yogsoth-ai Multi-Stage Cascade Extraction
Mention detection → coreference clustering → [saliency] → relation extraction, all stages consuming the full prior stage's output. Covers SciERC/SciREX/NLP-Contribution-Graph — three methods with different stage counts but the same "consume-the-full-prior-layer" structure (graph correction S6: merged under the unifying rule "same action-sequence length → mergeable via parameterization").
Execution
Subagent — spawned via spawn-agent skill.
Why Direct From paper-fetch, Not Through unit-segmentation
This cascade discovers its own mention spans over the whole document rather than consuming pre-segmented sentence/clause units — sentence-level segmentation is the wrong granularity for a method whose relations are 99% cross-sentence (SciREX's own reported figure). This is a deliberate graph choice, not an oversight — see spec §5's flagged note before "fixing" this dependency.
Errors Compound Stage-Over-Stage
NLP Contribution Graph's own reported consistency figures fall from stage to stage (67.92% → 41.82% → 22.31%) — this is the shared risk profile of this whole method family, not specific to one method. Producing every stage's intermediate output (not just the final relations) is what makes this compounding visible and debuggable.
Available SOPs
| SOP | When to use |
|---|---|
| spawn-agent | Spawn a customized CC subagent with full MCP tool access. |