用结构化表格提升模型搜索多样性，比纯文本更准更全。
Diversed Model Discovery via Structured Table Discovery
- 通过表格发现算子挖掘模型卡片中的关键数据表
- 在597个查询上实现更高证据覆盖率与多样性
- 适合需要对比不同模型性能的研究者和开发者
模型卡片通过文本描述与结构化数据（如性能、配置、数据集表格）呈现模型行为。现有模型搜索系统主要依赖文本语义相似性，易导致结果同质化，限制探索空间。我们认为模型搜索本质是对比性的：用户希望找到任务对齐但可量化的差异化模型。我们假设这种平衡需基于浓缩、高质量的证据而非冗长文本，而这些证据多集中于结构化表格。本文提出StructuredSemanticSearch框架，基于ModelTables基准构建。给定查询，该框架结合语义基线确保任务对齐，并通过结构感知管道使用可并集性、可连接性、关键词搜索等表格发现算子，定位相关模型卡片表格。检索到的表格在受控top-k预算下映射回模型卡片，实现文本与表格检索的公平比较。此外，通过面向方向的表格集成，将部分重叠或转置的证据表格整合为紧凑视图。评估方面，我们引入基于要点的可审计协议，从模型卡片中提取紧凑证据项，匹配查询到条件或意图特定的要点，衡量检索候选集的证据覆盖与多样性。该协议也为动态模型湖中的近似、基于证据的标注提供可扩展路径。597个模型推荐查询的实验表明，结构感知管道在要点覆盖上优于语义基线。
原文摘要 · Abstract (English)
Model cards describe model behavior through a mixture of textual descriptions and structured artifacts, including performance, configuration, and dataset tables. Existing model search systems rely predominantly on semantic similarity over text, which can produce homogeneous result sets and limit exploration of alternatives. We argue that model search is inherently comparative: users want models that are task-aligned yet differentiated in measurable ways. We hypothesize that this balance requires retrieval over condensed, high-quality evidence rather than verbose descriptions, and much of that evidence is concentrated in structured tables. We present StructuredSemanticSearch, a table-driven model search framework built on the ModelTables benchmark. Given a query, StructuredSemanticSearch combines a semantic baseline for task alignment with a structure-aware pipeline that discovers query-related model-card tables using table discovery operators such as unionability, joinability, and keyword search. Retrieved tables are mapped back to model cards under a controlled top-k budget, enabling fair comparison between text-based and table-based retrieval. Beyond retrieval, StructuredSemanticSearch adapts table integration to the model-table domain through orientation-aware integration, producing compact integrated views of tables from partially overlapping and sometimes transposed evidence tables. For evaluation, we introduce a nugget-based, auditable protocol that extracts compact evidence items from model cards, matches queries to condition- or intent-specific nuggets, and measures evidence coverage and diversity over retrieved model-card candidate sets. This protocol also provides a scalable path toward approximate, evidence-based labeling in dynamic model lakes. Experiments on 597 model-recommendation queries show improved nugget coverage for the structure-aware pipeline than semantic baseline
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