Log inSign up
Sean McLeish
186 posts
@SeanMcleish

Sean McLeish

@SeanMcleish
PhD student at the University of Maryland
mcleish7.github.io
Joined November 2023
159
Following
660
Followers
RepliesRepliesRepostsRepostsMediaMedia

Log in or sign up for X

See what’s happening and join the conversation

Continue with phone
or
Log in with username or email
Terms·Privacy·Cookies·Accessibility·Ads Info·© 2026 X Corp.
  • Pinned
    @SeanMcleish
    Sean McLeish
    @SeanMcleish
    Jun 10
    Humans don’t maintain exact, line-by-line recall of huge contexts like full codebases or long legal documents. We keep a high-level mental model, then look things up when precision matters. We enable LLMs to do this, with high speed.
    @Pavel_Izmailov
    Pavel Izmailov
    @Pavel_Izmailov
    Jun 10
    New paper: Latent Context Language Models (LCLMs)! Idea: encode 16 tokens as 1 latent token, and have the LLM work on top of the latent tokens. Result: general-purpose model with much better performance / speed / memory usage frontier.
    Image
  • @SeanMcleish
    Sean McLeish
    @SeanMcleish
    Jun 9
    Scaling context works but it is expensive. Prior KV cache compression promises answers but never fully delivers vLLM speed, we solve this.
    @iamleonli
    Leon
    @iamleonli
    Jun 9
    How far can we compress the discrete tokens in an LLM's context into compact latent vectors? With the right training recipe at large scale, our Latent Context Language Models (LCLMs) compress context up to 16× and land on a new Pareto frontier for long-context inference. 🧵(1/n)
    Image
  • @SeanMcleish
    Sean McLeish
    @SeanMcleish
    May 28
    Offline recurrence can improve inference accuracy by iteratively refining fast weights, giving the model an adaptation mechanism at test time.
    @sang_yun_lee
    Sangyun Lee
    @sang_yun_lee
    May 28
    Almost all animals sleep. Why don’t LMs? Introducing our new work on language model sleep. tl;dr : A periodic, recurrent “sleep” phase allows LMs to digest their context and transfer it into their weights, improving recall and reasoning on challenging tasks.
    Image
    00:00
  • @SeanMcleish
    Sean McLeish
    @SeanMcleish
    Nov 11, 2025
    Looped latent reasoning models like TRM, HRM, Ouro and Huginn are great for reasoning, but they’re inefficient to train at larger scales. We fix this by post training regular language models into looped models, achieving higher accuracy on a per training FLOP basis. 📜1/7
    Image
    9
  • @SeanMcleish
    Sean McLeish
    @SeanMcleish
    Jul 22, 2025
    Updates from our depth-recurrent model adventure: 📈 KV cache sharing across recurrences = higher accuracy quicker + less memory 🔧 Now with vLLM integration & finetuning examples! github.com/seal-rg/recurr…
    @jonasgeiping
    Jonas Geiping
    @jonasgeiping
    Jul 22, 2025
    A recurrent depth/Huginn-3.5B Update: I orginally wanted to post these more often, but I guess time is a river, and I just don't like posting all that much yet... The most interesting finding about the depth recurrent model has been this unassuming chart, actually:
    Image
Advertisement
Advertisement