🤖Reddit r/MachineLearning•Sep 1, 2026Freshcollected in 6m

Mapping the Latent Reasoning Frontier

🤖Read original on Reddit r/MachineLearning

#latent-reasoning#recurrent-models#continual-learning#test-time-computelatent-reasoning-architecturesbdh-cqhrmtrmcoconutarc-agi-1

💡See how BDH-CQ, HRM, TRM, and Coconut differ beyond conventional chain-of-thought.

⚡ 30-Second TL;DR

What Changed

The article argues that verbalized chain-of-thought may not faithfully represent the computation producing an answer.

Why It Matters

If these approaches scale reliably, AI developers may shift from optimizing longer visible CoT traces toward architectures that compute in hidden recurrent states. However, differences in task adaptation, transductive evaluation, and benchmark methodology make direct comparisons difficult.

What To Do Next

Benchmark a small Coconut-style hidden-state loop against verbal CoT on your reasoning task, measuring accuracy, latency, token use, and adaptation cost separately.

Who should care:Researchers & Academics

Key Points

•The article argues that verbalized chain-of-thought may not faithfully represent the computation producing an answer.
•Coconut and Soft Thinking perform reasoning through continuous hidden or concept states rather than fully verbalized intermediate steps.
•HRM and TRM recursively refine latent and answer states, but their ARC workflows use task-specific optimization before evaluation.
•BDH-CQ uses demonstrations to write into recurrent memory at inference time, then solves new inputs through separate latent iterative computation.
•The author identifies task acquisition and the location of computation as key dimensions for comparing latent reasoning systems.