Parthenon is a self-evolving legal-agent framework that factors components for traceability and uses an anti-leakage learning loop to improve from scored failures on legal matters.
Title resolution pending
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
A new evaluation framework using MMD on Biber features shows LLMs deviate from human linguistic distributions across registers, with closest models varying by register rather than size.
citing papers explorer
-
Parthenon Law: A Self-Evolving Legal-Agent Framework
Parthenon is a self-evolving legal-agent framework that factors components for traceability and uses an anti-leakage learning loop to improve from scored failures on legal matters.
-
How Human-Like Are Large Language Models? A Register-Aware Linguistic Evaluation Framework
A new evaluation framework using MMD on Biber features shows LLMs deviate from human linguistic distributions across registers, with closest models varying by register rather than size.