most citedMemory-R1: Enhancing Large Language Model Agents to Manage and Utilize Memories via Reinforcement Learning

2 citations · 2 across the 4 of their papers we have counts for

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cs.AI2026

DeepLook: Deeper Thinking with Lookahead

Tingxin Yang, Zefeng Wang, Mengyue Wang +2

Inference-time scaling has emerged as a powerful paradigm for improving large language model reasoning, often delivering larger gains on difficult reasoning tasks than parameter sc…

cs.AI2026

Policy of Thoughts: Scaling Test-Time Training for LLM Reasoning via Online Policy Evolution

Zhengbo Jiao, Hongyu Xian, Qinglong Wang +5

Large language models (LLMs) struggle with complex, long-horizon reasoning due to instability caused by their frozen policy assumption. Current test-time scaling methods treat exec…