Parallel thinking has emerged as a promising paradigm for reasoning, yet it imposes significant computational burdens. Existing efficiency methods primarily rely on local, per-trajectory signals and lack principled mechanisms to exploit global dynamics across parallel branches. We introduce 2D probing, an interface that exposes the width-depth dynamics of parallel thinking by periodically eliciting intermediate answers from all branches. Our analysis reveals three key insights: non-monotonic scaling across width-depth allocations, heterogeneous reasoning branch lengths, and early stabilization of global consensus. Guided by these insights, we introduce $\textbf{Parallel-Probe}$, a training-free controller designed to optimize online parallel thinking. Parallel-Probe employs consensus-based early stopping to regulate reasoning depth and deviation-based branch pruning to dynamically adjust width. Extensive experiments across three benchmarks and multiple models demonstrate that Parallel-Probe establishes a superior Pareto frontier for test-time scaling. Compared to standard majority voting, it reduces sequential tokens by up to $\textbf{35.8}$% and total token cost by over $\textbf{25.8}$% while maintaining competitive accuracy.
Parallel-R1: Towards Parallel Thinking via Reinforcement Learning
Tencent AI Lab Seattle（腾讯人工智能实验室西雅图）；University of Maryland, College Park（马里兰大学帕克分校）；University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；City University of Hong Kong（香港城市大学）；Washington University in St. Louis（圣路易斯华盛顿大学）
StreamingThinker: Large Language Models Can Think While Reading
Shanghai Jiao Tong University（上海交通大学）；Eastern Institute of Technology, Ningbo（宁波东部技术研究院）；Ningbo Key Laboratory of Spatial Intelligence and Digital Derivative, Institute of Digital Twin（宁波空间智能与数字衍生关键实验室，数字孪生研究院）；Hong Kong Polytechnic University（香港理工大学）；Munich Center for Machine Learning, LMU（慕尼黑机器学习中心，莱茵-慕尼黑大学）
Turning Logic Against Itself : Probing Model Defenses Through Contrastive Questions
Ubiquitous Knowledge Processing Lab (UKP Lab), Department of Computer Science and Hessian Center for AI (hessian.AI), Technical University of Darmstadt（德累斯顿技术大学计算机科学系、海斯堡人工智能中心（hessian.AI）、通用知识处理实验室（UKP Lab））
针对推理驱动的隐蔽越狱漏洞，本文提出 POATE 对比推理攻击，并设计 Intent-Aware CoT 与 Reverse Thinking CoT 以检测恶意意图、拒绝有害输出。
Thinking Like a Botanist: Challenging Multimodal Language Models with Intent-Driven Chain-of-Inquiry
Department of Robotics and Mechatronics Engineering, University of Dhaka（达卡大学机器人与机电工程系）；Department of Computer Science and Engineering, University of Dhaka（达卡大学计算机科学与工程系）；Department of Agronomy, Gazipur Agricultural University（加兹ipur农业大学作物学系）；Department of Botany, University of Dhaka（达卡大学植物学系）
