The Gold Medals in an Empty Room: Diagnosing Metalinguistic Reasoning in
  LLMs with Camlang
Authors: name: Fenghua Liu, name: Yulong Chen, name: Yixuan Liu, name: Zhujun Jin, name: Solomon Tsai, name: Ming Zhong

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.