arXiv AI
Looped Reasoning Finishes Earlier Than You Think
Read the original on arXiv AI →The Flow has not summarised this story yet — read it at arXiv AI.
Related stories
arXiv Machine Learning
Aug 26
Steering Recurrent Reasoners at Inference Time with Readout Feedback
arXiv:2608.24136v1 Announce Type: new Abstract: Recurrent models, which repeatedly update latent states with shared computation blocks, have emerged as powerful architectures for solving complex reas...
Hugging Face Trending Papers
Oct 7
Invariant-Measure Reasoners: Stable Representations for Latent Reasoning
Latent reasoning models repeatedly update a latent state using the same recurrent block. As the recurrent depth increases, the sequence of latent states may converge to a compact subset of the state s...
arXiv Machine Learning
Oct 9
Invariant-Measure Reasoners: Stable Representations for Latent Reasoning
Invariant-Measure Reasoners (ImR) introduce a stable representation for latent reasoning models by using an invariant measure that captures the long‑run distribution of latent states on a compact subset of the state space. Unlike traditional approaches that predict from a single latent state, ImR predicts from the expectation of the prediction head’s output under this invariant measure, thereby reducing prediction instability across recurrent depths. Experiments on maze and Sudoku tasks show that both fine‑tuning and training from scratch with ImR improve accuracy and allow models to exploit non‑fixed‑point dynamics that were previously destabilizing.
arXiv AI
Jul 14
Interpreting Latent CoT Reasoning as Dynamical Systems
arXiv:2607. 09698v1 Announce Type: new Abstract: Recent latent reasoning methods, such as CODI and COCONUT, face a fundamental interpretability problem: they maintain multiple superimposed candidate traces in the hidden space at each step, unlike explicit- CoT, which follows a single transparent reasoning trace.
arXiv AI
Jun 30
Invariant Reasoning Directions in Latent Trajectories of Language Models
arXiv:2606. 29164v1 Announce Type: cross Abstract: Latent reasoning models perform multi-step inference directly in hidden-state space, yet the structure of these latent reasoning trajectories remains poorly understood.
arXiv Computation and Language
Sep 22
LoopCD: Loop-wise Contrastive Decoding for Improving Reasoning in Looped Language Models
arXiv:2609.24196v1 Announce Type: new Abstract: Looped Language Models (LoopLMs) perform "latent reasoning" by recursively refining internal latent representations with shared weights, offering a mor...