baseline-establishment

Research

SOTA Performance Baseline Campaign — 5 strategies for systematically collecting, standardizing, and analyzing performance data across methods. Produces standardized comparison tables, progress curves, and headroom analysis.

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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 metadata
performance_data.json — Extracted scores with provenance
conditions_matrix.json — Standardized conditions per method
discrepancy_report.json — Flagged score inconsistencies
progress_curves.json — Time-series performance data
baseline_report.md — Final synthesized baseline document

Available Strategies

Optional, no fixed order; the final leaf is always a sop.

Strategy	When to use
condition-standardization	Standardize evaluation condition differences across papers — 20 methods, 60 data points, 30 web searches budget
discrepancy-analysis	Identify discrepancies between reported and reproducible scores — 15 methods, 45 data points, 30 web searches budget
method-inventory	Comprehensively identify all relevant methods for a task — 50 methods, 60 web searches budget
performance-extraction	Systematically extract performance data and conditions from papers — 30 methods, 150 data points, 40 web searches budget
progress-quantification	Track performance progress over time, quantify remaining headroom — 30 methods, 100 data points, 40 web searches budget

Available SOPs

Optional, no fixed order; the final leaf is always a sop.

SOP	When to use
context-checkpoint	Append research process and results to the current Phase's context file. Each append MUST contain >=500 lines of markdown covering both process and results. Use this skill at plan-designated checkpoint points — typically after each strategy completes or at key decision nodes within a research Phase.
context-init	Create a new context file for a research Phase. Called once at Phase start to initialize the file that subsequent context-checkpoint calls will append to. Use this skill whenever a new research Phase begins and a fresh context file is needed.