arXiv AI July 22, 2026 By Wei-Rui Chen, Samar M. Magdy, Chiyu Zhang, Wenhui Zhu, Zhipeng Wang, Muhammad Abdul-Mageed

LatentMT: Machine Translation with Latent Reasoning

arXiv:2607. 18618v1 Announce Type: cross Abstract: Latent-reasoning looped language models (LoopLMs) offer a different scaling path for machine translation (MT): instead of increasing parameter count or emitting explicit chain-of-thought tokens, they spend additional recurrent computation inside hidden states.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

arXiv AI

Jul 29

Penelope: Localized Latent Recurrence for Efficient Structured Reasoning

arXiv:2607. 25915v1 Announce Type: new Abstract: Complex structured reasoning tasks often require additional computation, yet current language models obtain it mainly by increasing parameter scale or by serializing intermediate steps as chain-of-thought (CoT) tokens.

By Yutong Chen, Shouqian Shi, Xinran Liu, Haochen Wang, Jiaying Wang, Tianxing Xu, Yuanxi Wang, Zirui Ding

llms diffusion efficiency benchmarks

arXiv AI

Aug 3

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation

arXiv:2607. 29287v1 Announce Type: cross Abstract: Multi-domain machine translation (MDMT) poses a unique challenge due to varying levels of linguistic complexity across domains.

By Yongshi Ye, Biao Fu, Chongxuan Huang, Yidong Chen, Xiaodong Shi

llms nlp reinforcement-learning fine-tuning benchmarks

arXiv AI

Jun 16

Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate

arXiv:2606. 16360v1 Announce Type: cross Abstract: Chain-of-thought (CoT) prompting improves reasoning in large language models (LLMs) by externalizing intermediate computation as discrete text tokens, but this textual interface also introduces redundancy and inference overhead.

By Hanyu Lin, Min Cai, Jiawei Wen, Haodi Zhang

llms

arXiv AI

Aug 11

Think Deep, Speak Once: Relit, A Recursive Latent Implicit Transformer Framework

arXiv:2608. 08113v1 Announce Type: new Abstract: Chain-of-Thought (CoT) prompting has become the dominant paradigm for eliciting reasoning in Large Language Models (LLMs), yet it creates substantial computational overhead by forcing models to externalize intermediate reasoning steps as discrete tokens.

By Abhishek Panwar, Maheep Singh, Saksham Bansal

llms benchmarks

arXiv AI

Jun 16

Latent Thought Flow: Efficient Latent Reasoning in Large Language Models

arXiv:2606. 16222v1 Announce Type: new Abstract: Large Language Models (LLMs) increasingly rely on intermediate reasoning, yet explicit Chain-of-Thought (CoT) suffers from a linguistic space bottleneck: each thought must be decoded into tokens, causing high inference overhead.

By Xiandong Zou, Jing Huang, Jianshu Li, Pan Zhou

llms fine-tuning

arXiv Machine Learning

Aug 4

Native Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages

arXiv:2608. 00533v1 Announce Type: cross Abstract: Large Language Models have achieved substantial progress in reasoning capabilities.

By Sean Gip Lim, William Chandra Tjhi, Hai Leong Chieu

llms rag agents fine-tuning efficiency benchmarks safety

Related stories

Penelope: Localized Latent Recurrence for Efficient Structured Reasoning

Translation with Thought: Difficulty-Adaptive Reasoning via Reinforcement Learning for Multi-Domain Machine Translation

Tyler: Typed Latent Reasoning for Language Models -- When to Think, What to Compute, and How Much to Allocate

Think Deep, Speak Once: Relit, A Recursive Latent Implicit Transformer Framework

Latent Thought Flow: Efficient Latent Reasoning in Large Language Models

Native Multilingual Chain-of-Thought Reasoning in Low-Resource Southeast Asian Languages