3 papers
cs.LG2025
Thoughtbubbles: an Unsupervised Method for Parallel Thinking in Latent Space
Houjun Liu, Shikhar Murty, Christopher D. Manning +1
Current approaches for scaling inference-time compute in transformers train them to emit explicit chain-of-thought tokens before producing an answer. While these methods are powerf…
cs.CL2025
Mechanistic evaluation of Transformers and state space models
Aryaman Arora, Neil Rathi, Nikil Roashan Selvam +3
State space models (SSMs) for language modelling promise an efficient and performant alternative to quadratic-attention Transformers, yet show variable performance on recalling bas…
cs.LG2025
Do Language Models Use Their Depth Efficiently?
Róbert Csordás, Christopher D. Manning, Christopher Potts
Modern LLMs are increasingly deep, and depth correlates with performance, albeit with diminishing returns. However, do these models use their depth efficiently? Do they compose mor…