3 papers
cs.AI2026
When Can a Machine Trust a Statute? A Survival Certificate for Machine-Extracted Legal Logic
Surya Saka
Statutes are increasingly parsed by machines before people read them, and the parsers disagree: on Missouri's statutes, two independently written extractors diverge on numeric-thre…
cs.LG2026
GreenLeaf Law Embed Tiny: A Compact Embedding Model for Legal Domain Retrieval
Surya Saka
We present GreenLeaf Law Embed Tiny, a 0.6B parameter embedding model for legal domain retrieval. GreenLeaf-Tiny achieves 75.11% on the Massive Legal Embedding Benchmark (MLEB) and…
cs.LG2026
Calibrated Trust, Not Sharper Prediction: An Empirical Test of Uncertainty Fusion
Surya Saka
A recurring proposal in legal AI is to improve case-outcome prediction by fusing uncertainty tools (evidence graphs with belief propagation, sequential Bayesian odds updating, Demp…