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Casualty Litigation Risk & Nuclear Verdict Forecasting with Google ADK and OpenHands

Nuclear verdicts exceeding $10M increased by 40.7% in recent commercial liability litigations. You will build an automated litigation trajectory forecaster using Google ADK (Agent Development Kit) integrated with Gemini 1.5 Pro and OpenHands code agent execution. The agent must predict nuclear verdict probability with AUC-ROC > 0.85 and forecast damages within 15% error.

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What you are building

Develop an automated legal risk trajectory predictor using Google ADK and OpenHands agent execution to project potential nuclear verdict exposures.

How work is evaluated

Evaluation

Evaluate nuclear verdict probability forecasting accuracy on 200 historical commercial auto liability cases.

Datasets

Public datasets, plus the private datasets you have access to.

How submissions are scored

Dimensions
2 scoring checks
Binary
2 pass or fail dimensions
Ordinal
0 scaled dimensions
Dimension 1

valid_prob_range

Probability must be strictly between 0.0 and 1.0.

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Dimension 2

auc_roc

Area under ROC curve for nuclear verdict classification. • target: 0.85 • range: 0.5-1

Binary check

This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.

Learning goals

  • Build a Google ADK agent using Gemini 1.5 Pro to parse legal dockets and claim files.

  • Integrate OpenHands runtime environment to execute complex statistical regression scripts.

  • Analyze key nuclear verdict predictors like jurisdiction severity, plaintiff counsel history, and injury type.

  • Output structured litigation reserve adjustment recommendations for claims officers.

Resources

Dataset notes

Dataset of 250 commercial liability court dockets with venue ratings, injury scores, and final settlement/verdict amounts.

Challenge setup

Evaluate nuclear verdict probability forecasting accuracy on 200 historical commercial auto liability cases.

Challenge input

JSON with case_id, venue_county, injury_severity_index, plaintiff_firm_type, initial_demand

Google ADK

Native framework for building structured, multimodal agents powered by Gemini.

OpenHands

Provides dynamic environment execution for code generation and mathematical fitting.

Evaluated output

JSON with nuclear_verdict_probability (0-1), expected_award_usd, recommended_reserve_usd

Evaluation checks
  • Probability must be strictly between 0.0 and 1.0.
  • Area under ROC curve for nuclear verdict classification. • target: 0.85 • range: 0.5-1
Configured evaluation evidence
  • Benchmark: NuclearVerdictBench-2026
  • Auc Roc target: 0.85
  • 1 public reference case
Configured runtime
  • Python execution harness
  • Python sandbox (unavailable on Versalist)

Shows the first task, up to three tools, and up to three evidence items.

View technical recipe

Configured tools

Environment
  • OpenHands · Optional
Action Space
  • Google · Required
  • OpenHands · Optional
  • Google Gemini · Optional
Orchestration
  • OpenHands · Optional

Evaluation contract

  • valid_prob_range · Weight 1
  • auc_roc · Weight 1

Metrics

  • auc_roc · Target 0.85

Benchmarks

  • NuclearVerdictBench-2026
  • general

Reference cases

  • 1 public reference case

Workflow tasks

  1. Task 1

    Input: JSON with case_id, venue_county, injury_severity_index, plaintiff_firm_type, initial_demand

    Output: JSON with nuclear_verdict_probability (0-1), expected_award_usd, recommended_reserve_usd

    Execution harness: Python

Evaluation tools

  • Google ADK · Agent Framework
  • OpenHands · Agent System Tool

Recipe freeze marker

No recipe freeze time is recorded.

Frequently Asked Questions about Casualty Litigation Risk & Nuclear Verdict Forecasting with Google ADK and OpenHands