---
title: "Who cites Wei Song — 78 papers of our library"
description: "78 papers of the Elamur Research library cite the works of Wei Song, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/wei-song/cited-by/"
---
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# Who cites Wei Song

79 citations of their works from 78 papers of our library (of the 149,439 whose bibliographies we read) · 27 with a page here, the most cited first · [← Wei Song](https://md.research.elamur.ai/free/en/authors/wei-song/)

1. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/)
2. **[VoiceBench: Benchmarking LLM-Based Voice Assistants](https://md.research.elamur.ai/free/en/papers/chen-2026-voicebench-benchmarking-llm-based-voice/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Yiming Chen, Xianghu Yue, Chen Zhang, et al.*2026 · 74 citations*
3. [**Step-Audio 2 Technical Report arXiv:2507.16632**Boyong Wu, Chao Yan, Chen Hu, et al.*2025 · 31 citations*](https://md.research.elamur.ai/free/en/papers/wu-2025-step-audio-2-technical/)
4. [**Baichuan-Omni-1.5 Technical Report arXiv:2501.15368**Yadong Li, Jun Liu, Tao Zhang, et al.*2025 · 28 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-baichuan-omni-1-5/)
5. [**MiMo-Audio: Audio Language Models are Few-Shot Learners arXiv:2512.23808**Xiaomi LLM-Core Team, Dong Zhang, Gang Wang, et al.*2025 · 24 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-llm-mimo-audio-audio-language/)
6. [**Baichuan-Audio: A Unified Framework for End-to-End Speech Interaction arXiv:2502.17239**Tianpeng Li, Jun Liu, Tao Zhang, et al.*2025 · 16 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-baichuan-audio-unified-framework/)
7. **[A Survey of Inductive Reasoning for Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2026-survey-inductive-reasoning-large-language/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kedi Chen, Dezhao Ruan, Yuhao Dan, et al.*2026 · 10 citations*
8. **[Audio-centric Video Understanding Benchmark without Text Shortcut](https://md.research.elamur.ai/free/en/papers/yang-2025-audio-centric-video-understanding-benchmark/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yudong Yang, Jimin Zhuang, Guangzhi Sun, et al.*2025 · 10 citations*
9. [**Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models arXiv:2501.01034**Bin Wang, Xunlong Zou, Shuo Sun, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-advancing-singlish-understanding-bridging-gap/)
10. **[VideoPro: Adaptive Program Reasoning for Long Video Understanding](https://md.research.elamur.ai/free/en/papers/li-2026-videopro-adaptive-program-reasoning-long/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chenglin Li, Feng Han, Yikun Wang, et al.*2026 · 6 citations*
11. **[Interleaved Latent Visual Reasoning with Selective Perceptual Modeling](https://md.research.elamur.ai/free/en/papers/dong-2026-interleaved-latent-visual-reasoning-selective/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shuai Dong, Siyuan Wang, Xingyu Liu, et al.*2026 · 5 citations*
12. **[Dual-Axis Generative Reward Model Toward Semantic and Turn-taking Robustness in Interactive Spoken Dialogue Models](https://md.research.elamur.ai/free/en/papers/chen-2026-dual-axis-generative-reward-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yifu Chen, Shengpeng Ji, Zhengqing Liu, et al.*2026 · 4 citations*
13. **[Chronological Thinking in Full-Duplex Spoken Dialogue Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-chronological-thinking-full-duplex-spoken/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Donghang Wu, Haoyang Zhang, Chen Chen, et al.*2026 · 3 citations*
14. **[Closing the Modality Reasoning Gap for Speech Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-closing-modality-reasoning-gap-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chaoren Wang, Heng Lu, Xueyao Zhang, et al.*2026 · 2 citations*
