CLAIM : An Intent-Driven Multi-Agent Framework for Analyzing Manipulation in Courtroom Dialogues (2025)

Abstract: Courtrooms are places where lives are determined and fates are sealed, yet they are not impervious to manipulation. Strategic use of manipulation in legal jargon can sway the opinions of judges and affect the decisions. Despite the growing advancements in NLP, its application in detecting and analyzing manipulation within the legal domain remains largely unexplored. Our work addresses this gap by introducing LegalCon, a dataset of 1,063 annotated courtroom conversations labeled for manipulation detection, identification of primary manipulators, and classification of manipulative techniques, with a focus on long conversations. Furthermore, we propose CLAIM, a two-stage, Intent-driven Multi-agent framework designed to enhance manipulation analysis by enabling context-aware and informed decision-making. Our results highlight the potential of incorporating agentic frameworks to improve fairness and transparency in judicial processes. We hope that this contributes to the broader application of NLP in legal discourse analysis and the development of robust tools to support fairness in legal decision-making. Our code and data are available at CLAIM.

## introduction courtroom decisions have significant legal and societal implications, shaping legal precedents and affecting lives. however, the inherently adversarial and strategic nature of legal discourse fosters an environment where linguistic manipulation is prevalent. tactical orchestration of manipulation can shape perceptions, steer arguments, and ultimately influence judicial outcomes. over the years, studies like @xcite , @xcite and @xcite have explored the various techniques, covert and overt, employed to manipulate courtroom dynamics, which can manifest through crafted narratives and psychological attacks. @xcite * these authors contributed equally to this work figure 1 : an example of a courtroom conversation that contains manipulation, but chatgpt-4o fails to identify the primary manipulator and technique accurately. ## related works detecting and analyzing manipulation in conversations has been an emerging research focus, especially with the rise of large language models (llms) and their role in social, legal, and media contexts. several datasets and frameworks have emerged to explore different kinds of manipulation like mental manipulation and, persuasion, misinformation, and toxicity. the increasing prevalence of misinformation has also led to the development of specialized models designed for social manipulation and fake news detection, @xcite outlines strategies for mitigating manipulation in the llm era, emphasizing the need for explainability and interpretability. @xcite introduces a benchmark dataset manitweet for detecting manipulative tweets based on their distortion of news articles, highlighting the limitations of fact-checking systems and the need for better manipulation detection in social contexts. agent-based approaches have also shown promise. @xcite leverages llm agents for fake news detection, while (jeptoo and sun, 2024) proposes a multi-agent debate framework, where agents critique each other's outputs to improve factual accuracy. while these studies provide important insights into manipulation across domains, none of them focus on courtroom dialogues. ## methodology in courtroom conversations, manipulation is often quiet and deeply rooted in the speaker's intent, rhetorical strategy, and power dynamics. psychological studies show that individuals with a more substantial theory of mind (tom) are better at interpreting others' intentions and withstanding manipulation @xcite . a recent study also suggests that llms can improve their tom performance when guided by structured reasoning techniques like chain-of-thought (cot) prompting @xcite . building on these studies, we propose claim, a two-stage framework to improve manipulation analysis in courtroom dialogues: (1) intent-driven chain-of-thought prompting to extract speaker intent and (2) a multi-agent decision framework to analyze manipulation using these inferred intents with the help of agents. this framework allows our method to reason with the speaker's intent based on contextual evidence. figure 4 provides an overview of our framework, illustrating how each stage contributes to the final result. ## conclusion and future work this study introduces legalcon, a dataset of courtroom conversations aimed at detecting and analyzing manipulation. alongside this, we propose claim, a two-stage intent-driven multi-agent framework, to enhance the detection and analysis of manipulation in courtroom conversations. extensive experiments showed that our method consistently outperformed baseline models on various prompting techniques. however, the models struggled to accurately identify specific manipulative techniques, revealing a critical limitation. these findings highlight the inherently subjective and nuanced nature of manipulation. with legalcon and claim we hope to address a critical gap in nlp research at the intersection of law and manipulative language. since legal decisions are lasting and influential we hope that this lays a necessary foundation for further work in this field. future work could focus on expanding the legalcon dataset to include different types of cases and create a more comprehensive dataset. multi-lingual transcripts can be incorporated to enhance diversity and enable cross-cultural analysis of manipulative language. multi-modal frameworks can be explored to yield deeper insights into manipulation dynamics. integrating these advancements into real-world legal settings is a valuable opportunity and moving forward, efforts should focus on the responsible deployment of such models to support legal professionals and promote fairness in courtrooms. given that manipulation detection in the legal domain is a relatively underexplored area, further research in this field could provide valuable insights and open up new avenues for improving legal processes and contributing to the advancement of the application of technology in the legal domain.