# The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in LLMs with Camlang

## TL;DR

This paper introduces Camlang, a constructed language designed to test whether large language models can develop genuine metalinguistic reasoning, revealing significant gaps between model performance and human-like understanding.

## Contribution

The paper presents Camlang, a novel language and evaluation framework that distinguishes between pattern matching and true reasoning in LLMs, highlighting their limitations in mastering grammatical and semantic rules.

## Key findings

- GPT-5 achieves 47% accuracy in Camlang, far below human 87%.
- Models mainly rely on shallow lexical cues rather than systematic grammar.
- Camlang exposes fundamental gaps in current LLMs' metalinguistic reasoning.

## Abstract

Large Language Models (LLMs) achieve gold-medal performance across many benchmarks, yet it remains unclear whether such success reflects genuine reasoning or pattern matching. From a cognitive science perspective, an informative test is whether models can master an unfamiliar language through explicit metalinguistic deductive learning, a paradigm where human learners can reliably internalise grammatical systems through metalinguistic reasoning. We address this question with Camlang, a novel constructed language that exhibits naturalistic yet unattested feature combinations. Camlang consists of two explicit resources, a grammar book and a bilingual dictionary, which mirror adult second-language learning via explicit grammar rules and lexical lookup, and enable us to disentangle errors in morpho-syntax, lexical semantics, and sentence-level reasoning. Human experiments show that these resources are sufficient for participants to acquire Camlang and successfully solve Camlang tasks. To operationalise evaluation, we adapt CommonsenseQA into Camlang, creating Camlang-CSQA-v0, the first task in a broader suite where solving questions requires applying grammar rules and lexical mappings. Experimental results show that GPT-5 achieves 98\% EM accuracy in English but only 47\% in Camlang, far below human performance at 87\%, while other state-of-the-art reasoning LLMs perform even worse. Human verification further reveals that most model successes stem from shallow lexical alignment while GPT-5 shows emerging metalinguistic awareness to a limited extent but not systematic grammatical mastery as humans. Camlang establishes a cognitively grounded evaluation paradigm that exposes fundamental gaps between current models and human metalinguistic competence.