When the Next Step Is Not One Step: Distribution-Aware Execution Modeling for Concurrent Go Programs
arXiv:2606.17508v1 Announce Type: new Abstract: Training a model to predict the next step in a concurrent program is harder than it looks: two runs of the same program from the same trace prefix can produce different next events, both valid, because the scheduler is nondeterministic. A model trained against a single label is learning to guess one outcome of a random process. We turn this around and use the nondeterminism as a training signal. We run each program many times, aggregate the observed next events into an empirical distribution, and fine-tune a 7B model to match that distribution wi
The increasing complexity of concurrent software and the rapid advancement of AI models capable of handling nuanced data are converging to enable new approaches to program analysis.
This development could significantly improve the reliability, predictability, and efficiency of complex software systems, particularly those relying on concurrent execution such as AI infrastructure and distributed computing.
Traditional deterministic program analysis is augmented by probabilistic modeling that accounts for scheduler nondeterminism, leading to more robust and accurate predictions of program behavior.
- · Software developers
- · Cloud computing providers
- · AI infrastructure companies
- · High-performance computing sector
- · Companies with high software error rates
- · Traditional debugging tool vendors
More stable and reliable concurrent software, reducing debugging time and production outages.
Accelerated development of complex, highly concurrent applications, including advanced AI agents and distributed systems.
A shift towards more robust AI-assisted software engineering practices, potentially automating significant portions of validation and verification.
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Read at arXiv cs.LG