New Recurrent Depth Architecture Enables Implicit Reasoning Without Chain-of-Thought
iScienceLuvr · x · 2026-09-02
A paper titled 'Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach' proposes a novel language model architecture. It iterates a recurrent block to unroll to arbitrary depth at test-time, performing implicit reasoning in latent space. This contrasts with mainstream reasoning models that scale compute by producing more tokens. The approach requires no specialized CoT training data, works with small context windows, and captures reasoning not easily represented in words. A proof-of-concept has been scaled to 3.5B parameters.
More from Research
- Loop Launches Supply Chain AI Benchmark AuditBench — daniellewis · 2026-09-02
- 3B TwIL Model Outperforms 120B Open Source Model on Formal Reasoning — Socially-great8275 · 2026-09-02
- Implementing Q-learning in a GDevelop platformer game — tristanbob · 2026-09-02
- Google Open-Sources MAPL-EMIT: Satellite Methane Leak Detection with 84% Accuracy — DynamicWebPaige · 2026-09-02
- Study: ChatGPT caused 21-50% drop in writing variance across the web — maier_ak · 2026-09-02
- AI Polishing Erases Linguistic Identity, Threatens Social Diagnostics — maier_ak · 2026-09-02