RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.
Journal of Legal Analysis , volume=
4 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 4representative citing papers
Adversarial compromise of tool outputs misleads agentic AI via breadth and depth attacks, revealing that epistemic and navigational robustness are distinct and often trade off against each other.
A neuro-symbolic system is proposed that uses formal logic to constrain LLM outputs so legal inferences stay faithful to source text.
PoliLegalLM, trained with continued pretraining, progressive SFT, and preference RL on a legal corpus, outperforms similar-scale models on LawBench, LexEval, and a real-world PoliLegal dataset while staying competitive with much larger models.
citing papers explorer
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Uncertainty Quantification for LLM-based Code Generation
RisCoSet applies multiple hypothesis testing to construct risk-controlling partial-program prediction sets for LLM code generation, achieving up to 24.5% less code removal than prior methods at equivalent risk levels.
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How Adversarial Environments Mislead Agentic AI?
Adversarial compromise of tool outputs misleads agentic AI via breadth and depth attacks, revealing that epistemic and navigational robustness are distinct and often trade off against each other.
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Bridging Legal Interpretation and Formal Logic: Faithfulness, Assumption, and the Future of AI Legal Reasoning
A neuro-symbolic system is proposed that uses formal logic to constrain LLM outputs so legal inferences stay faithful to source text.
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PoliLegalLM: A Technical Report on a Large Language Model for Political and Legal Affairs
PoliLegalLM, trained with continued pretraining, progressive SFT, and preference RL on a legal corpus, outperforms similar-scale models on LawBench, LexEval, and a real-world PoliLegal dataset while staying competitive with much larger models.