LENS：动态文档上的上下文内搜索新范式 LENS: In-Context Search via Latent Evidence Exploration over Dynamic Raw Documents

精读摘要

当 LLM 智能体需要从持续变化的原始文档集中寻找证据时，传统检索增强方案要预先做分块、嵌入和建索引，成本高、易过期，且证据粒度在提问之前就被固定。LENS 提出「上下文内搜索」新思路，将其形式化为潜在证据空间上的预算化证据定位，让证据的粒度与范围随查询动态决定。这一方法省去了维护持久索引的开销，更适合文档频繁更新的场景。 Traditional retrieval-augmented approaches pre-materialize evidence via fixed chunking, embeddings, and persistent indexes, which are costly, stale-prone, and committed to a granularity before the query is known. LENS formulates in-context search as Budgeted Evidence Localization over a latent evidence space induced by dynamic raw documents. This removes the need for persistent indexes in fast-changing document collections.