arXiv:2512.23292v3 Announce Type: replace-cross Abstract: The prevailing paradigm in AI for physical systems (scaling general-purpose foundation models toward universal multimodal reasoning) confronts a fundamental barrier at the control interface. Recent benchmarks show that even frontier vision--language models achieve only 50--53% accuracy on basic quantitative physics tasks, behaving as approximate guessers that preserve semantic plausibility by violating physical constraints. This input unfaithfulness is not a scaling deficiency but a structural limitation: perception-centric architecture
The paper identifies a fundamental limitation of current general-purpose AI architectures in physical system control, suggesting a pivot toward domain-specific models, which is critical as AI deployment expands into high-stakes environments.
This work highlights the need for a new paradigm in AI for physical systems, moving beyond perception-centric models to systems designed for verifiable physical constraint obedience, which is crucial for reliability and safety.
The focus in AI development for physical systems will shift from scaling general-purpose models to developing specialized, physically grounded AI, potentially accelerating progress in autonomous control for critical infrastructure.
- · Specialized AI/robotics companies
- · Nuclear energy sector
- · Control systems engineers
- · Domain-specific AI developers
- · General-purpose foundation model providers (for physical control)
- · Purely perception-centric AI approaches
- · AI developers lacking physics expertise
Development accelerates for AI agents capable of reliably controlling complex physical systems, like nuclear reactors, through domain-specific foundation models.
Increased investor focus and R&D funding shift toward AI architectures that enforce physical constraints rather than merely preserve semantic plausibility, fostering new industry standards.
Enhanced safety and efficiency in critical infrastructure sectors (e.g., energy, manufacturing, defense) become possible through the deployment of verifiably robust AI control, lowering operational risks and costs across these industries.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.LG