arXiv:2605.24693v1 Announce Type: new Abstract: Large language models still struggle with contest-level programming, while many agentic remedies rely on massive inference-time sampling or expensive multi-stage post-training. We study when execution feedback reliably helps an LLM CP solver and which mechanisms govern the gains. We model feedback-driven solving as a calibrated stopped process and identify three quantities: false-admission risk, program-level evidence against bad programs, and the active-state success hazard. Under held-out trace calibration and selection from a pre-declared fini
This research is emerging now as large language models demonstrate increasing capabilities, yet still fall short in complex, feedback-driven tasks like competitive programming, prompting a focus on agentic remedies.
This work is critical as it advances the understanding of how to reliably improve LLM performance in algorithmic problem-solving, which is a key barrier to more generalized AI agent development.
The ability to calibrate and control risk in feedback-driven AI agents for complex tasks changes how reliably LLMs can tackle open-ended or adversarial environments, reducing the need for exhaustive sampling or expensive post-training.
- · AI agent developers
- · Software engineering automation
- · Competitive programming platforms
- · AI research institutions
- · Manual software testers
- · Companies relying on brute-force LLM inference
- · Programming contest organizers with static problem sets
More robust and efficient AI agents will be developed for solving complex analytical and coding problems.
This improved capability could accelerate the automation of certain software development and debugging tasks, increasing developer productivity.
Further advancements might lead to fully autonomous AI systems capable of creating novel algorithms and software, fundamentally reshaping programming as a discipline.
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