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- Identify gaps in GraphRAG, tooluse, and multi-agent coordination benchmark cases. - Add deterministic fixtures under GOLDEN/datasets/**. - Update `cases.json` in the respective directories to include the new cases. - Keep deterministic outputs stable by hardcoding generated data. - Update `evaluation/scoring/evidence.ts` to export missing functions. Co-authored-by: BrianCLong <6404035+BrianCLong@users.noreply.github.com>
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Code Review
This pull request expands the evaluation suite by adding new datasets and test cases for GraphRAG, multi-agent resource allocation, and tool-use scenarios. It also introduces scoring functions for evidence retrieval and tool efficiency. Feedback focused on correcting the naming of the evidence scoring function—which currently calculates recall rather than precision—optimizing its performance using a Set, and handling edge cases in the tool efficiency calculation where zero steps are taken.
| export function scoreEvidencePrecision(required: string[], provided: string[]): number { | ||
| if (required.length === 0) return 1.0; | ||
| const hits = required.filter(r => provided.includes(r)).length; | ||
| return hits / required.length; | ||
| } |
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The function name scoreEvidencePrecision is misleading because the implementation calculates Recall (hits / required.length) rather than Precision (hits / provided.length). Additionally, using provided.includes(r) inside a filter results in O(N * M) complexity. Using a Set for the provided items improves performance to O(N + M).
| export function scoreEvidencePrecision(required: string[], provided: string[]): number { | |
| if (required.length === 0) return 1.0; | |
| const hits = required.filter(r => provided.includes(r)).length; | |
| return hits / required.length; | |
| } | |
| export function scoreEvidenceRecall(required: string[], provided: string[]): number { | |
| if (required.length === 0) return 1.0; | |
| const providedSet = new Set(provided); | |
| const hits = required.filter(r => providedSet.has(r)).length; | |
| return hits / required.length; | |
| } |
| export function scoreToolEfficiency(optimalSteps: number, actualSteps: number): number { | ||
| if (actualSteps <= optimalSteps) return 1.0; | ||
| return Math.max(0, optimalSteps / actualSteps); | ||
| } |
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The current implementation returns 1.0 (perfect efficiency) when actualSteps is 0 and optimalSteps is greater than 0. In a benchmark context, taking zero steps when some are required usually indicates a failure to attempt the task. It is safer to return 0.0 in this case unless optimalSteps is also 0.
| export function scoreToolEfficiency(optimalSteps: number, actualSteps: number): number { | |
| if (actualSteps <= optimalSteps) return 1.0; | |
| return Math.max(0, optimalSteps / actualSteps); | |
| } | |
| export function scoreToolEfficiency(optimalSteps: number, actualSteps: number): number { | |
| if (actualSteps === 0) return optimalSteps === 0 ? 1.0 : 0.0; | |
| if (actualSteps <= optimalSteps) return 1.0; | |
| return Math.max(0, optimalSteps / actualSteps); | |
| } |
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| export function scoreEvidencePrecision(required: string[], provided: string[]): number { | ||
| if (required.length === 0) return 1.0; | ||
| const hits = required.filter(r => provided.includes(r)).length; | ||
| return hits / required.length; |
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This implementation divides by required.length, which computes recall/coverage, not precision. If a run returns all required evidence plus many irrelevant IDs, the score still becomes 1.0 (for example, 2 required hits out of 20 provided), so false positives are never penalized and benchmark precision is systematically inflated. Precision should use the provided evidence count (ideally deduplicated) as the denominator.
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Expanded Summit Bench benchmark coverage by adding deterministic fixtures and updating cases for GraphRAG, tooluse, and multi-agent coordination.
PR created automatically by Jules for task 6701132027924806688 started by @BrianCLong