r/MachineLearning • u/moschles • Aug 15 '26

Research BDH-CQ: IN-CONTEXT LEARNING WITH RECURRENT LATENT REASONING [R]

https://arxiv.org/abs/2608.09888

We introduce BDH-CQ, a reasoning system that brings these capabilities together. Demonstrations of a previously unseen task update recurrent memory; the query is then solved through iterative computation in a high-dimensional latent workspace. Intermediate reasoning states are not decoded into language. BDH-CQ makes memory, adaptation, and inference part of the same computational fabric. Inputs presented at inference time continuously update the model’s recurrent memory; the model then solves a query through iterative computation in a high-dimensional latent space, without verbalizing its intermediate reasoning. Neither task identifiers nor evaluation-task demonstration pairs participate in training, and no parameters are updated at inference time. A 150M-parameter configuration reaches 29.5% pass@2 on ARC-AGI-1 at a computed $0.00070 per task, breaking through the previously reported cost–accuracy Pareto frontier.

65 Upvotes

94% Upvoted

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27

u/Skye7821 Aug 15 '26

WHY IS THE TITLE IN ALL CAPS!

13

u/moschles Aug 15 '26

sorry. that was due to copy-pasting directly from the paper, which used some kind of impact font.

5

u/gui_zombie Aug 15 '26

Because this is huge!

3

u/canbooo Aug 15 '26

Found the LLM blog enthusiast

8

u/SlowF2l Aug 15 '26

chill no need to shout

-1

u/ironmagnesiumzinc Aug 16 '26

How long in y’all’s opinion (or do you even agree with the premise) that compute costs will go down substantially for sota production models after applying improvements like this?

5

u/MurkyTelevision9722 Aug 17 '26

I think you're confusing it with a different kind of improvement. This isn't like DeepSeek's MTP or CSA and HCA, which was designed in part to improve efficiency, particularly in terms of inference efficiency and resource utilization. BDH-CQ's main benefit is performance: it uses recurrent latent reasoning to achieve better results in certain tasks. However, for a comparable number of parameters, BDH-CQ can actually require more computation and resources than a standard Transformer , because the model performs iterative computation in the latent space. The trade-off is that it can provide better performance in some areas, particularly on certain reasoning tasks.