15. **[Do We Need Distinct Representations for Every Speech Token? Unveiling and Exploiting Redundancy in Large Speech Language Models](https://md.research.elamur.ai/free/en/papers/xiang-2026-do-we-need-distinct-representations/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Bajian Xiang, Tingwei Guo, Xuan Chen, et al.*2026 · 2 citations*
16. **[MedMCP-Calc: Benchmarking LLMs for Realistic Medical Calculator Scenarios via MCP Integration](https://md.research.elamur.ai/free/en/papers/zhu-2026-medmcp-calc-benchmarking-llms-realistic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yakun Zhu, Yutong Huang, Shengqian Qin, et al.*2026 · 2 citations*
17. **[Discovering and Causally Validating Emotion-Sensitive Neurons in Large Audio-Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2026-discovering-causally-validating-emotion-sensitive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiutian Zhao, Björn Schuller, Berrak Sisman*2026 · 1 citation*
18. **[From Static Inference to Dynamic Interaction: A Survey of Streaming Large Language Models](https://md.research.elamur.ai/free/en/papers/tong-2026-static-inference-dynamic-interaction-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Junlong Tong, Zilong Wang, YuJie Ren, et al.*2026 · 1 citation*
19. **[UltraEval-Audio: A Unified Framework for Comprehensive Evaluation of Audio Foundation Models](https://md.research.elamur.ai/free/en/papers/shi-2026-ultraeval-audio-unified-framework-comprehensive/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qundong Shi, Jie Zhou, Biyuan Lin, et al.*2026 · 1 citation*
20. **[Afrispeech Semantics: Evaluating Audio–Semantic Reasoning in Spoken Language Models Across Domains and Accents](https://md.research.elamur.ai/free/en/papers/okocha-2026-afrispeech-semantics-evaluating-audio-semantic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Chibuzor Okocha, Christan Grant*2026*
21. **[Don't Just Listen, Try Planning: Graph-based Retrieval-Generation Agent for Long-form Audio Meeting Understanding](https://md.research.elamur.ai/free/en/papers/tang-2026-don-t-just-listen-try/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Quanwei Tang, Dong Zhang, Shoushan Li, et al.*2026*
22. **[Evaluating the Expressive Appropriateness of Speech in Rich Contexts](https://md.research.elamur.ai/free/en/papers/wang-2026-evaluating-expressive-appropriateness-speech-rich/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tianrui Wang, Ziyang Ma, Yizhou Peng, et al.*2026*
23. **[HearSay Benchmark: Do Audio LLMs Leak What They Hear?](https://md.research.elamur.ai/free/en/papers/wang-2026-hearsay-benchmark-do-audio-llms/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jin Wang, Kaiwen Luo, Liang Lin, et al.*2026*
24. **[RealTalk-CN: A Realistic Chinese Speech Task-Oriented Dialogue Benchmark with Cross-Modal Analysis](https://md.research.elamur.ai/free/en/papers/wang-2026-realtalk-cn-realistic-chinese-speech/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Enzhi Wang, Jiaming Zhou, Yuhang Jia, et al.*2026*
25. **[SDiaReward: Modeling and Benchmarking Spoken Dialogue Rewards with Modality and Colloquialness](https://md.research.elamur.ai/free/en/papers/lu-2026-sdiareward-modeling-benchmarking-spoken-dialogue/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jingyu Lu, Yuhan Wang, Fan Zhuo, et al.*2026*
26. **[Think Smart, Not Hard: Difficulty Adaptive Reasoning for Large Audio Language Models](https://md.research.elamur.ai/free/en/papers/sheng-2026-think-smart-not-hard-difficulty/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhichao Sheng, Shilin Zhou, Chen Gong, et al.*2026*
27. **[UniSRM: A Unified Speech Reward Model for Reasoning-Based Fine-grained Assessment](https://md.research.elamur.ai/free/en/papers/wang-2026-unisrm-unified-speech-reward-model/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yuanyuan Wang, Dongchao Yang, Yayue Deng, et al.*2026*

51 more citing papers of our library have no page here yet.

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