BDH-CQ: In-Context Learning with Recurrent Latent Reasoning | Björn Engdahl et al. | ResearchPod

Transcript: BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

Alex: Welcome to another episode of ResearchPod. Today we're looking at a paper about a model called BDH-CQ. The central question it asks is surprisingly practical: can a system learn a new task from just a handful of examples, and then solve it without writing out every step of its thinking?

Sam: Why would writing out every step be a problem?

Alex: Because most AI systems today do their reasoning by generating lots of words before they land on a final answer. That gives them room to work through a problem, but it also makes them slower and more expensive to run. BDH-CQ tries a different approach: it keeps the working-out part hidden inside the model, like a scratchpad it never has to read aloud.

Sam: So the model is still thinking, just not in words?

Alex: Exactly. Think of it like a student who solves a maths problem in their head instead of writing every step on paper. The model updates a hidden workspace over and over, using the examples it has seen, until it has a confident answer. The paper calls this a latent reasoning workspace — "latent" just means hidden, not spoken.

Sam: And what kind of puzzles are they using to test this?

Alex: They use something called ARC — the Abstraction and Reasoning Corpus. These are small grid puzzles. Each one shows you a few examples of an input grid and its matching output grid, and your job is to figure out the rule and apply it to a new grid. So if the examples all show a pattern shifting one square to the right, you have to do the same thing to the test grid.

Sam: That sounds like a clean way to check whether the model actually understood the rule, rather than just guessing.

Alex: Precisely. And that is exactly why the paper uses them. A lucky guess might get one output right, but if the model truly learned the rule, it should apply it consistently across every new case it sees. The paper checks both things: whether the model gets individual outputs right, and whether it follows the same rule across an entire task.

Sam: So what did they find?

Alex: Performance varied quite a bit depending on the type of visual change involved. When the task was something simple — like moving a shape to a new position, or copying a pattern to several spots — the model handled it well and stayed consistent. But when tasks required keeping several objects in a specific order, or tracking one pattern nested inside another, accuracy dropped.

Sam: Why would ordering or nesting be harder?

Alex: Because they require the model to juggle multiple relationships at once. Moving a shape is a single, local change. But ordering means keeping track of which object comes first, second, third — and getting any one of those wrong breaks the whole output. Nesting means preserving a relationship where one pattern sits inside another, and the model sometimes gets the structure right but makes one wrong inside-or-outside call. So these are two different kinds of failure, not just one general weakness.

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

Björn Engdahl, Adrian Kosowski, Jan Chorowski, Zuzanna Stamirowska, Przemysław Uznański, Junlin Jiang, Rohan Phadke, Remigiusz Kinas, Richard Zhong

Aug 10, 2026·5:36·arxiv·Original Paper

In-context learningRecurrent latent reasoningARC-AGI-1 benchmarkCost-efficient inferenceLatent workspaceAbstraction and Reasoning Corpus (ARC)

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6 min

Briefing

Abstract

We introduce BDH-CQ, a reasoning model that combines in-context learning with recurrent latent reasoning. 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. We evaluate the model on the public ARC-AGI-1 evaluation set and use controlled ARC-like interventions to study what it learns from demonstrations, how consistently it applies an inferred transformation, and which concepts remain difficult. A 150M-parameter configuration reaches 29.5% pass@2 at a computed inference cost of \$0.0007 per task. This operating point breaks through the previously reported ARC-AGI-1 cost-accuracy Pareto frontier, establishing a new state of the art in benchmark cost efficiency.

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning: Research Paper Podcast

BDH-CQ: In-Context Learning with Recurrent Latent Reasoning

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