{
  "video_id": "reddit_1w2r37q",
  "channel_slug": "LocalLLaMA",
  "channel_handle": "r/LocalLLaMA",
  "title": "Are there any interesting architectural innovations that we seem to be on the verge of for LLM models or AI models that might be a big deal? (Excluding maybe N-gram, since everyone is already well aware of that one)",
  "url": "https://www.reddit.com/r/LocalLLaMA/comments/1w2r37q/are_there_any_interesting_architectural/",
  "external_url": null,
  "upload_date": "20260830",
  "published_at": "2026-08-30T19:36:21+00:00",
  "transcript": "So, ideally for this thread we exclude the ones that everyone on here is already well aware of and discussing on here a lot, like N-gram, quantization improvements, MTP, D-flash, and D-spark, since those are improvement areas that most people on here are already pretty familiar with.  \n\nI'm more curious about any interesting fundamental architectural changes to either LLMs or other types of AI models, that you guys have been reading about or is starting to get any buzz that maybe most of us don't know about.\n\nI know one person on here seemed pretty interested in the possibilities of more MAMBA-leaning architectures, although I don't know enough about AI to understand what makes it interesting compared to the more traditional LLM transformers and how they do attention.  Like, what the high-end potential would be if people took it to greater extremes, let's say.\n\nAnyway, I am curious if there are any other notable architectural things, maybe even more significantly different than just MAMBA or hybrid architecture changes, if there are some more radical ones you've seen people theorizing about, that maybe some of you have found interesting or think have a lot of potential.\n\nAnd if possible, explain why you think it is interesting or might have a lot of potential.\n\n\n\n--- Top Comments ---\n\n\n[40 upvotes] I'll cover what I think is next below. That being, I'll just say what Qwen's opinion is with Qwen3.8 Next, which represents the Qwen4 arch that will be released later this year.\n\nBut first ... \"Mamba\" is mostly replaced with \"linear attention\" in recent terminology and encompasses a wide variety of linear attention designs that have \"State Space Models\" or ways of lossily compressing the KV cache into a fixed size (very fast but not accurate). The current best models all use linear attention to one degree or another. Linear attention is under the broader classification of \"attention mechanisms\" which are effectively interchangeable for any given layer of a transformer and generally are tradeoffs between speed and accuracy.\n\nSome architectures opted for \"sparse attention\" starting earlier this year. That is some variety of normal / full attention (multi head attention) where there is some manner of indexing-ish mechanism done first so the whole KV cache doesn't have to be checked for every token (not as fast as linear but more accurate).\n\nThey've been making hybrids of these attentions with models since last year because they hadn't quite figured out how to make linear attention acc\n\n[28 upvotes] Regarding pre-training, Next latent prediction.   \nhttps://arxiv.org/abs/2511.05963\n\nGrowing evidences outside the original paper showing great improvements over multi token prediction. \n\n[16 upvotes] Like many, I wonder if n-gram knowledge might actually be it - not the tech itself, but if they find a way to update a model's knowledge base without re-training it.\n\nWhen we start to reach the ceiling for a given model size and progress begins to plateau, that could become *really* important - while the reasoning capabilities might be as good as they can be, the knowledge will quickly get out of date (Qwen 3.8 27B with a training cut-off back in 2024, anyone?). Pluggable n-gram modules (for a given model architecture, I'm not expecting cross-architecture compatibility) might really be the best and cheapest option.\n\nThat could also be a significant cost saving in terms of model training, which could become hugely important post-bubble-pop.\n\n[15 upvotes] the danish model foundaton’s latest 1B model that appears competitive on benchmarks used HRM rather than transformers. Earlier analysis said that generally speaking, you can get HRM’s comparatively superior results to vanilla transformers by tweaking the transformer architecture but ig it’s still worth mentioning \n\n'DFM Mimir v1: An Open HRM Delivering Frontier Performance at 1B Parameters Using Only Permissible Post-Training Data', https://arxiv.org/abs/2608.13517\n\nedit: spelling\n\n[12 upvotes] Tencent's MIX-STQ compression seems promising. Gave it a vibe coded try myself and reduced LFM'S 8B Q8 to 2.5 GB and got something not-too-bad but the base model wasn't good enough either. And I quantized a quant (hardware limitations), so there's room for improvement. ",
  "transcript_chars": 4270,
  "ingested_at": "2026-08-31T01:30:03.192254+00:00",
  "source": "reddit",
  "yt_meta": {
    "score": 81,
    "upvote_ratio": 0.96,
    "num_comments": 23,
    "author": "DeepOrangeSky",
    "is_self": true
  }
}