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anthropic published the J-space paper today. tl;dr: models have a small emergent set of internal “silent words” (~a few dozen concepts at a time, subtext runs qwen3.5-4B in bf16 on a single 12GB GPU and reads the lens at 9 layers on every token — both while the model reads your message and while it replies. streams at full generation speed (the lens is just a matmul + unembed per layer, basically free). favorite moment: type “is this correct? 12 + 5 = 1” and incorrect lights up mid-network while it’s still reading the equation. zero reply tokens exist at this point. the verdict is just sitting there, internally, before the model says anything. repo: https://github.com/ninjahawk/Subtext no GPU: recorded session replays in the browser: https://ninjahawk.github.io/Subtext/ paper: https://www.anthropic.com/research/global-workspace the live readout path is verified against anthropic’s reference implementation — audit script in the repo, top-5 matches exactly at every layer/position tested, cosine 0.99998. that’s it. questions welcome. submitted by /u/TheOnlyVibemaster [link] [comments]
