arXiv:2606.27406v1 Announce Type: cross Abstract: Software engineering, whether performed by humans or by AI agents, requires reasoning about how software behaves. We call the internal model that supports such reasoning the software world model, and view current code-execution benchmarks as covering one well-studied slice of it -- control flow. In this paper, we take a step toward a broader evaluation by shifting the observable axis to execution resources: alongside test outcome and exception class, we predict peak memory, wall-clock time, and ranked profiler outputs at method and line granula
The rapid advancement and widespread deployment of large language models for coding necessitates more sophisticated evaluation methods to understand their true capabilities and limitations beyond simple functional correctness.
Improving the 'world models' of coding LLMs is crucial for developing robust, reliable AI agents capable of complex software engineering tasks, directly impacting productivity and innovation.
The focus of LLM evaluation is shifting from mere code correctness to deeper understanding of how software interacts with computational resources, offering a more nuanced and performance-oriented measure of AI capabilities.
- · AI development platforms
- · Software engineering companies adopting AI
- · Cloud providers
- · Software engineers relying solely on basic coding skills
- · Companies with inefficient software development processes
More capable coding LLMs will automate a broader range of software development tasks, from optimization to debugging.
This improved understanding of software behavior could lead to AI systems that design entire software architectures with resource efficiency as a primary constraint.
The ability of AI to reason about system resources could enable self-optimizing and self-healing software systems, drastically reducing operational overhead.
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Read at arXiv cs.AI