Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers

Abstract:Astrolinguistics -- communication with minds that categorize reality differently from ours -- has been purely speculative since Freudenthal's Lincos (1960). We make it experimental. Two language models with deliberately incompatible constructed lexicons (one encoding shape, color, and motion; the other fusing color with motion, encoding parity, and lacking shape) serve as informants with complete ground truth, while a fully scripted orchestrator translates between the two category systems. The central failure mode is the kangaroo effect: the silent attachment of a word to the wrong referent -- Quine's indeterminacy of translation, operationalized. Across 400+ simulated and live runs, a protocol combining cross-situational elimination, pre-registered predictive probes, active scene selection, a stricter recovery round, and quarantine produced no undetected mistranslations under the tested conditions and exceeded a passive baseline's coverage (d = 0.62). Injected kangaroo traps defeated naive ostension and pure statistical learning in 100% of runs, while the full protocol intercepted every decoy and, where discriminating evidence is ontologically unavailable, declared Quinean equivalence classes instead of guessing. Under informant noise it degrades gracefully: zero kangaroos persist up to 2% per-word noise; at 10% the protocol predominantly abstains rather than errs. Finally, words outside the scripted hypothesis space (a history-dependent relational term and an XOR contextual homonym) are recovered by a generate-and-test loop in which an LLM proposes rules and the script verifies them: coverage scales with proposer capability (0% -> 18% -> 72% -> 100%) while undetected mistranslations stayed at zero throughout. In the tested conditions, correctness is a property of the protocol; coverage is a property of the instruments.

Computation and Language (cs.CL); Artificial Intelligence (cs.AI)

arXiv:2608.19124 [cs.CL]

(or arXiv:2608.19124v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2608.19124 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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