An Exploratory Framework for Future SETI Applications: Detecting Generative Reactivity via Language Models

TL;DR

This paper explores whether language models can produce structured responses to noise-like inputs, suggesting a new approach for SETI that focuses on model reactivity rather than decoding signals.

Contribution

It introduces a framework to test generative reactivity in language models with noise-like inputs, shifting SETI focus from decoding to detecting latent structure in data.

Findings

Whale and bird vocalizations elicited higher reactivity scores than white noise.

Human speech triggered moderate responses in language models.

Language models may detect latent structure in non-semantic data.

Abstract

We present an exploratory framework to test whether noise-like input can induce structured responses in language models. Instead of assuming that extraterrestrial signals must be decoded, we evaluate whether inputs can trigger linguistic behavior in generative systems. This shifts the focus from decoding to viewing structured output as a sign of underlying regularity in the input. We tested GPT-2 small, a 117M-parameter model trained on English text, using four types of acoustic input: human speech, humpback whale vocalizations, Phylloscopus trochilus birdsong, and algorithmically generated white noise. All inputs were treated as noise-like, without any assumed symbolic encoding. To assess reactivity, we defined a composite score called Semantic Induction Potential (SIP), combining entropy, syntax coherence, compression gain, and repetition penalty. Results showed that whale and bird vocalizations had higher SIP scores than white noise, while human speech triggered only moderate responses. This suggests that language models may detect latent structure even in data without conventional semantics. We propose that this approach could complement traditional SETI methods, especially in cases where communicative intent is unknown. Generative reactivity may offer a different way to identify data worth closer attention.