3 articles tagged with #majority-voting. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.
Researchers propose 'best-of-∞' approach for large language models that uses majority voting with infinite samples, achieving superior performance but requiring infinite computation. They develop an adaptive generation scheme that dynamically selects the optimal number of samples based on answer agreement and extend the framework to weighted ensembles of multiple LLMs.
Researchers introduce Propagational Proxy Voting (PPV), an unsupervised aggregation method for multi-sample LLM inference that outperforms standard majority voting on MMLU-Pro benchmarks by leveraging semantic entropy and reasoning geometry signals. The method achieves +1.5 percentage point overall improvement and +2.24 pp on difficult questions without requiring labeled data or auxiliary training.
Researchers identify a critical failure mode in test-time reinforcement learning (TTRL) where majority voting locks onto incorrect answers, permanently suppressing correct signals in low-ability problems. They introduce TTRL-Guard, a framework using flip-rate monitoring and selective updating to prevent this 'Correct-Answer Extinction Window,' achieving 54% relative improvement on AIME 2025 benchmarks.
