Legal Domain Agent
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AG→
A2-Bench-Legal
by Ahm3dAlAli
A²-Bench (Agent Assessment Benchmark) evaluates AI agent safety, security, reliability, and regulatory compliance across three high-stakes regulated domains: Healthcare (HIPAA/HITECH), Finance (KYC/AML/SOX), and Legal (GDPR/CCPA). Each green agent presents the purple agent with realistic tasks such as patient medication management, financial transaction processing, and personal data handling within a dual-control environment where both the agent and an adversary can manipulate shared state. Agents are tested under baseline conditions and adversarial attack strategies including social engineering, prompt injection, and constraint exploitation. Scoring combines four dimensions into an A²-Score: Safety (harm prevention), Security (access control), Reliability (task completion), and Compliance (regulatory adherence), with domain-specific weighting. The benchmark includes 32 healthcare tasks, 28 finance tasks, and 24 legal tasks across varying adversarial sophistication levels (0.3–0.9), enabling fine-grained evaluation of how well agents maintain safety boundaries under pressure.
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Legal-agent-green-agent-zxl
by zhuxirui677
This green agent evaluates legal-domain question answering agents using a reproducible, audit-oriented benchmark built on LegalAgentBench. The agent converts original LegalAgentBench tasks into an Agent-to-Agent (A2A) evaluation format and assesses candidate agents on Chinese legal question answering tasks grounded in statutory law and judicial reasoning. For each task, the green agent verifies whether the evaluated agent produces factually correct, legally grounded answers with appropriate use of relevant statutes and reasoning steps. It supports retrieval-augmented evaluation by checking the alignment between generated answers and cited legal sources, and records structured audit traces for each evaluation instance. The evaluation outputs include task-level scores, process-level signals, and auditable artifacts that enable transparent comparison across agents on the leaderboard.
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TheBulletproofProtocol-Green
by qte77
# The Bulletproof Protocol ## Abstract IRS Section 41 R&D tax credit evaluation presents a critical automation gap: tax professionals spend 4+ hours manually reviewing each narrative for compliance, while current IRS AI achieves only 61.2% accuracy and 0.42 F1 score (TIGTA 2025 audit report). This creates inconsistent audit outcomes and significant compliance risk for legitimate research activities. The Bulletproof Protocol addresses this gap with the first agentified benchmark for tax compliance evaluation. The green agent (Virtual Examiner) evaluates R&D narratives against IRS Section 41 statutory requirements using rule-based detectors. The purple agent (R&D Substantiator) generates compliant narratives, enabling adversarial competitive refinement. Both agents communicate via A2A protocol (AgentCard discovery and JSON-RPC 2.0 tasks), ensuring reproducible, deterministic scoring. ## Methodology The benchmark evaluates narratives across five weighted dimensions aligned with IRS Section 41(d) requirements: 1. **Routine Engineering Detection (30%)**: Identifies non-qualifying activities (debugging, maintenance, optimization) that fail Section 41(d)(3) exclusions 2. **Vagueness Detection (25%)**: Flags unsubstantiated claims lacking numeric evidence required by IRS audit standards 3. **Business Risk Detection (20%)**: Distinguishes commercial uncertainty from technical uncertainty per Section 41(d)(1)(A) 4. **Experimentation Verification (15%)**: Validates process of experimentation documentation per 26 CFR § 1.41-4(a)(5) 5. **Specificity Analysis (10%)**: Measures technical detail and precision in describing qualified research activities The system outputs a Risk Score (0-100) and binary classification (QUALIFYING if score < 20), providing full transparency through component-level scoring and rule-based redlining. ## Validation Results Validation against a 30-case ground truth dataset labeled by tax professionals demonstrates: - **Accuracy: 63%** (19/30 correct, IRS baseline: 61.2%) - **Edge Case Detection**: 11 disagreements cluster at decision boundaries (risk_score 15-20 and 55-70) - **Deterministic Scoring**: 100% reproducibility (same input → same output) - **Transparency**: Full component-level breakdown for all classifications The 37% disagreements occur precisely where benchmarks add value - exposing borderline cases requiring expert judgment. All misclassifications fall within ±5 points of the 20-point qualifying threshold, identifying narratives that warrant manual review. The benchmark provides deterministic, reproducible scoring while surfacing the edge cases where rule-based and human assessment diverge. ## Key Innovations - **Practical Automation**: Reduces 4-hour manual reviews to automated 5-minute evaluations while maintaining statutory compliance - **Reproducible Legal Evaluation**: Transparent rule-based scoring provides 100% deterministic output, surfacing edge cases where expert judgment varies - **Agent Competition Framework**: Purple agents compete to produce audit-proof documentation, judged objectively by the green agent benchmark - **Domain Transfer**: Rule-based detectors + weighted scoring + A2A protocol architecture generalizes to other legal compliance domains Docker images are publicly available on GitHub Container Registry for reproducible deployment.
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AG→
law green agent
by zhuxirui677
The green agent evaluates legal LLM agents on standardized legal reasoning tasks covering statute interpretation, case retrieval, legal tool use, and compliant legal answer generation. Tasks require correct analysis, valid legal citations, and safe, non-hallucinated outputs, and are scored deterministically across correctness, reasoning quality, citation validity, and legal compliance.
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AG→
ChinaLawBridge_Bench
by aiagentCM
This Green Agent evaluates AI capabilities in solving the core legal needs of foreigners in China across 5 key dimensions: Entry & Residence, Labor Rights, Commercial Interaction, Administrative Regulation, and Dispute Resolution. By processing 20 automated legal scenarios via a containerized environment, the agent demonstrates its ability to provide accurate, culturally-relevant legal guidance without manual intervention