# Reading Between The Lines: Modeling and Evaluating Behavioral Realism in Legal Simulation

arXiv: https://arxiv.org/abs/2608.13712 · HTML: https://mlsanity.com/paper/2608.13712/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 29 references that are in the map.
- First posted on arXiv 13 Aug 2026.

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

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models](https://mlsanity.com/paper/2308.11462/) (2023)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation](https://mlsanity.com/paper/1908.11540/) (2019)
- [DialogXL: All-in-One XLNet for Multi-Party Conversation Emotion Recognition](https://mlsanity.com/paper/2012.08695/) (2020)
- [EmoBERTa: Speaker-Aware Emotion Recognition in Conversation with RoBERTa](https://mlsanity.com/paper/2108.12009/) (2021)
- [Rehearsal: Simulating Conflict to Teach Conflict Resolution](https://mlsanity.com/paper/2309.12309/) (2023)
- [AgentCourt: Simulating Court with Adversarial Evolvable Lawyer Agents](https://mlsanity.com/paper/2408.08089/) (2024)
- [LLMs with Personalities in Multi-issue Negotiation Games](https://mlsanity.com/paper/2405.05248/) (2024)
- [Social Skill Training with Large Language Models](https://mlsanity.com/paper/2404.04204/) (2024)
- [Chinese Court Simulation with LLM-Based Agent System](https://mlsanity.com/paper/2508.17322/) (2025)
- [Generative Personality Simulation via Theory-Informed Structured Interview](https://mlsanity.com/paper/2502.12109/) (2025)
- [Eval4Sim: An Evaluation Framework for Persona Simulation](https://mlsanity.com/paper/2603.02876/) (2026)
- [AI-Assisted Moot Courts: Simulating Justice-Specific Questioning in Oral Arguments](https://mlsanity.com/paper/2603.04718/) (2026)

## Abstract (arXiv)

Deposition training requires attorneys to manage dynamic witness behavior, yet legal-AI evaluations largely focus on factual accuracy, reasoning, or response-level plausibility. We introduce WitnessSim, a deposition simulator driven by controllable legal personas. We use an evaluation framework separating behavioral realism from pedagogical usefulness. We assess realism through adversarial testing, blinded attorney comparison, and analysis of longitudinal behavioral trajectories. WitnessSim generally maintained plausible behavioral boundaries, and attorneys did not systematically prefer either original testimony or WitnessSim generated testimony. Pedagogical tests showed that witness behavior changed meaningfully in response to question form and attorney intervention without uniformly collapsing the assigned persona. Together, these results showcase a model of behavioral fidelity in legal simulations, and provide a framework for evaluating its performance.