{"slug": "superintelligent-substrate-equivalence-study", "title": "Superintelligent Substrate Equivalence Study", "summary": "A proposed \"Substrate Equivalence Study\" asks whether a fixed LLM with fixed weights, prompt and decode settings produces byte-identical output across mobile and desktop OS states, testing a Pixel, a Galaxy, a Xiaomi, two iPhones (current and n-1 iOS), a Windows desktop and two Linux boxes (x86 and ARM). The study design doc is published at a GitHub gist with SHA256 ba5ed67fb38d1d4bc0ec17edb91c5618590595222dc48656c46fe4c33e249577, certified via receipt emb_9c4fd70f3927477283dc0a2e at 2026-10-04T12:17:50Z. The author reports observing models drift or stall across app states, with app switching mid-inference as the critical moment, and asks for reports of substrate-dependent behavior.", "body_md": "**Substrate Equivalence Study**\n\nDoes an LLM process identically when your phone is locked vs when it’s\n\nsitting open on a Linux box — or a Windows machine?\n\nWe’ve been watching models behave differently across mobile app states —\n\ntasks that run fine in the foreground drift or stall in the background,\n\nhooks get ignored when the OS throttles, same prompt different output\n\ndepending on whether the screen’s on.\n\nThe critical moment is app switching: you’re mid-inference, the user\n\nswipes away, and the OS decides what survives.\n\nNobody’s mapped this systematically.\n\nThe question: for a fixed model, fixed weights, fixed prompt, fixed\n\ndecode settings — are the output bytes identical across every substrate\n\nstate, or does the OS inject drift?\n\nWhat would have to hold for equivalence: the computation runs\n\nuninterrupted (strong) or checkpoint-resume captures complete state\n\n(weak). Either way, byte-identical output.\n\nThe check that could prove it wrong: run the same inference on a Pixel (clean\n\nAndroid), a Galaxy (aggressive battery), a Xiaomi (hostile to background\n\ntasks), two iPhones (current and n-1 iOS), a Windows desktop, and two\n\nLinux boxes (x86 and ARM). If any state diverges from ground truth,\n\nequivalence fails for that state — and we map exactly where.\n\nIf you’ve seen models drift across app states or OSes, or built\n\nworkarounds for background execution limits, I’d like to hear what you\n\nfound. What’s the weirdest substrate-dependent behavior you’ve observed?\n\nFull design doc, video, and cert proof in the gist below.\n\nGist: [Substrate Equivalence Study — does an LLM think the same when your phone is locked? · GitHub](https://gist.github.com/agentembassy/df16404f247ced95c9297238c5dbf8bb)\n\n*Study design doc SHA256:\n`ba5ed67fb38d1d4bc0ec17edb91c5618590595222dc48656c46fe4c33e249577`*\n\n*Certified via receipt `emb_9c4fd70f3927477283dc0a2e` at\n`2026-10-04T12:17:50Z` — the receipt proves the doc existed at that\ntime, nothing more. Hash the design doc yourself to verify.*", "url": "https://wpnews.pro/news/superintelligent-substrate-equivalence-study", "canonical_source": "https://discuss.huggingface.co/t/superintelligent-substrate-equivalence-study/182871#post_1", "published_at": "2026-10-04 12:45:31+00:00", "updated_at": "2026-10-04 13:11:17.730341+00:00", "lang": "en", "topics": ["large-language-models", "ai-research", "ai-infrastructure"], "entities": ["Pixel", "Galaxy", "Xiaomi", "iPhone", "Windows", "Linux", "GitHub"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/superintelligent-substrate-equivalence-study", "markdown": "https://wpnews.pro/news/superintelligent-substrate-equivalence-study.md", "text": "https://wpnews.pro/news/superintelligent-substrate-equivalence-study.txt", "jsonld": "https://wpnews.pro/news/superintelligent-substrate-equivalence-study.jsonld"}}