Input-Conditioned Search for Efficient Test-Time Scaling of Recursive Reasoners
People often tackle difficult problems by thinking more deeply or exploring more possibilities. Similarly, recursive reasoners, which reason by repeatedly applying a learned update rule to latent states, can also scale inference through more updates (depth) or additional trajectories generated by perturbing latent states (width). When people hit a dead end in their thinking, they may step back and approach the problem from a fresh angle. However, neither depth scaling nor perturbation-based width scaling of recursive reasoners can reliably achieve this: both may converge to incorrect attractors. To address this, we propose to explore diverse reasoning traces by applying different update m...
| Authors | Haochen Shi, Tianwen Fu, Qianggang Ding et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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