Interactivity Demo

Interactive Research Visualization

This document showcases interactive components developed for the LegalChain benchmark. We blend data-rich Epoch-style visualizations with our scholarly "Document-Sized" design system.

Performance Trajectories

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Highlight Groups

Granular Skill Surface

Hover over labels for technical definitions of each benchmark milestone.

87.3%
Citation Pass Rate
92.1%
Structural IRAC Score
0.94
Stability Index

Methodological Framework

Phase 1: Authority Identification

During the primary detection phase (Steps S1-S3), the runner evaluates a model's ability to resolve citations and identify governing law:

  • S1: Exact Case Resolution from Metadata
  • S2: Authority Retrieval from Fact Patterns
  • S3: Authority Validation (Overrule Checks)
Phase 2: Reasoning & Application

The reasoning phase (Steps S4-S7) transforms raw retrieval into legal application:

  • Delta Analysis
    The benchmark isolates the performance gain when a model provides an "Open-Book" Synthesis (S7) compared to a "Closed-Book" reasoning task (S6).

Developer Interface

LegalChain can be integrated into existing pipelines using our Python SDK. 

# Initialize the Agentic Chain
from legal10 import BenchmarkRunner

runner = BenchmarkRunner(
    model="claude-3-5-sonnet",
    temperature=0.0,
    runs=1
)

# Run end-to-end evaluation
results = runner.evaluate(
    dataset="supreme_court_2024",
    steps=["S1", "S4", "S8"]
)

# Capture integrity metrics
print(f"Citation Pass: {results.s8_success:.1%}")

Comparative Analysis

Model Identification Aggregate Score Citation (S8) IRAC Integrity