Papers by Shijia Xu

2 papers
RCBSF: A Multi-Agent Framework for Automated Contract Revision via Stackelberg Game (2026.findings-acl)

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Challenge: despite the adoption of Large Language Models (LLMs), contract revision remains impeded because generic models treat strict legal constraints as mere suggestions.
Approach: They propose a risk-constrained bilevel Stackelberg framework that models high-stakes revision as a strategic interaction rather than an open-ended conversation.
Outcome: The proposed framework achieves state-of-the-art performance with an average RRR of 84.21% and enhanced token efficiency.
Self-Correcting RAG: Enhancing Faithfulness via MMKP Context Selection and NLI-Guided MCTS (2026.findings-acl)

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Challenge: Existing approaches to retrieval-augmented generation still face problems with low context utilization and frequent hallucinations.
Approach: They propose a framework that reformulates retrieval and generation as constrained optimization and path planning.
Outcome: The proposed framework significantly improves reasoning accuracy on complex queries while reducing hallucinations.

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