Emergent communication (EmCom) with deep neural network-based agents promises to yield insights into the nature of human language, but remains focused primarily on a few subfieldspecific goals and metrics that prioritize communication schemes which represent attributes with unique characters one-to-one and compose them syntactically.We thus reinterpret a common EmCom setting, the attribute-value reconstruction game, by imposing a smallvocabulary constraint to simulate double articulation, and formulating a novel setting analogous to naturalistic inflectional morphology (enabling meaningful comparison to natural language communication schemes).We develop new metrics and explore variations of this game motivated by real properties of inflectional morphology: concatenativity and fusion.Through our experiments, we discover that simulated phonological constraints encourage concatenative morphology, and emergent languages replicate the tendency of natural languages to fuse grammatical attributes.

Humans acquire syntactic constructions like filler-gap dependencies from limited and often noisy input.Can neural language models do the same?We investigate this question by evaluating GPT-2 models trained on childoriented input from the BabyLM Challenge.Our experiments focus on whether these "baby" language models acquire filler-gap dependencies, generalize across constructions, and respect structural constraints such as island effects.We apply a suite of syntactic constructions to four models trained on child language, including two base models (trained on 10M and 100M tokens) and two well-performing models from the BabyLM Challenge (ConcreteGPT and BabbleGPT).We evaluate model behavior using wh-licensing scores, flip tests, and grammaticality contrasts across four constructions.Results show that BabyLM-scale models partially acquire filler-gap dependencies but often fail to generalize or fully capture island constraints.Our code and datasets are available at https://github.com/um-cap-lab/ EMNLP-2025-submission.