Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions? explores A benchmark suite to rigorously evaluate the stability of AI feature attribution methods in vision systems, revealing critical insights into their reliability under real-world perturbations.. Commercial viability score: 5/10 in Computer Vision Explainability.
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Canonical route: /paper/feature-attribution-stability-suite-how-stable-are-post-hoc-attributions
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Canonical ID feature-attribution-stability-suite-how-stable-are-post-hoc-attributions | Route /paper/feature-attribution-stability-suite-how-stable-are-post-hoc-attributions
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curl https://sciencetostartup.com/api/v1/agent-handoff/paper/feature-attribution-stability-suite-how-stable-are-post-hoc-attributionsMCP example
{
"tool": "get_paper",
"arguments": {
"arxiv_id": "2604.02532"
}
}source_context
{
"surface": "paper",
"mode": "paper",
"query": "Feature Attribution Stability Suite: How Stable Are Post-Hoc Attributions?",
"normalized_query": "2604.02532",
"route": "/paper/feature-attribution-stability-suite-how-stable-are-post-hoc-attributions",
"paper_ref": "feature-attribution-stability-suite-how-stable-are-post-hoc-attributions",
"topic_slug": null,
"benchmark_ref": null,
"dataset_ref": null
}Constellation, claims, and market context stay visible on the paper proof page even when commercialization rails are held back for incomplete proof receipts.
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