the J-space paper quietly settled a chunk of the “do LLMs actually think” argument. i built a live viewer so you can watch for yourself instead of arguing

if you haven’t read it: https://www.anthropic.com/research/global-workspace. language models have an emergent internal workspace of silent words they can report, steer, and reason with. the part that got me: ask a model to check “12 + 5 = 1” and incorrect saturates internally while it’s still reading the problem, the “no, that’s not right” it types a moment later is narration of a decision that already happened. the arguing is optional now. you can just look. repo: https://github.com/ninjahawk/Subtext this sub has spent years on “it’s just autocomplete” vs “it’s actually reasoning” and the honest answer turns out to be: both, and now it’s measurable. the instrument shows most of the model’s fluent output , grammar, tone, common facts, bypassing the workspace entirely (no “thinking” involved), while multi-step problems visibly route through it. both camps were half right. that’s the fun part. anthropic open sourced the lens and neuronpedia published pre-fitted ones for qwen, so i wired it into a chat interface. 9 layers of readout per token, rendered live, including while it reads your message, before any output exists. demo video in the repo: the verdict on 12+5=1 forming during reading, then the model holding modulo and bitwise in mind several tokens before saying either word (it was planning the modular arithmetic caveat. you can watch it plan.) browser replay if you don’t have a GPU: https://ninjahawk.github.io/Subtext/ and yes, functional availability is not consciousness, before anyone starts — the paper is careful about that and so am i. but that’s the interesting part: nobody designed this workspace. it just shows up in transformers when you train them, on a random open 4B the same as on claude. ¯\(ツ)/¯