LoopUS converts pretrained LLMs into looped latent refinement models via block decomposition, selective gating, random deep supervision, and confidence-based early exiting to improve reasoning performance.
Looping back to move forward: Recursive transformers for efficient and flexible large multimodal models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
background 1representative citing papers
SoftMoR mixes all recursion-step outputs via per-token weights so recursive Vision Transformers gain accuracy from added depth with only ~1.7M extra parameters on ImageNet-1K.
Experimental study finds Recursive-Transformer for ASR encoders achieves comparable performance with 66% fewer parameters when limited recursion is applied in the latent space.
citing papers explorer
-
LoopUS: Recasting Pretrained LLMs into Looped Latent Refinement Models
LoopUS converts pretrained LLMs into looped latent refinement models via block decomposition, selective gating, random deep supervision, and confidence-based early exiting to improve reasoning performance.
-
Soft Mixture-of-Recursions: Going Deeper with Recursive Vision Transformers
SoftMoR mixes all recursion-step outputs via per-token weights so recursive Vision Transformers gain accuracy from added depth with only ~1.7M extra parameters on ImageNet-1K.
-
Rethinking Depth: A study of the Recursive-Transformer for Speech Recognition
Experimental study finds Recursive-Transformer for ASR encoders achieves comparable performance with 66% fewer parameters when limited recursion is applied in the latent space.