arXiv:2510.03415v3 Announce Type: replace-cross Abstract: Recent work asks whether large language models (LLMs) condition their reasoning on explicit rules rather than statistical regularities from pretraining. Program execution provides a canonical instance: formal semantics define behavior through symbolic transition rules that can be systematically altered under distribution shift. We investigate whether LLMs can condition their reasoning on formal semantics through program execution and introduce PLSemanticsBench, pairing featherweight C programs with two semantic systems -- small-step ope
This research is emerging as the capabilities and limitations of LLMs in complex reasoning tasks, particularly programming, are under intense scrutiny.
A strategic reader should care because this research suggests fundamental limitations in how LLMs currently process and apply formal semantics, impacting their reliability in critical applications like code generation and verification.
This research changes the understanding of core LLM reasoning mechanisms, suggesting that current models primarily rely on statistical priors rather than deep comprehension of underlying semantic rules when processing code.
- · Symbolic AI researchers
- · Formal verification tooling
- · Domain-specific language developers
- · LLM-only code generation platforms
- · Developers relying solely on LLMs for semantic correctness
- · AI paradigms overemphasizing statistical correlation
It confirms that current LLMs struggle with reliably interpreting and applying formal rules independently of their training data's statistical regularities.
This limitation will necessitate hybrid AI approaches combining LLMs with symbolic systems or specialized formal reasoning components for robust software engineering applications.
The long-term implication could be a re-evaluation of LLM architectures and training methodologies to better integrate explicit semantic understanding, or a broader acceptance of LLMs as powerful but semantically shallow tools.
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Read at arXiv cs.AI