全新广义贝尔曼递推揭示序列决策中的三重对偶性，重塑强化学习与最优控制理论基础。
arXiv:2607.18077v1 Announce Type: cross Abstract: What gives the Bellman equation its form? We show that the recursive properties of optimal value fun…
流式系统里，如何用不确定性感知决策规则，智能触发LLM调用而避免过量消耗，融合轻量模型实现高效推理。
arXiv:2607.13048v1 Announce Type: cross Abstract: Streaming inference pipelines increasingly pair lightweight fast models with Large Language Models (…
最新研究揭示情绪诱导如何使大语言模型在序列决策中产生行为偏差，视角独特。
arXiv:2607.12631v1 Announce Type: new Abstract: As Large Language Models (LLMs) are increasingly deployed as autonomous agents in high-stakes domains,…
如何在不确知备选对象价值时，依序挑选最优群体？这篇论文给出新解法。
arXiv:2508.16386v2 Announce Type: replace Abstract: We study the problem of fair cohort selection under uncertainty, motivated by university admission…
交错感知与推理，PRISM让多模态智能体缩小决策差距，迈向复杂场景的序列决策新突破。
arXiv:2605.05407v2 Announce Type: replace Abstract: Scaling LLM-based embodied agents from text-only environments to complex multimodal settings remai…
提出软序列策略优化方法，用更平滑的目标函数处理序列决策问题，提升训练稳定性和性能。
arXiv:2602.19327v3 Announce Type: replace-cross Abstract: A significant portion of recent research on Large Language Model (LLM) alignment focuses on …
探究大语言模型何时能胜任序列决策任务的策略优化器，理论突破与实验验证并存
arXiv:2605.30719v1 Announce Type: cross Abstract: We study when large language models (LLMs) can serve as effective black-box policy optimizers for re…
本论文提出MATE方法，通过记忆累积转移嵌入解决上下文MDP中的长期依赖问题，为强化学习领域贡献新思路。
arXiv:2605.17431v1 Announce Type: new Abstract: We propose MATE, a simple yet effective memory architecture for solving Contextual Markov Decision Pro…