trace-citations

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🇺🇸

Original

English

🇨🇳

Translation

Chinese

Trace Citations

追踪引用关系

Map the citation graph around one focal paper into useful buckets. Use this when the human wants lineage, influence, and strong versus weak citation edges around a paper.

将某篇核心论文周边的引用图谱划分为实用类别。当用户想要了解某篇论文的传承脉络、影响力以及强/弱引用关联时，可使用此工具。

Arguments

参数说明

The positional argument is the focal paper query. Quote multi-word titles.
```
--depth 1|2
```
controls whether to expand a second hop from the strongest first-hop edges.
```
--max-references <n>
```
and
```
--max-citations <n>
```
cap the first-hop fetch sizes.
```
--second-hop-limit <n>
```
caps how many first-hop anchors get expanded at depth two.

位置参数为核心论文的查询内容。多词标题需加引号。
```
--depth 1|2
```
控制是否从最强的第一跳关联扩展至第二跳。
```
--max-references <n>
```
和
```
--max-citations <n>
```
限制第一跳的获取数量上限。
```
--second-hop-limit <n>
```
限制在深度为2时，有多少个第一跳锚点会被扩展。

Workflow

工作流程

Run
```
python scripts/run.py ...
```
.
Read
```
result.foundations
```
for strong references behind the focal paper.
Read
```
result.direct_descendants
```
for strong citing descendants.
Read
```
result.bridge_nodes
```
for medium-confidence connectors with rich context or intent signal.
Read
```
result.weak_edges
```
for low-signal edges that are probably less useful.
If
```
depth=2
```
, read
```
result.second_hop
```
only after the first-hop picture looks sensible.

运行
```
python scripts/run.py ...
```
。
查看
```
result.foundations
```
获取核心论文背后的强关联参考文献。
查看
```
result.direct_descendants
```
获取强关联的后续引用文献。
查看
```
result.bridge_nodes
```
获取具有丰富上下文或意图信号的中等置信度关联文献。
查看
```
result.weak_edges
```
获取低信号关联的文献，这类文献可能实用性较低。
如果设置了
```
depth=2
```
，需在第一跳图谱内容合理的前提下，再查看
```
result.second_hop
```
。

Output

输出结果

The script prints the unified JSON envelope described in
```
output_contract.md
```
.
The underlying workflow result is
```
CitationTraceResult.to_dict()
```
.

result.reference_count_examined

and

result.citation_count_examined

show the first-hop search breadth.

脚本会输出
```
output_contract.md
```
中描述的统一JSON包。
底层工作流程的结果为
```
CitationTraceResult.to_dict()
```
。

result.reference_count_examined

和

result.citation_count_examined

显示第一跳搜索的广度。

When To Escalate

需升级处理的情况

The focal paper resolves incorrectly.
The API returns very sparse context and intent data, making edge interpretation weak.
The first-hop graph is too noisy and needs a tighter focal paper choice before going to depth two.

核心论文解析错误。
API返回的上下文和意图数据极为稀疏，导致关联关系的解读可信度低。
第一跳图谱过于杂乱，需要先选定更精准的核心论文，再进行第二跳扩展。