arXiv:2602. 12276v2 Announce Type: replace Abstract: Test-time scaling has become a standard way to improve performance and boost reliability of neural network models.
Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.
arXiv:2604. 02863v2 Announce Type: replace Abstract: Majority voting is the standard for aggregating multi-agent responses into a final decision.
arXiv:2602. 16745v2 Announce Type: replace-cross Abstract: Test-time scaling can improve model performance by aggregating stochastic reasoning trajectories.
arXiv:2601. 19921v2 Announce Type: replace-cross Abstract: Multi-agent debate (MAD) is widely used to improve large language model (LLM) performance through test-time scaling, yet recent work shows that vanilla MAD often underperforms simple majority vote despite higher computational cost.
arXiv:2511. 00802v2 Announce Type: replace-cross Abstract: With data-driven development now widely adopted, online A/B testing is an established method for measuring the effects of new technologies.
arXiv:2606. 12935v1 Announce Type: new Abstract: Parallel test-time scaling samples many reasoning traces and majority-votes their answers, improving LLM accuracy but requiring traces to run to completion, incurring substantial computational overhead.
arXiv:2607. 20468v1 Announce Type: new Abstract: AI agents are increasingly used to automate research and development tasks, yet existing benchmarks typically evaluate them on prescribed workflows or narrow action spaces.