Skip to main content

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 BDH

model = BDH(
    dim = 512,
    num_tokens = 20_000
)

ids = torch.randint(0, 20_000, (2, 1024))

logits = model(ids) # (2, 1024, 20_000)

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-0.0.10.tar.gz (8.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

bdh_cq-0.0.10-py3-none-any.whl (8.0 kB view details)

Uploaded Python 3

File details

Details for the file bdh_cq-0.0.10.tar.gz.

File metadata

  • Download URL: bdh_cq-0.0.10.tar.gz
  • Upload date:
  • Size: 8.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for bdh_cq-0.0.10.tar.gz
Algorithm Hash digest
SHA256 eee4f911fbe69882f84835878734a15bb5e209037628e53fe8565f8a5cebbe01
MD5 b234e75f38306276b1518b709a06759b
BLAKE2b-256 6332a8509c8f52f0ef13643899f4faf67c76cdabc8765b738c80a3e1c3374121

See more details on using hashes here.

File details

Details for the file bdh_cq-0.0.10-py3-none-any.whl.

File metadata

  • Download URL: bdh_cq-0.0.10-py3-none-any.whl
  • Upload date:
  • Size: 8.0 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: uv/0.8.17

File hashes

Hashes for bdh_cq-0.0.10-py3-none-any.whl
Algorithm Hash digest
SHA256 082aa8f8feeaa5987a982e3372520a6970893b10ca09f0d593205fa8bc608319
MD5 08299cebbef07b3ef06f53647edf1b77
BLAKE2b-256 dcd8159437cd3aef3c96bac66cbaf33bb195558245546cfe0c2db69a3a4fda1f

See more details on using hashes here.

Release history Release notifications | RSS feed

0.0.20

2 files

0.0.19

2 files

0.0.17

2 files

0.0.16

2 files

0.0.15

2 files

0.0.14

2 files

0.0.12

2 files

0.0.11

2 files

This release

0.0.10 This release

2 files

0.0.9

2 files

0.0.7

2 files

0.0.6

2 files

0.0.5

2 files

0.0.4

2 files

0.0.3

2 files

0.0.2

2 files

0.0.1

2 files

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page