As large language models (LLMs) are increasingly considered for high-stakes legal applications, a central question for trust and auditability is whether their outputs reflect genuine reasoning over the supplied legal materials, or merely surface statistical patterns absorbed during pre-training. We address this question at the mechanistic level by applying activation patching, a causal-mediation technique, to four open-source multilingual LLMs on two bar-exam datasets: U.S. MBE and Brazilian OAB. For each multiple-choice question we contrast a gold supporting passage with an irrelevant-but-plausible counterfactual. We then surgically replace residual-stream activations across layers and token regions and measure recovery of the logit difference. Gold passages exert a measurable but model- and linguistic-dependent causal effect; integration concentrates in the upper layers (a late-binding signature); and the size of the effect is strongly mediated by the linguistic and discourse structure of the passage itself, not just its relevance. We introduce a four-cluster taxonomy that operationalises these regimes and identifies cases where retrieved evidence is causally decisive, inert, or actively harmful.

Language change has been the subject of numerous studies in linguistics. However, due to the dynamic and complex nature of this phenomenon, and to the difficulty of obtaining extensive real data of language in use, some of its aspects remain obscure. In recent years, nonetheless, research has used computational modeling to simulate features related to variation, change, propagation, and evolution of languages in speech communities, finding compelling results. In this article, agent-based modeling and simulation is used to study language change. Drawing on previous studies, a speech community was modeled using Zachary’s karate club network, a well-established small-world network model in the field of complex systems. Idiolects were assigned through numerical values for each agent. The results demonstrate that the centrality of each agent in the network, interpreted as social prestige, appears to be a factor influencing change. Additionally, the nature of idiolects also seems to impact the spread of linguistic variants in the language change process. These findings complement the theoretical understanding of the language change phenomenon with new simulation data and provide new avenues for research.