论文

本文将 Agentic 检索-生成循环形式化为有限时域部分可观测马尔可夫决策过程，显式建模其控制策略与状态转移，并构建了全面的分类体系与模块化架构分解，按规划机制、检索编排、记忆范式与工具调用行为对系统进行分类。This paper formalizes agentic retrieval-generation loops as finite-horizon partially observable Markov decision processes, explicitly modeling their control policies and state transitions, and develops a comprehensive taxonomy and modular architectural decomposition that categorizes systems by their planning mechanisms, retrieval orchestration, memory paradigms, and tool-invocation behaviors.

在 MS MARCO 与真实生产流量上的结果表明，自适应检索 cardinality 能够在不降低检索质量的前提下提升检索效率。Results on both MS MARCO and real-world production traffic suggest that adaptive retrieval cardinality can improve retrieval efficiency without degrading retrieval quality.

通过考察包含意识形态话语的 RAG 框架对 LLM 生成答案的影响，发现 RAG 框架倾向于将意识形态话语传递到 LLM 响应中，且采样温度对这种传递的强度有可测量的影响。Examining the influence of the RAG framework, comprising ideological discourses, in LLM-generated answers shows that the RAG framework is prone to transferring ideological discourses into LLM responses, with sampling temperature having a measurable impact on the strength of this transfer.

提出 AI Prototyper，一个开源 Figma 插件，通过分解与 RAG 流水线自动完成 GUI 原型设计，并引入人在回路编辑步骤，允许用户在渲染前审查、修改或扩展生成的功能列表。AI Prototyper is presented, an open-source Figma plugin that automates GUI prototyping through a decomposition and retrieval-augmented generation (RAG) pipeline, and introduces a human-in-the-loop editing step that lets users review, modify, or extend the generated feature list before rendering.

提出 PA-HDP（Prompt-Aware Dynamic Hierarchical Differential Privacy）框架，通过 prompt 感知的风险分层动态评估不同查询下的隐私风险，并采用自适应敏感实体替换与基于指数机制的文本选择，在保留语义可用性的同时提供差异化的隐私保护。A Prompt-Aware Dynamic Hierarchical Differential Privacy framework (PA-HDP) is proposed, which performs a prompt-aware risk hierarchy to dynamically assess privacy risks under different queries and applies adaptive sensitive entity replacement and exponential mechanism-based text selection to provide differentiated privacy protection while preserving semantic utility.