多通道场景下如何精准优化增量收益？这项研究给出可落地的策略学习方法，适合因果推断与增长算法从业者。
arXiv:2607.28182v1 Announce Type: new Abstract: E-commerce platforms must allocate fixed marketing budgets across multiple channels to maximize busine…
机器人策略学习新思路，PoLAR将潜在动作清晰分解为程度与模式，助力高效控制。
arXiv:2606.21139v1 Announce Type: cross Abstract: Latent action pretraining learns representations of visual change from pairs of observations, but ex…
提出联合训练策略与世界观模型的新方法，让语言智能体在复杂任务中表现更佳。
arXiv:2606.02388v1 Announce Type: new Abstract: Reinforcement learning (RL) improves large language model (LLM) agents by teaching them which actions …
破解LLM智能体多轮交互中策略遗忘难题，提出统一上下文演化框架，提升跨任务学习效率。
arXiv:2606.02304v1 Announce Type: new Abstract: LLM-based agents can solve multi-step interactive tasks by combining reasoning with environment feedba…
论文提出一种信念增强的对话生成策略，让模型在对话中主动选择澄清、拒绝或直接回答，提升智能交互的可靠性。
arXiv:2605.25831v1 Announce Type: cross Abstract: Large language models (LLMs) define a distribution over text, which can be viewed as a probabilistic…
新动作出现时，离线上下文bandit如何优化？这篇论文提出解决方案，提升推荐系统等场景的决策效果。
arXiv:2605.18509v1 Announce Type: new Abstract: Automated decision-making algorithms drive applications such as recommendation systems and search engi…
离线数据下的风险感知策略学习新框架，用悲观原则优化高风险场景的决策效果
arXiv:2605.15620v1 Announce Type: cross Abstract: We study risk-aware offline policy learning, aiming to learn a decision rule from logged data that i…