dataset regressed by more than 0.006.
 
 PredictLM's TTT is an independent implementation of the published technique. This repo does not include or derive from TabPFN code or weights — PredictLM weights are trained from scratch on synthetic data and shipped under Apache-2.0.