BDH-CQ (wip)
Implementation of BDH-CQ: In-Context Learning with Recurrent Latent Reasoning, proposed by Pathway Research
Install
$ pip install bdh-cq
Usage
import torch
from bdh_cq import BDHCQ
model = BDHCQ()
ids = torch.randn(2, 1024)
logits = model(ids)
Citations
@misc{engdahl2026bdhcq,
title = {BDH-CQ: In-Context Learning with Recurrent Latent Reasoning},
author = {Björn Engdahl and Adrian Kosowski and Jan Chorowski and Zuzanna Stamirowska and Przemysław Uznański and Junlin Jiang and Rohan Phadke and Remigiusz Kinas and Richard Zhong},
year = {2026},
eprint = {2608.09888},
archivePrefix = {arXiv},
primaryClass = {cs.NE},
url = {https://arxiv.org/abs/2608.09888}
}
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
bdh_cq-0.0.2.tar.gz
(7.7 kB
view details)
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file bdh_cq-0.0.2.tar.gz.
File metadata
- Download URL: bdh_cq-0.0.2.tar.gz
- Upload date:
- Size: 7.7 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via:
uv/0.8.17
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
78a91b5e6ec5165c94b3e2875f83e8d3bb905388a33f2af6505f64565955235e
|
|
| MD5 |
0e83303f8c72942d31c8125958f34cd1
|
|
| BLAKE2b-256 |
48b669360403d8f7a0f6b1005f32925f369a5d17d52175ac23f0959f9c97c108
|
File details
Details for the file bdh_cq-0.0.2-py3-none-any.whl.
File metadata
- Download URL: bdh_cq-0.0.2-py3-none-any.whl
- Upload date:
- Size: 7.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via:
uv/0.8.17
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3c86b00d418dc744b2ba1adbfddf9e23f7d7f047cd259f2314e2463629383c55
|
|
| MD5 |
8822bdfc48e31973061b105ed5ef6ff2
|
|
| BLAKE2b-256 |
0a94f15e5992c8777adacd0883b427e135d9597f29eb2885100dc919f11f166d
|