{
  "video_id": "reddit_1uugulk",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "Anthropic found Claude reasoning in silence (J-space) — we ran the same lens on open Qwen3-8B",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1uugulk/anthropic_found_claude_reasoning_in_silence/",
  "external_url": null,
  "upload_date": "20260712",
  "published_at": "2026-07-12T14:22:37+00:00",
  "transcript": "Anthropic’s research on Claude found a silent internal workspace they call J-space — hidden reasoning that never shows up as visible text.\n\nClassic example: the model answers `49`, but inside J-space they caught `21 → 42 → 49`.\n\nImportant distinction:\n\n* Chain-of-thought = text you can read\n* J-space = silent concepts in activations (“what’s on its mind”)\n\nWe fitted the open Jacobian lens (J-lens) on Qwen3-8B, ran it locally, and used it to catch prose drift before tool calls (model leaning toward “To, You, Do…” instead of JSON).\n\nThen we wired that into agent guards: stop / cancel / keep the useful space, and distill recoveries into LoRA data.\n\nMade an 8-min explainer demo:\n\n[https://www.youtube.com/watch?v=5UMN6hDONJk](https://www.youtube.com/watch?v=5UMN6hDONJk)\n\nHappy to answer questions about the probe setup or the guard loop.\n\n\n\n--- Top Comments ---\n\n\n[202 upvotes] Anthropic uses Qwen3.6-27B in its own demo...\n\n[https://www.neuronpedia.org/qwen3.6-27b/jlens](https://www.neuronpedia.org/qwen3.6-27b/jlens)\n\nYou didn't even read the front page, did you?\n\n[48 upvotes] Cool replication, and the tool-call drift is really useful, so thanks for sharing. Two clarifications though helpful:\n\n1. **\"21 → 42 → 49\" being decodable ≠ the model performing that derivation.** Intermediates are decodable *representations*, not *computational* *steps*.\n2. **Intermediate representations and generated CoT tokens are similarly disassociated from final outputs.** We already have decent evidence that visible traces are dissociated from final answer quality. Models trained on deliberately scrambled traces perform as well or better as actual ones (Valmeekam et al. 2025), and locally coherent intermediate tokens routinely accompany globally invalid solutions and vice versa (Samineni et al. 2025).  The J-lens moves the monitoring channel one layer down to a rich statistical intermediate, but it’s still not reasoning.\n\nMechanically, what’s happening here is likely intermediate representations of model navigation over its internal geometry. The current literature shows that high entropy “forking-tokens” (however, assuming, but, given, etc.) do pathway selection while the rest is cached deterministic completion (Wang et al. 2025). Read that way, J-space isn't a window into \n\n[39 upvotes] I abliterated Qwen3.5 9B yesterday using the j-space: https://huggingface.co/jwest33/qwen3.5-9b-jspace-abliterated (the iterative function in my toolkit is busted so this was a single pass and targeted towards non-thinking). \n\nMost interesting finding was that virtually all refusals are related to self-reference. The models almost always say “I cannot..” or “I refuse”, but self-referential language is actually pretty rare otherwise. Which is obvious in hindsight, and almost certainly an artifact of safety post-training but still interesting. ",
  "transcript_chars": 2850,
  "ingested_at": "2026-07-13T01:30:14.717176+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 75,
    "upvote_ratio": 0.63,
    "num_comments": 71,
    "author": "Murky-Sign37",
    "is_self": true
  }
}