{"schema_version":"onlylabs.public_signal.v1","title":"Google (DeepMind / Gemini) Repo: google-deepmind/padic-ml","description":"Google (DeepMind / Gemini) repo signal with public source context, captured evidence pages, related signals, and data-business radar classification.","url":"https://onlylabs.fyi/signals/b2b8bc6d-2681-4885-b5b2-dc2d7c24b196","json_url":"https://onlylabs.fyi/signals/b2b8bc6d-2681-4885-b5b2-dc2d7c24b196/signal.json","generated_at":"2026-10-25T16:02:23.054Z","evidence_latest_fetched_at":"2026-09-24T07:03:29.795741+00:00","signal_first_seen_at":"2026-09-24T07:00:33.170701+00:00","org":{"slug":"google-deepmind","name":"Google (DeepMind / Gemini)","category":"frontier-lab","category_label":"Frontier lab","dossier_url":"https://onlylabs.fyi/labs/google-deepmind","dossier_json_url":"https://onlylabs.fyi/labs/google-deepmind/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/b2b8bc6d-2681-4885-b5b2-dc2d7c24b196","signal_json":"https://onlylabs.fyi/signals/b2b8bc6d-2681-4885-b5b2-dc2d7c24b196/signal.json","source":"https://github.com/google-deepmind/padic-ml","lab_dossier":"https://onlylabs.fyi/labs/google-deepmind","lab_dossier_json":"https://onlylabs.fyi/labs/google-deepmind/dossier.json","analysis":"https://onlylabs.fyi/analysis/google-deepmind","analysis_json":"https://onlylabs.fyi/analysis/google-deepmind/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/google-deepmind/evidence.json","category":"https://onlylabs.fyi/frontier","category_json":"https://onlylabs.fyi/frontier.json","category_feed":"https://onlylabs.fyi/frontier/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json","topic":null,"topic_signals_json":null,"topic_feed":null,"data_business":null},"answer_pack":{"answer":"Google (DeepMind / Gemini) published google-deepmind/padic-ml (Python). 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Paper code is being refactored into this library. Thanks for your patience!** This is a JAX library for differentiable training of _p_-adic neural networks, implementing methods described in *[Continuous Optimization for p-adic Models](https://arxiv.org/abs/2609.25501)* (arXiv 2026). Install with `pip install padic-ml`. **Disclaimer:** This is not an officially supported Google product. Usage `PArray` is an array primitive for \"learnable\" _p_-adic numbers (points in the _p_-adic injective hull $\\Gamma_p$ in Berkovich space). 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