解决多地域搜索结果去重与多样性问题，提升跨区域信息获取效率。
Latent Objective Induction and Diversity-Constrained Selection: Algorithms for Multi-Locale Retrieval Pipelines
- 通过约束整数划分实现多地域资源分配，兼顾预算、比例与最小覆盖。
- 新算法使首方来源占比提升62%，同域重复减少89%。
- 适用于需要高多样性与跨域覆盖的多语言检索系统。
我们提出三种具有形式化正确性保证和复杂度边界的新算法，用于从排序后的搜索结果中选取多样化的多地域信息源。首先，将加权地域分配建模为带约束的整数划分问题，给出一个 $O(n "log n)$ 的算法，同时满足最小表示、预算耗尽和比例约束，且偏差严格小于1；其次，定义级联国家代码推断函数，作为异构信号（顶级域名结构、模型推断元数据、语言回退）的确定性优先链，证明其具备确定性与渐进降级特性；第三，引入 $κ$-域多样性约束，并设计 $O(|K| \cdot R)$ 算法，通过哈希表查找维持不变量，消除基于URL去重导致的聚合垄断问题。我们进一步形式化了隐式目标诱导（LOI），这是一种作用于提示空间的环境塑造算子，可引导下游模型行为而不限制输出集合可行性，并在弱假设下证明其收敛性。应用于多地域检索流水线后，该方法在120个双语查询上实现了首方来源比例提升62%，同域重复降低89%。
原文摘要 · Abstract (English)
We present three algorithms with formal correctness guarantees and complexity bounds for the problem of selecting a diverse, multi-locale set of sources from ranked search results. First, we formulate weighted locale allocation as a constrained integer partition problem and give an $O(n \log n)$ algorithm that simultaneously satisfies minimum-representation, budget-exhaustion, and proportionality-bound constraints; we prove all three hold with a tight deviation bound of $< 1$. Second, we define a cascaded country-code inference function as a deterministic priority chain over heterogeneous signals (TLD structure, model-inferred metadata, language fallback) and prove it satisfies both determinism and graceful degradation. Third, we introduce a $κ$-domain diversity constraint for source selection and give an $O(|K| \cdot R)$ algorithm that maintains the invariant via hash-map lookup, eliminating the aggregator monopolization pathology present in URL-level deduplication. We further formalize Latent Objective Induction (LOI), an environment-shaping operator over prompt spaces that steers downstream model behavior without restricting the feasible output set, and prove its convergence under mild assumptions. Applied to a multi-locale retrieval pipeline, these algorithms yield 62% improvement in first-party source ratio and 89% reduction in same-domain duplication across 120 multilingual queries.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。