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This paper is a diagnostic study of how fine-tuned vision-language models use scene-graph memory in embodied question answering, and it introduces a "graph-swap" test that measures reliance on the graph by counting answer changes when each episode receives another episode's graph. The main finding is that text-delivered memory is used heavily, while graph-encoder delivery is largely discarded by standard pooling and late fusion, with per-node tokens recovering part of the gap. For AI readers building embodied agents, the practical takeaway is that accuracy alone can hide whether a model actually relies on its memory, and the swap test is a cheap way to check.
Do Embodied VLMs Actually Use Their Scene-Graph Memory?
arXiv:2611.02056v1 Announce Type: cross Abstract: Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a natural form for it. A vision-language model (VLM) can receive such a graph as text or through a graph encoder, but accuracy alone cannot show whether the graph holds the episode's facts, reaches the model, or is actually relied on. In this diagnostic study, we introduce a graph-swap test for the last of these: each episode is given another episode's graph, and we count how many answers change. On an embodied question-answering benchmark with routes through two to seven rooms, we use it to compare ways of delivering scene-graph memory to a fine-tuned VLM. As text, the memory is used strongly, adding 30 points on route questions. As a graph, it is not, and we trace why: the encoder preserves the information, but standard pooled and late fusion discard it. Giving each node its own input token recovers part of it, a gain of 8.3 points on route questions that the swap test confirms comes from the graph. The test also exposes a shortcut that accuracy hides: when the same facts appear in the text, the model ignores its graph. The test thus offers a cheap check of whether a VLM uses its scene-graph memory, and our analysis points to how that memory should be delivered.
Title:Do Embodied VLMs Actually Use Their Scene-Graph Memory?
Abstract:Embodied agents that answer questions about their route need a memory of what they saw, and scene graphs are a natural form for it. A vision-language model (VLM) can receive such a graph as text or through a graph encoder, but accuracy alone cannot show whether the graph holds the episode's facts, reaches the model, or is actually relied on. In this diagnostic study, we introduce a graph-swap test for the last of these: each episode is given another episode's graph, and we count how many answers change. On an embodied question-answering benchmark with routes through two to seven rooms, we use it to compare ways of delivering scene-graph memory to a fine-tuned VLM. As text, the memory is used strongly, adding 30 points on route questions. As a graph, it is not, and we trace why: the encoder preserves the information, but standard pooled and late fusion discard it. Giving each node its own input token recovers part of it, a gain of 8.3 points on route questions that the swap test confirms comes from the graph. The test also exposes a shortcut that accuracy hides: when the same facts appear in the text, the model ignores its graph. The test thus offers a cheap check of whether a VLM uses its scene-graph memory, and our analysis points to how that memory should be delivered.
Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2611.02056 [cs.RO]
(or arXiv:2611.02056v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2611.02056 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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