← Back to all news
arXiv Machine Learning By Yuto Inui, Takuya Konishi, Yoshinobu Kawahara

Invariant-Measure Reasoners: Stable Representations for Latent Reasoning

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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...

By Shunsuke Kamiya, Masanori Koyama, Seongcheol Jeong, Fumiya Uchiyama, Kenji Kubo, Kohei Hayashi, Masahiro Suzuki, Yutaka Matsuo
More like this →
arXiv AI
Sep 2

Latent Recurrent Thoughts: Recurrent Refinement of Proposed Latents for Reasoning with Frozen LLMs

Latent Recurrent Thoughts (LRT) proposes a method for reasoning with frozen large language models by operating in the model’s continuous representation space. A small auxiliary network generates initial latent vectors, which a tiny recurrent reasoner refines over multiple steps, decoupling computational depth from model size. Experiments on symbolic and natural‑language reasoning tasks show that LRT outperforms prior frozen‑decoder continuous‑space methods and chain‑of‑thought prompting while using far less inference compute.

By Zhaoliang Chen, Jie Fu
llmsbenchmarks
More like this →
arXiv Computation and Language
Oct 1

LatentHarness: Learning Latent Actions for Memory and Reasoning via Counterfactual Policy Distillation

LatentHarness unifies memory access and latent reasoning by treating them as sequential latent actions—THINK, RECALL, and EXIT—within a language model. It is trained via counterfactual policy distillation, which evaluates the impact of each action on the emitted token and learns when to recall evidence versus continue reasoning. On six long‑context reasoning benchmarks, a 1.4B‑parameter LatentHarness model outperforms the strongest baselines by 2.8% and 10.0% relative, while running 5.9× faster than the leading long‑context baseline.

By Xiaoqiang Wang, Suyuchen Wang, Bang Liu
efficiencybenchmarks
More like this →