# ContraVis: Evidence-Grounded Visual Analytics for Contradiction Review in Legal Contracts

arXiv: https://arxiv.org/abs/2609.27014 · HTML: https://mlsanity.com/paper/2609.27014/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 26 references that are in the map.
- First posted on arXiv 22 Sep 2026.

## Builds on (most cited first, 18 of 18)

- [CUAD: An Expert-Annotated NLP Dataset for Legal Contract Review](https://mlsanity.com/paper/2103.06268/) (2021)
- [ContractNLI: A Dataset for Document-level Natural Language Inference for Contracts](https://mlsanity.com/paper/2110.01799/) (2021)
- [LegalBench-RAG: A Benchmark for Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2408.10343/) (2024)
- [Re2G: Retrieve, Rerank, Generate](https://mlsanity.com/paper/2207.06300/) (2022)
- [Don't Forget to Connect! Improving RAG with Graph-based Reranking](https://mlsanity.com/paper/2405.18414/) (2024)
- [Fast and Accurate Factual Inconsistency Detection Over Long Documents](https://mlsanity.com/paper/2310.13189/) (2023)
- [ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models](https://mlsanity.com/paper/2311.09182/) (2023)
- [ContractEval: Benchmarking LLMs for Clause-Level Legal Risk Identification in Commercial Contracts](https://mlsanity.com/paper/2508.03080/) (2025)
- [VIANA: Visual Interactive Annotation of Argumentation](https://mlsanity.com/paper/1907.12413/) (2019)
- [LegalWiz: A Multi-Agent Generation Framework for Contradiction Detection in Legal Documents](https://mlsanity.com/paper/2510.03418/) (2025)
- [A Survey of Classification Tasks and Approaches for Legal Contracts](https://mlsanity.com/paper/2507.21108/) (2025)
- [What to Read in a Contract? Party-Specific Summarization of Legal Obligations, Entitlements, and Prohibitions](https://mlsanity.com/paper/2212.09825/) (2022)
- [The Hidden Structure -- Improving Legal Document Understanding Through Explicit Text Formatting](https://mlsanity.com/paper/2505.12837/) (2025)
- [LegalVis: Exploring and Inferring Precedent Citations in Legal Documents](https://mlsanity.com/paper/2203.02001/) (2022)
- [Detecting Logical Relation In Contract Clauses](https://mlsanity.com/paper/2111.01856/) (2021)
- [SF-RAG: Structure-Fidelity Retrieval-Augmented Generation for Academic Question Answering](https://mlsanity.com/paper/2602.13647/) (2026)
- [Metadata Extraction Leveraging Large Language Models](https://mlsanity.com/paper/2510.19334/) (2025)
- [Living Contracts: Beyond Document-Centric Interaction with Legal Agreements](https://mlsanity.com/paper/2602.01396/) (2026)

## Abstract (arXiv)

Legal contracts are structurally complex documents in which contradictions may emerge across distant and interconnected provisions. Although large language models (LLMs) improve legal language understanding, contradiction analysis remains a human-centered and evidence-grounded review task. We present ContraVis, a visual analytics system for human-in-the-loop contradiction analysis in legal contracts. The system models contracts as typed paragraph graphs that combine explicit contractual references with semantic relationships between paragraphs. This graph plays a dual role: it conditions LLM reasoning and serves as the interactive representation the analyst explores, keeping model context and human inspection aligned across coordinated views. In a controlled comparison, graph-conditioned reasoning recovered more injected contradictions than standalone LLM analysis as contract length grew, while surfacing additional candidates for analyst validation. A formative study with contract-domain lawyers indicated that in-context evidence comparison supported contradiction validation, and we distill design implications for evidence-grounded, LLM-assisted document review.