Spatial Atlas: Compute-Grounded Reasoning for Spatial-Aware Research Agent Benchmarks

Spatial Atlas addresses a core failure mode of current LLMs: the tendency to hallucinate spatial relationships and logical constraints that should be deterministic. By introducing Compute-Grounded Reasoning (CGR), it formalizes a workflow where the LLM acts as an orchestrator that calls deterministic scripts or solvers to 'ground' its reasoning. The project is extremely early (0 stars, 2 days old) and functions primarily as a research artifact associated with the FieldWorkArena and MLE-Bench benchmarks. While the industrial focus (warehouses, factories) provides a niche, frontier labs are aggressively pursuing 'System 2' reasoning (e.g., OpenAI's o1 or Google's search-grounded models) which effectively internalizes this 'compute-before-generate' logic. The project's defensibility is currently low as it lacks a community or proprietary data moat; its value lies in the benchmark data and the specific A2A implementation. It risks being absorbed by general-purpose agent frameworks like LangGraph or directly by model-layer capabilities that integrate spatial tool-use natively.