3 papers
cs.LG2026
Steering Recurrent Reasoners at Inference Time with Readout Feedback
Shunsuke Kamiya, Masanori Koyama, Seongcheol Jeong +5
Recurrent models, which repeatedly update latent states with shared computation blocks, have emerged as powerful architectures for solving complex reasoning tasks. Existing inferen…
cs.LG2026
Looped Transformers with Source-Centered State Evolution
Bum Jun Kim, Kohei Hayashi, Shunsuke Kamiya +3
Looped Transformers create a useful train- and test-time compute axis by reusing the same Transformer block over recurrent depth, increasing effective depth at a fixed parameter co…
cs.LG2026
C-voting: Confidence-Based Test-Time Voting without Explicit Energy Functions
Kenji Kubo, Shunsuke Kamiya, Masanori Koyama +3
Neural network models with latent recurrent processing, where identical layers are recursively applied to the latent state, have gained attention as promising models for performing…