---
title: "Who cites Beyond Majority Voting: Towards — 4 citing papers"
description: "4 papers of the Elamur Research library cite «Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time…"
canonical: "https://research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Beyond Majority Voting: Towards Fine-grained and…](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/) / cited by

# Who cites «Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning»

4 papers of our library cite it (of the 149,439 whose bibliographies we read) · 2 with a page here, the most cited first · [← back to «Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning»](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/)

1. **[SAT: Balancing Reasoning Accuracy and Efficiency with Stepwise Adaptive Thinking](https://md.research.elamur.ai/free/en/papers/huang-2026-sat-balancing-reasoning-accuracy-efficiency/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Weiyang Huang, Xuefeng Bai, Kehai Chen, et al.*2026 · 3 citations*
2. **[Beyond Unimodal Shortcuts: MLLMs as Cross-Modal Reasoners for Grounded Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/ma-2026-beyond-unimodal-shortcuts-mllms-as/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jinlong Ma, Yu Zhang, Xuefeng Bai, et al.*2026*

2 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/wang-2026-beyond-majority-voting-fine-grained/) — Beyond Majority Voting: Towards Fine-grained and More Reliable Reward Signal for Test-Time Reinforcement Learning
