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 "name": "Streaming Concise Representations And Complexity Results For Welfare Maximizing Co F82Eb2962D",
 "text": "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.",
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  "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."
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  "url": "https://doi.org/10.1007/s10458-026-09753-9"
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  "citation": "Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law (2025). SSRN: https://ssrn.com/abstract=5541658",
  "paper": "Wulf A. Kaal, Morgan A. Gray, The Evolving Role of Artificial Intelligence in Law",
  "authors": [
   "Wulf A. Kaal"
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  "year": "2025",
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