arXiv:2603.19954v2 Announce Type: replace Abstract: Transformers have shown inconsistent success in AI planning tasks, and theoretical understanding of when generalization should be expected has been limited. We take important steps towards addressing this gap by analyzing the ability of decoder-only models to verify whether a given plan correctly solves a given planning instance. To analyse the general setting where the number of objects -- and thus the effective input alphabet -- grows at test time, we introduce C*-RASP, an extension of C-RASP designed to establish length generalization guar
The rapid advancement and widespread deployment of transformer models across various AI tasks necessitate a deeper theoretical understanding of their capabilities and limitations in complex reasoning tasks like AI planning.
Understanding the theoretical underpinnings of transformers' ability to verify plans is crucial for developing more reliable and generalizable AI systems, especially for safety-critical applications.
This research provides a foundational step towards improving the robustness and predictability of AI models in planning and verification, shifting from empirical observation to theoretical guarantees.
- · AI researchers
- · Developers of autonomous systems
- · High-assurance AI industries
- · Ad-hoc AI planning solutions
- · Applications reliant on unreliable AI verification
Improved theoretical understanding accelerates the development of more trustworthy AI systems capable of complex reasoning.
Enhanced reliability in plan verification could lead to broader adoption of AI in fields requiring stringent safety and correctness, such as logistics and robotics.
The development of 'provably correct' AI planning agents could revolutionize autonomous systems, enabling them to operate in highly dynamic and uncertain environments with greater confidence.
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Read at arXiv cs.AI