Co-citation predictability for statute retrieval decays over 20 years in Ukrainian court data, dropping 33-47% in MRR with non-uniform patterns across legal domains.
Bridging Legal Knowledge and AI: Retrieval-Augmented Generation with Vector Stores, Knowledge Graphs, and Hierarchical Non-negative Matrix Factorization; 2025
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ConflictQA benchmark shows LLMs fail to resolve conflicts between text and KG evidence and often default to one source, motivating the XoT explanation-based reasoning method.
SAT-Graph RAG is a new ontology-driven temporal graph framework for legal RAG that models Works vs. Expressions, reuses versioned components for temporal states, and treats legislative events as queryable Action nodes to support deterministic point-in-time and causal queries.
A 1D CNN with FastText embeddings classifies legal texts at 97.26% accuracy using 5.1 million parameters and runs over 13 times faster than BERT.
KnowPilot integrates knowledge retrieval and memory systems into generative agents to achieve better results on domain-specific tasks such as text generation.
citing papers explorer
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Temporal Decay of Co-Citation Predictability: A 20-Year Statute Retrieval Benchmark from 396M Ukrainian Court Citations
Co-citation predictability for statute retrieval decays over 20 years in Ukrainian court data, dropping 33-47% in MRR with non-uniform patterns across legal domains.
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Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method
ConflictQA benchmark shows LLMs fail to resolve conflicts between text and KG evidence and often default to one source, motivating the XoT explanation-based reasoning method.
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An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach
SAT-Graph RAG is a new ontology-driven temporal graph framework for legal RAG that models Works vs. Expressions, reuses versioned components for temporal states, and treats legislative events as queryable Action nodes to support deterministic point-in-time and causal queries.
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Towards Intelligent Legal Document Analysis: CNN-Driven Classification of Case Law Texts
A 1D CNN with FastText embeddings classifies legal texts at 97.26% accuracy using 5.1 million parameters and runs over 13 times faster than BERT.
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KnowPilot: Your Knowledge-Driven Copilot for Domain Tasks
KnowPilot integrates knowledge retrieval and memory systems into generative agents to achieve better results on domain-specific tasks such as text generation.