InvestigationWorlds: An Agentic Environment for Legal Investigation

Authors:Albert Yu Sun, Andrew Benard, Sil Hamilton, Anna Teresita A. Marcelo, Yong Jae Kim, Carl-Leander Henneking, Rundong Hu, Yuhong Wang, David Mimno, Bishan Yang, Igor Labutov

Abstract:We introduce InvestigationWorlds, an agentic environment for legal investigation. We build on an underused artifact of U.S. civil litigation: the summary judgment motion. This motion relies upon a record composed of real evidence exhibits, and results in a court-adopted hypothesis that is treated as ground truth for the purposes of deciding the motion. Each environment is built from a real U.S. Federal Court case retrieved from Public Access to Court Electronic Records (PACER) and augmented by an attorney-validated generation pipeline that synthesizes role-tagged documents around the original record. The resulting corpus admits multiple coherent factual readings, only one of which matches the court-adopted hypothesis. Evaluating on 100 cases, we find agents often commit to incorrect hypotheses despite retrieving relevant evidence, struggling to distinguish the court-adopted hypothesis from alternative hypotheses.

Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Computers and Society (cs.CY); Machine Learning (cs.LG)

arXiv:2610.04129 [cs.AI]

(or arXiv:2610.04129v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.04129 arXiv-issued DOI via DataCite (pending registration)