用大模型模拟外星语言，设计实验拦截错误翻译。
Intercepting the Kangaroo: Experimental Astrolinguistics with Constructed Lexicons, Active Probing, and Large Language Models as Informants and Hypothesis Proposers
- 构建两种语义体系不同的语言模型，通过协议实现跨系统翻译
- 在400次测试中零漏检错误翻译，比基线提升显著（d=0.62）
- 适合研究跨文化沟通、语言哲学或人工智能可解释性
astrolinguistics——与分类现实方式迥异的智能体通信——自弗赖登塔尔的Lincos（1960）以来始终是纯假设性的。本文使其成为实验性研究。两个具有故意不兼容构词法的语言模型（一个编码形状、颜色与运动；另一个融合颜色与运动，编码奇偶性且无形状）作为拥有完整真值的知情者，由一个完全脚本化的协调器在两类范畴系统间进行翻译。核心失败模式为“袋鼠效应”：单词无声地附着于错误指称对象——奎因翻译不确定性原理的操作化。在400多次模拟与实时运行中，结合跨情境排除、预注册预测探测、主动场景选择、更严格恢复轮次及隔离机制的协议，在测试条件下未检测到任何误译，且覆盖能力优于被动基线（d=0.62）。注入的袋鼠陷阱在100%运行中击败了朴素示现与纯统计学习策略；而完整协议则拦截了所有干扰项，并在本体论证据缺失时声明奎因等价类而非猜测。在知情者噪声下协议表现稳健：每词噪声≤2%时无袋鼠残留；至10%时协议主要选择不回应而非出错。对于脚本外词汇（历史依赖关系词与异或上下文同音词），通过生成-测试循环由大模型提出规则并由脚本验证，覆盖率随提议者能力提升（0% → 18% → 72% → 100%），全程未出现未检测的误译。在测试条件下，正确性属于协议本身；覆盖能力取决于工具性能。
原文摘要 · Abstract (English)
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.
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