Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
Openai o1 system card
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.LG 2years
2026 2roles
background 1polarities
background 1representative citing papers
QuasiMoTTo uses quasi-Monte Carlo to produce correlated yet marginally correct samples from language models, matching i.i.d. pass@k with 25-47% fewer samples on reasoning benchmarks and 50% fewer RL training steps.
citing papers explorer
-
Latent Chain-of-Thought Improves Structured-Data Transformers
Latent chain-of-thought via recurrent feedback tokens from compressed hidden states improves transformer performance on time-series forecasting and tabular prediction across 36 datasets.
-
QuasiMoTTo: Quasi-Monte Carlo Test-Time Scaling
QuasiMoTTo uses quasi-Monte Carlo to produce correlated yet marginally correct samples from language models, matching i.i.d. pass@k with 25-47% fewer samples on reasoning benchmarks and 50% fewer RL training steps.