# A Qualitative Model for Reasoning about Path and Support

arXiv: https://arxiv.org/abs/2609.20349 · HTML: https://mlsanity.com/paper/2609.20349/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 3 of its 37 references that are in the map.
- First posted on arXiv 17 Sep 2026.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 3 of 3)

- [Learning to Play Video Games with Intuitive Physics Priors](https://mlsanity.com/paper/2409.13886/) (2024)
- [Bilateral Spatial Reasoning about Street Networks: Graph-based RAG with Qualitative Spatial Representations](https://mlsanity.com/paper/2512.15388/) (2025)
- [A Qualitative Model to Reason about Object Rotations (QOR) applied to solve the Cube Comparison Test (CCT)](https://mlsanity.com/paper/2601.08382/) (2026)

## Abstract (arXiv)

Spatial reasoning abilities correlate strongly with performance in STEM fields. Games offer a compelling medium for training these critical skills in developing children who have a natural proclivity for play. However, to facilitate human-like tutoring and player guidance, these games require an AI agent capable of making commonsense inferences from spatial events. Qualitative reasoning (QR) models appear to be a suitable framework for these application domains. As these models reason in symbolic representations, they can seamlessly translate game states into interpretable feedback for human-like player guidance. This paper introduces a hybrid qualitative model designed for Camelot Jr., a block-puzzle game that requires constructing multi-level bridges to connect two avatars stationed on separate towers. The game poses a challenge for the player, who must make platforms stable, plan their path, and ensure they use all the provided blocks. To handle the precise physics required by the domain, we integrate a mathematical center-of-mass stability logic to guide our qualitative solver. Our work facilitates spatial skill training in Camelot Jr. and contributes to the development of human-centric, explainable game-playing agents.