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  • Real-Time Reasoning Agents in Evolving Environments
    Despite advances in language model reasoning, existing approaches fail to account for this dynamic nature We introduce real-time reasoning as a new problem formulation for agents in evolving environments and build Real-Time Reasoning Gym to demonstrate it
  • Real-Time Reasoning Agents in Evolving Environments
    We introduce real-time reasoning as a new problem formulation for bringing reasoning capabilities to agents operating in evolving environments, featuring AgileThinker architecture that combines System 1 and System 2 reasoning paradigms
  • Real-Time Reasoning Agents in Evolving Environments - arXiv. org
    We introduce real-time reasoning as a new problem formulation for agents in evolving environments and build Real-Time Reasoning Gym to demonstrate it
  • R -T R AGENTS IN ENVIRONMENTS - OpenReview
    To evaluate the real-time reasoning capabilities of agents, we need an environment that is (1) dy-namic: the environment state continuously updates without waiting for the agent’s decision; (2) cognitively challenging: the tasks should be challenging enough so that logical reasoning is needed;
  • Real-Time Reasoning Agents in Evolving Environments
    We introduce real-time reasoning as a new problem formulation for agents in evolving environments and build Real-Time Reasoning Gym to demonstrate it
  • 演化环境中的实时推理智能体 | alphaXiv
    本研究通过将“实时推理”正式化为一个独特的挑战，并提出了 AgileThinker（一种同时支持快速反应和深思熟虑规划的双线程代理架构），来解决这一根本性限制。 作者通过引入反映真实世界动态的时间约束，重新定义了代理与环境的交互范式。 与环境无限期等待代理行动的传统回合制系统不同，他们的“实时推理训练场”以固定速率前进，无论代理的计算状态如何。 关键创新在于使用 token 计数作为与硬件无关的时间代理。 这种方法利用了 LLM 解码时间与输出长度之间近乎线性的关系： 其中 $T$ 是推理时间， ${N}_{T}$ 是生成的 token 数量，TPOT 代表每个输出 token 的时间。
  • GitHub - wenyl22 RealtimeGym
    Realtime Reasoning Gym is a specialized evaluation framework for testing how well language agents can reason and make decisions under real-time constraints
  • Real-Time Reasoning Agents in Evolving Environments - ADS
    Despite advances in language model reasoning, existing approaches fail to account for this dynamic nature We introduce real-time reasoning as a new problem formulation for agents in evolving environments and build Real-Time Reasoning Gym to demonstrate it
  • Real-Time Reasoning Agents in Evolving Environments | alphaXiv
    View recent discussion Abstract: Agents in the real world must make not only logical but also timely judgments This requires continuous awareness of the dynamic environment: hazards emerge, opportunities arise, and other agents act, while the agent's reasoning is still unfolding Despite advances in language model reasoning, existing
  • ICLR2026 论文汇总 · 1529篇论文解读，每篇5分钟读懂核心思想
    提出AutoFigure——第一个基于"推理渲染"范式的Agent框架，通过解耦结构布局规划和美学渲染两阶段自动从长科学文本生成达到出版质量的科学插图，配合首个大规模基准FigureBench（3,300对）进行系统评估，66 7%的生成结果被原作者认为可用于camera-ready版本。





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