arXiv AI
Sep 3

CHASE: Cache-Hole-Adapted Skip Exit for Looped State-Space Language Models

The paper introduces CHASE, a cache‑hole‑adapted skip‑exit mechanism for looped state‑space language models, specifically Looped Mamba and Looped Hybrid Mamba‑Transformer. It shows that looping these architectures improves performance on controlled reasoning tasks and remains competitive in pre‑training benchmarks while using fewer distinct parameters. The cache‑hole adaptation allows selective skipping of recurrent steps during inference, maintaining perplexity close to full computation and achieving significant speedups.

By Zhenxuan Yu, Takeshi Kojima, Yutaka Matsuo, Yusuke Iwasawa
arXiv Machine Learning
Aug 19

Recirculation

The paper introduces recirculation, an inference‑time architectural enhancement for foundation models that reduces perplexity and improves accuracy on generation and reasoning tasks without adding significant latency. Recirculation adds a specific form of recurrence, enabling the model to function as a dynamical system that tracks belief states, and is distinct from chain‑of‑thought or depth‑recurrence methods. An adaptive variant requires minimal hyperparameter tuning and achieves notable gains on the Gemma3 family, including a 23% perplexity drop and a 21% accuracy increase on GSM8k.

By Michael C. Mozer, Shoaib Ahmed Siddiqui, Danny Sawyer, Sunny Sanyal, Rosanne Liu
arXiv AI
Aug 20

Looped Language Models Improve Compositional Tool Calling

The paper investigates the use of looped language models for compositional tool calling, where models must coordinate multiple API calls and maintain state across interactions. Experiments on API-Bank, BFCL, and NESTful show that recurrent computation generally improves compositional and dependency-aware tool use, with accuracy increasing as recurrent depth grows. Adaptive inference offers a better compute‑performance trade‑off by allocating extra computation only when necessary.

By Andrei Cristian Popescu, Haitz S\'aez de Oc\'ariz Borde, Pietro Li\`o
arXiv Machine Learning
Sep 4

RecurTrace: Adaptive Latent Reasoning with Loop-Time Memory

RecurTrace introduces adaptive latent reasoning for language models by allowing each looped layer to attend to its own past states and by using a halting head to decide when to stop iterating. This approach overcomes two limitations of prior latent recurrence methods: limited access to earlier computations and a fixed loop count that mismatches input difficulty. In experiments on MathQA, RecurTrace achieves 56.9% accuracy with an average of 2.0 loops, outperforming fixed‑depth baselines and other adaptive methods, and it also improves generation accuracy across a range of model sizes.

By Yuxiang Wang, Kunyu Feng, Yingda Shen, Haoning Xu, Junyu Wang, Zhizheng Wu