SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments

TL;DR

SpatialEvo introduces a self-evolving framework for 3D spatial reasoning that leverages deterministic geometric properties to improve model accuracy without relying on noisy pseudo-labels.

Contribution

It formalizes a deterministic geometric environment (DGE) that enables objective, noise-free training for spatial reasoning models, surpassing prior consensus-based pseudo-label methods.

Findings

Achieves highest average scores on nine spatial reasoning benchmarks.

Demonstrates consistent improvements at 3B and 7B model scales.

Maintains performance on general visual understanding tasks.

Abstract

Spatial reasoning over three-dimensional scenes is a core capability for embodied intelligence, yet continuous model improvement remains bottlenecked by the cost of geometric annotation. The self-evolving paradigm offers a promising path, but its reliance on model consensus to construct pseudo-labels causes training to reinforce rather than correct the model's own geometric errors. We identify a property unique to 3D spatial reasoning that circumvents this limitation: ground truth is a deterministic consequence of the underlying geometry, computable exactly from point clouds and camera poses without any model involvement. Building on this insight, we present SpatialEvo, a self-evolving framework for 3D spatial reasoning, centered on the Deterministic Geometric Environment (DGE). The DGE formalizes 16 spatial reasoning task categories under explicit geometric validation rules and converts unannotated 3D scenes into zero-noise interactive oracles, replacing model consensus with objective physical feedback. A single shared-parameter policy co-evolves across questioner and solver roles under DGE constraints: the questioner generates physically valid spatial questions grounded in scene observations, while the solver derives precise answers against DGE-verified ground truth. A task-adaptive scheduler endogenously concentrates training on the model's weakest categories, producing a dynamic curriculum without manual design. Experiments across nine benchmarks demonstrate that SpatialEvo achieves the highest average score at both 3B and 7B scales, with consistent gains on spatial reasoning benchmarks and no degradation on general visual understanding.