kaal:position:2026-07-31-7691

Concise representations and complexity results for welfare-maximizing combinatorial assignment presents the following source proposition: These problems have broad applications, yet many important variants are computationally hard, including well-known instances in operations research, computational economics, and artificial intelligence. This proposition is pertinent to Kaal's source-bound claim that Benchmark results for legal LLMs may overstate capability because of data contamination: if a model saw a benchmark's ground truth answers during training, its measured performance reflects memorization rather than genuine generalization. The proposed response is an extension: the relationship should remain limited to the retrieved source proposition and the mapped Kaal claim unless fuller source review supports a broader conclusion.

• The response is limited to the retrieved source proposition and mapped Kaal claim unless fuller source review supports a broader conclusion.
• External evidence level: abstract indexed.
• Mapping review tier: moderate-confidence claim review.
• Primary mapping confidence: 0.3683.
• The source-to-claim mapping remains explicitly ambiguous and is published with that limitation.

Concise representations and complexity results for welfare-maximizing combinatorial assignment

kaal:claim:5541658-032
Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658
Source PDF sha256: e543a2d698fcd522d4d02e034cc9ee1344d0015d2c824b40b9e05ab7c0728c60

Evidence: abstract indexed
Review tier: moderate-confidence claim review
Mapping confidence: 0.3683
Mapping ambiguous: true

economicshistorical-responsescholarly-literaturecrossref

Affirmed in kaal-review:2026-07-31:streaming-etl-0010 on 2026-07-31. Review record.

Canonical markdown sha256: 06a373e51d951c7ad4b9bb4fc3e51990ff1d6426c17b829f01594d2e95128197
curl -s https://wulfkaal.github.io/positions/2026-07-31-7691.md | sha256sum