Author

J. Hwang

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Preprint Oct 2026

When Does Exploration Prevent Collapse? Three Regimes and the Path of the Reward in Self-Consistency Training

Test-time reinforcement learning (TTRL) adapts a model without labels by rewarding answers that agree with the majority, but it collapses the policy onto its majority answer and can remove the correct answer from the samples. Exploration can prevent this collapse, yet its strength is chosen by trial and error, and how...

J. Hwang, Uiwon Hwang · 0 citations

Preprint Oct 2026

The Batch Size Can Be One: Test-Time Adaptation from Classifier Geometry

Test-time adaptation updates a deployed model on an unlabeled test stream. In practice this stream often arrives one sample at a time, with the classes in no particular order. A single sample then provides limited information for estimating distribution-level statistics, and selecting samples by confidence can admit pr...

J. Hwang, Yujin Oh, Juhyeon Shin et al. · 0 citations

Preprint Oct 2026

Certified Adaptive List Decoding for Error-Correcting Codes

Maximum-likelihood (ML) decoding of linear block codes is NP-hard, so neural decoders are evaluated by how closely they approach it. We observe that proving a candidate optimal can be far easier than finding it, and build a decoder that does both. We cast decoding as a search over an absorbing-state discrete diffusion...

J. Hwang, Byung-Sun Won, Uiwon Hwang · 0 citations