feat: add 40 graph-native scenarios + 8-dim evaluation#203
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feat: add 40 graph-native scenarios + 8-dim evaluation#203sandeepkunkunuru wants to merge 1 commit intoIBM:mainfrom
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…ework Add 40 new scenarios (IDs 601-640) across 7 categories that test graph traversal, vector similarity, graph algorithms, and optimization: - Multi-hop dependency (8): BFS/DFS over DEPENDS_ON edges - Cross-asset correlation (6): Anomaly correlation across connected equipment - Failure pattern similarity (6): Vector search on FailureMode embeddings - Criticality analysis (5): PageRank, WCC, articulation points - Maintenance optimization (5): Constrained scheduling, Pareto optimization - Root cause analysis (5): Reverse TRIGGERED/DETECTED_ANOMALY traversal - Temporal pattern (5): MTBF, seasonal patterns, degradation trends Scenarios are tool-agnostic — any graph-capable agent can attempt them. All reference equipment, sensors, and failure modes in AssetOpsBench data. Also add 2 new evaluation dimensions (Graph Utilization, Semantic Precision) extending the original 6-dimensional framework to 8 dimensions. Signed-off-by: Sandeep Kunkunuru <sandeep.kunkunuru@gmail.com>
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Summary
New Scenario Categories
Design Principles
id,type,text,category,characteristic_form,deterministic,note)characteristic_form, evaluated by existing grading agentFiles
src/tmp/assetopsbench/scenarios/single_agent/graph_utterance.json— 40 scenariosdocs/extended_evaluation_8dim.md— evaluation framework documentationMotivation
Graph-based approaches to industrial maintenance enable query types that flat document stores cannot support: multi-hop dependency analysis, vector similarity on failure mode embeddings, PageRank criticality ranking, and Pareto-optimal scheduling. These scenarios provide a standardized way to evaluate such capabilities.