Paper
Not All Features Are Created Equal: A Mechanistic Study of Vision-Language-Action Models
Authors
Bryce Grant, Xijia Zhao, Peng Wang
Abstract
Vision-Language-Action (VLA) models combine perception, language, and motor control in a single architecture, yet how they translate multimodal inputs into actions remains poorly understood. We apply activation injection, sparse autoencoders (SAEs), and linear probes to six models spanning 80M--7B parameters across 394,000+ rollout episodes on four benchmarks. The visual pathway dominates action generation across all architectures: injecting baseline activations into null-prompt episodes recovers near-identical behavior, while cross-task injection steers robots toward source-task positions (99.8\% of X-VLA episodes align with the source trajectory), exposing spatially bound motor programs tied to scene coordinates rather than abstract task representations. Language sensitivity depends on task structure, not model design: when visual context uniquely specifies the task, language is ignored; when multiple goals share a scene, language becomes essential (X-VLA \texttt{libero\_goal}: 94\%$\to$10\% under wrong prompts vs.\ \texttt{libero\_object}: 60--100\% regardless). In all three multi-pathway architectures (\pizhalf{}, SmolVLA, GR00T), expert pathways encode motor programs while VLM pathways encode goal semantics ($2\times$ greater behavioral displacement from expert injection), and subspace injection confirms these occupy separable activation subspaces. Per-token SAE processing is essential for action fidelity on most architectures, though mean-pooling improves fidelity on X-VLA. Contrastive identification recovers 82+ manipulation concepts, and causal ablation reveals sensitivity spanning 28--92\% zero-effect rates independent of representation width. We release \textbf{Action Atlas} (https://action-atlas.com) for interactive exploration of VLA representations across all six models.
Metadata
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2603.19233v1</id>\n <title>Not All Features Are Created Equal: A Mechanistic Study of Vision-Language-Action Models</title>\n <updated>2026-03-19T17:59:55Z</updated>\n <link href='https://arxiv.org/abs/2603.19233v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2603.19233v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>Vision-Language-Action (VLA) models combine perception, language, and motor control in a single architecture, yet how they translate multimodal inputs into actions remains poorly understood. We apply activation injection, sparse autoencoders (SAEs), and linear probes to six models spanning 80M--7B parameters across 394,000+ rollout episodes on four benchmarks. The visual pathway dominates action generation across all architectures: injecting baseline activations into null-prompt episodes recovers near-identical behavior, while cross-task injection steers robots toward source-task positions (99.8\\% of X-VLA episodes align with the source trajectory), exposing spatially bound motor programs tied to scene coordinates rather than abstract task representations. Language sensitivity depends on task structure, not model design: when visual context uniquely specifies the task, language is ignored; when multiple goals share a scene, language becomes essential (X-VLA \\texttt{libero\\_goal}: 94\\%$\\to$10\\% under wrong prompts vs.\\ \\texttt{libero\\_object}: 60--100\\% regardless). In all three multi-pathway architectures (\\pizhalf{}, SmolVLA, GR00T), expert pathways encode motor programs while VLM pathways encode goal semantics ($2\\times$ greater behavioral displacement from expert injection), and subspace injection confirms these occupy separable activation subspaces. Per-token SAE processing is essential for action fidelity on most architectures, though mean-pooling improves fidelity on X-VLA. Contrastive identification recovers 82+ manipulation concepts, and causal ablation reveals sensitivity spanning 28--92\\% zero-effect rates independent of representation width. We release \\textbf{Action Atlas} (https://action-atlas.com) for interactive exploration of VLA representations across all six models.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='cs.RO'/>\n <published>2026-03-19T17:59:55Z</published>\n <arxiv:comment>Accepted to Multimodal Intelligence Workshop @ ICLR</arxiv:comment>\n <arxiv:primary_category term='cs.RO'/>\n <author>\n <name>Bryce Grant</name>\n </author>\n <author>\n <name>Xijia Zhao</name>\n </author>\n <author>\n <name>Peng Wang</name>\n </author>\n </entry>"
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