Tipping point for emergent spatial capabilities under continued data scaling

Determine whether continued scaling of spatial-intelligence training data for multimodal foundation models—specifically the SenseNova-SI variants built upon InternVL3, Qwen3-VL, and Bagel—will reach a tipping point that triggers stronger emergent spatial intelligence capabilities beyond the diminishing returns observed at current scales.

Background

The paper investigates scaling laws for spatial intelligence by training SenseNova-SI models on an 8.5M QA corpus aligned to five spatial capability domains. While scaling consistently improves performance, the authors observe saturation effects where gains diminish as data volume increases.

Despite reporting early signs of emergent generalization (spill-over across tasks and some extrapolation beyond training context length), the authors explicitly note uncertainty about whether further scaling alone will eventually produce a qualitative shift—i.e., a tipping point—leading to stronger emergent capabilities. This uncertainty motivates open-sourcing model weights to enable research into algorithmic advances beyond data scaling.