Researchers have developed an experimental framework for communicating with entities that categorize reality differently from humans. They created two language models with incompatible lexicons and used them to simulate conversations between entities with different category systems. The team's protocol combines various techniques, including active probing and cross-situational elimination, to identify mistranslations and recover from errors. In simulated runs, the protocol successfully intercepted all decoy translations and declared equivalence classes when necessary, outperforming a passive baseline. --- Why it matters: This work matters because it provides a concrete approach to addressing Quine's indeterminacy of translation, which has been a long-standing challenge in artificial intelligence research. By developing a protocol that can effectively communicate with entities with different category systems, the researchers have made progress towards enabling more robust and accurate cross-cultural understanding. Source: https://arxiv.org/abs/2608.19124