# ARGUS: Role-Aware Event Knowledge Graphs for U.S. Employment-Discrimination Complaints

arXiv: https://arxiv.org/abs/2609.30184 · HTML: https://mlsanity.com/paper/2609.30184/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 25 references that are in the map.
- First posted on arXiv 24 Sep 2026.

## Builds on (most cited first, 14 of 14)

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- [Giveme5W1H: A Universal System for Extracting Main Events from News Articles](https://mlsanity.com/paper/1909.02766/) (2019)
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- [LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoning](https://mlsanity.com/paper/2605.28120/) (2026)
- [REGen: A Reliable Evaluation Framework for Generative Event Argument Extraction](https://mlsanity.com/paper/2502.16838/) (2025)
- [MetaGraph: A Large-Scale Meta-Analysis of GenAI in Financial NLP (2022-2025)](https://mlsanity.com/paper/2509.09544/) (2025)

## Abstract (arXiv)

U.S. employment-discrimination complaints describe complex event sequences that are not explicitly captured by lexical or embedding-based representations alone. We present ARGUS, a source-grounded pipeline that combines a 5W1H-inspired schema, legal-domain models, and LLM-based structured generation to construct document-level Event Knowledge Graphs (EKGs) from CourtListener complaints. ARGUS extracts fact-bearing statements, builds chunk-level event graphs with participant, temporal, and causal structure, and merges them into document-level representations. We evaluate graph quality through human and multi-model assessment and test downstream utility on claim classification and legal QA. The graph-structured classifier outperforms raw and linearized baselines on the held-out set, and EKG-only retrieval improves document-scoped QA, while open-retrieval gains remain limited by low first-stage candidate recall. These results suggest that EKGs are most useful for organizing and reasoning over evidence once relevant material has been retrieved.