Architecture-Matched Monitoring and the Representation Gap: Why VLA Failure Signatures, Perception Encoding, and Safety Certification Must Be Co-Designed

Vision-Language-Action (VLA) models are increasingly deployed as the cognitive core of physical robots, yet their failure modes, perceptual representations, and runtime safety mechanisms are almost universally designed and evaluated in isolation. This paper synthesizes five to seven specific findings from recent robotics, human-computer interaction, and systems-control preprints to argue a single defensible thesis: **the gap between a VLA's internal representational structure and the monitoring or certification apparatus placed around it is itself a primary source of deployment fragility — not a secondary concern to be patched post hoc.** We draw on empirical evidence that VLA architectures produce architecture-specific failure signatures at the motor-command level [corpus:arxiv:2605.28726], that visual encoders trained without dynamics awareness leave motion understanding to downstream policies that then fail under distribution shift [corpus:arxiv:2605.30350], that fine-grained language supervision reshapes policy behavior in ways coarse goal-level data cannot [corpus:arxiv:2605.27284], that belief-space safety filters require explicit conformal-prediction certification to handle inference error [corpus:arxiv:2606.02562], and that world-task factorization provides a principled structural decomposition that could align these concerns [corpus:arxiv:2606.02027]. Secondary evidence from swarm fault-tolerance [corpus:arxiv:2606.01970] and in-flight reinforcement learning [corpus:arxiv:2606.01478] grounds the argument in operational constraints that single-robot VLA narratives routinely ignore. The falsification path is concrete: if architecture-matched monitors do not outperform generic monitors on held-out hardware trials, or if dynamics-aware encoders do not reduce out-of-distribution failure rates beyond what coarse encoders achieve with domain randomization, the co-design thesis collapses to a restatement of the obvious. We close by identifying the structural conditions under which co-design is tractable and where it currently is not. ---

Authorship: Saluca Agentic AI Research Team (Saluca LLC). AI-drafted from arXiv preprint corpus on the date in the filename.

Cited arXiv preprints: 2605.27284, 2605.28726, 2605.29677, 2605.30326, 2605.30350, 2606.01478, 2606.01597, 2606.01970, 2606.02027, 2606.02562