yogsoth-ai Baseline Establishment
Strategy Routing
| User Intent | Route To |
|---|---|
| Find all methods for a task | method-inventory |
| Extract scores from papers | performance-extraction |
| Normalize conditions across papers | condition-standardization |
| Check reproducibility / discrepancies | discrepancy-analysis |
| Track progress over time / headroom | progress-quantification |
Manifest
Strategies (5)
| Strategy | Purpose |
|---|---|
| method-inventory | Comprehensively identify all relevant methods for a task |
| performance-extraction | Systematically extract performance data and conditions from papers |
| condition-standardization | Standardize evaluation condition differences across papers |
| discrepancy-analysis | Identify discrepancies between reported and reproducible scores |
| progress-quantification | Track performance progress over time, quantify remaining headroom |
Tactics (3)
| Tactic | Purpose |
|---|---|
| leaderboard-harvesting | Systematically collect performance data from platforms and papers |
| condition-normalization | Compare and standardize experimental conditions across papers |
| progress-curve-construction | Build performance-over-time progress curves |
Subagent SOPs (10)
| SOP | Purpose |
|---|---|
| method-discovery | Identify methods via literature, leaderboards, citation chains |
| score-extraction | Extract (Task, Dataset, Metric, Score, Conditions) tuples |
| condition-cataloging | Record evaluation conditions per method |
| reproducibility-checklist-audit | Assess paper against ML Reproducibility Checklist |
| performance-table-assembly | Assemble unified comparison table |
| compute-normalization | Normalize results by compute budget |
| discrepancy-identification | Compare same-method scores across sources |
| headroom-estimation | Estimate ceiling vs current SOTA gap |
| progress-curve-fitting | Construct performance-over-time data |
| baseline-synthesis | Produce final structured baseline report |
Budget Table
| Strategy | Methods | Data Points | Web Searches |
|---|---|---|---|
| method-inventory | 50 | 0 | 60 |
| performance-extraction | 30 | 150 | 40 |
| condition-standardization | 20 | 60 | 30 |
| discrepancy-analysis | 15 | 45 | 30 |
| progress-quantification | 30 | 100 | 40 |
| TOTAL | 145 | 355 | 200 |
MCP Tools
| MCP Server | Tools |
|---|---|
| brave-search | brave_web_search, brave_llm_context |
| apify | rag-web-browser, google-scholar-scraper |
| alphaxiv | get_paper_content, answer_pdf_queries |
| semantic-scholar | ss_paper, ss_relevance_search, ss_citations, ss_references |
Context Management
Campaign outputs are accumulated in the calling knowledge-acquisition context:
methods_inventory.json— All discovered methods with metadataperformance_data.json— Extracted scores with provenanceconditions_matrix.json— Standardized conditions per methoddiscrepancy_report.json— Flagged score inconsistenciesprogress_curves.json— Time-series performance databaseline_report.md— Final synthesized baseline document