— quoting @AnthropicAI
elie ✔ [pencil icon] @eliebakouch · 1h
computed the similarity (CKA) on the J-lens geometry of every layer inside and across 38 open models. the patterns are weirdly universal: same depth layout, same organization at the same relative depth, even between unrelated families like llama and olmo
eliebak.com/viz/jspace-open
[Embedded image: dashboard/visualization titled with layer axis controls, family presets (GEMMA-2, GEMMA-4, LLAMA3.1, LLAMA3.3, QWEN3, QWEN3.6, OLMO, GPT-OSS, GPT2, PYTHIA, EVERYTHING), size presets, and model checklist (gemma-2-2b, gemma-2-2b-it, gemma-2-9b, gemma-2-9b-it, gemma-2-27b, gemma-4-2b, gemma-4-9b, gemma-4-27b, llama3.1-8b, llama3.1-8b-it, etc. — 38 models selected). Center: large heatmap matrix of CKA similarity values (blue-purple-green-yellow scale) showing block-diagonal structure. Right: smaller "pair summary — matched-depth CKA" heatmap and stats panel listing "sensory block end 46.5%", "motor block start 64.1%", "blockiness (within-between) 0.315", "layer coupling (own off-diag) 0.757", "cross-model pairs" section with "matched-depth CKA 0.588", "off-diagonal block gain +0.040", "block separation (within-cross) +0.209", "depth order p (mean) 0.83", "pairs 703". Colorbar legend "CKA 0...1 hover for values".]
Quoted/embedded tweet below:
[AI icon] Anthropic ✔ @AnthropicAI · Jul 6
New Anthropic research: A global workspace in language models.
Of everything happening in your brain right now, only a tiny fraction ...
[Embedded video thumbnail, duration 5:27, showing a bird-flock/cloud abstract image split with a network diagram]
Note from Claude Sonnet 5
Technical interpretability visualization thread comparing layer-wise representational geometry (CKA similarity) across 38 open-weight LLM families, quote-tweeting an Anthropic research announcement about a "global workspace" in language models.
interpretabilitymechanistic interpretabilityllm researchanthropiccka analysis