A Simulator-Grounded Framework For Constructing Verifiable Muscle-Grounded QA From 3D Tongue Meshes

Abstract

Existing articulatory corpora based on real-time MRI and electromagnetic articulography capture tongue shape and motion but do not provide traceable labels for the muscle-driven process that generated an observed configuration. We introduce a simulator-grounded data-construction framework and instantiate it as 3DTongueQA. Controlled 11-dimensional muscle activations are mapped to fixed-topology tongue meshes with the ArtiSynth Badin finite-element model, converted into structured biomechanical records, and rendered as deterministic QA on muscle state, geometry, and target-directed change. We screen 295,157 configurations, retain 295,115 valid meshes, and construct 891,156 QA records per language. Language naturalization changes only surface form and is verified against the source records; English and Korean instantiations demonstrate construction-level portability. A swappable SpiralNet++--Qwen3-8B baseline reaches 62.9 $\pm$ 9.2 Muscle EM, 74.0 $\pm$ 0.2 Value Accuracy, and 65.9 $\pm$ 4.7 Direction EM, while mismatching the paired mesh reduces Muscle EM to 2.2; a dataset-leakage-controlled anchor-held-out model retains 80.4--98.6\% of the full-inventory scores on unseen anchors. Task-specific structured readouts further reach 88.7 $\pm$ 0.7 Muscle EM and 93.3 $\pm$ 1.0 Direction EM. These complementary results show that the constructed supervision supports both efficient structured prediction and heterogeneous natural-language QA rather than being tied to a particular decoder architecture.

15 pages, 4 figures, 15 tables