{
  "video_id": "reddit_1vohdrz",
  "channel_slug": "singularity",
  "channel_handle": "r/singularity",
  "title": "A 150M param recurrent model scores 29.5% on ARC-AGI-1 at $0.0007 per task",
  "url": "https://www.reddit.com/r/singularity/comments/1vohdrz/a_150m_param_recurrent_model_scores_295_on/",
  "external_url": "https://arxiv.org/abs/2608.09888",
  "upload_date": "20260814",
  "published_at": "2026-08-14T19:43:27+00:00",
  "transcript": "Not a transformer. It's a recurrent latent reasoning setup that keeps \"thinking\" in latent space before answering. Sits completely outside the published cost/accuracy frontier for ARC-AGI, and something this size runs on basically anything. Paper is from the Pathway team, dropped 4 days ago. I want to see it scaled to 1-3B before getting too excited, but the shape of the result is wild.\n\n[](https://www.reddit.com/submit/?source_id=t3_1voh6tx&composer_entry=crosspost_prompt)\n\n\n\n--- Top Comments ---\n\n\n[157 upvotes] Even if it's not scalable to become SOTA, more capable SLMs would be huge\n\n[56 upvotes] Imagine a future large model creating small models to solve specific problems. 🤯\n\n[47 upvotes] This is huge, this is HUGE\n\n[28 upvotes] I am.pretty sure such a mechanism is already actively researched (or even used) for upcoming frontier models. The base idea to switch to a fast latent space for reasoning instead of token-based CoT is nothing completely new afaik. The biggest hurdle (that isn't mentioned in the paper?) is the risk of a hidden misalignment.",
  "transcript_chars": 1067,
  "ingested_at": "2026-08-15T01:30:19.824854+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 384,
    "upvote_ratio": 0.95,
    "num_comments": 43,
    "author": "juanviera23",
    "is_self": false
  }
}