论文

提出一种审计方法，度量每个检索文档的留一因果影响，并对影响集中于低可信度文档的答案进行标记；并提出一种审计方法，度量每个检索文档的留一因果影响，并对影响集中于低可信度文档的答案进行标记。An audit is proposed that measures the leave-one-out causal influence of each retrieved document and flags answers whose influence concentrates on low-trust documents, and proposes an audit that measures the leave-one-out causal influence of each retrieved document and flags answers whose influence concentrates on low-trust documents.

本文提出 MathForm，一个通过 Mathlib 知识检索与验证引导的迭代优化来构建已验证训练数据的自动形式化框架，性能优于多个专用的 32B 自动形式化模型。MathForm is introduced, an autoformalization framework for constructing verified training data through Mathlib knowledge retrieval and verification-guided iterative refinement, and outperforming multiple specialized 32B autoformalizers.

跨数据集分析表明，语义切分在具有显式关系线索的抽取数据集（如 GM-CIHT 和 DDI）上表现更优，而固定切分在密集生化抽取和二分类场景（如 ChemProt 和 ADE）下仍具竞争力甚至更强。Cross-dataset analysis shows that semantic chunking improves extraction datasets with explicit relation cues, such as GM-CIHT and DDI, while fixed chunking remains competitive or stronger for dense biochemical extraction and binary classification settings such as ChemProt and ADE.

本文提出 SimLoss，一种面向单轮细粒度图像描述的无参考 embedding 空间目标；结果表明 embedding 空间监督能在单轮描述器延迟下恢复多阶段验证的质量。SimLoss is proposed, a reference-free embedding-space objective for single-pass fine-grained image captioning, and results show that embedding-space supervision can recover the quality of multi-stage verification at the latency of a single-pass captioner.

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.

提出 Functionalizer，一种无损预分词框架，将正字与结构变体分解为分词前的可组合操作码/操作数前缀流：以 Unicode 私有使用区编码的参数化变换操作符（操作码）为前缀，连接规范基础 token（操作数）。The Functionalizer is presented, a lossless pre-tokenizer framework that factors orthographic and structural variations into a compositional opcode/operand prefix stream before tokenization: a canonical base token (operand) prefixed by parametric transformation operators (opcodes) encoded in the Unicode Private Use Area.

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.

与现有句法语言模型在推理时对众多句法树求边际或在运行时丢弃句法不同，SiPE 以单一句法树为条件，在句法监督与推理成本之间建立了新的 Pareto 前沿。Unlike existing syntactic language models that marginalize over many parses at inference or discard syntax at runtime, SiPE conditions on a single parse, establishing a new Pareto frontier between syntactic supervision and inference cost.

本文提出 Syfer，一种用于多语言多跳问答的 synthesizer-folding 框架，默认推迟翻译而非直接应用翻译，在保持具有竞争力准确性的同时，在性能与计算成本之间取得良好平衡。The method Syfer is introduced, a synthesizer-folding framework for multilingual multi-hop question answering that defers translation rather than applying it by default and attains competitive accuracy while striking a favourable balance between performance and computational cost.