--- title: "Who cites Vibe-Eval: A hard evaluation suite — 19 citing papers" description: "19 papers of the Elamur Research library cite «Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models»…" canonical: "https://research.elamur.ai/free/en/papers/padlewski-2024-vibe-eval-hard-evaluation-suite/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/padlewski-2024-vibe-eval-hard-evaluation-suite/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Vibe-Eval: A hard evaluation suite for measuring…](https://md.research.elamur.ai/free/en/papers/padlewski-2024-vibe-eval-hard-evaluation-suite/) / cited by # Who cites «Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models» 19 papers of our library cite it (of the 149,439 whose bibliographies we read) · 12 with a page here, the most cited first · [← back to «Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models»](https://md.research.elamur.ai/free/en/papers/padlewski-2024-vibe-eval-hard-evaluation-suite/) 1. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/) 2. [**LLaVA-OneVision: Easy Visual Task Transfer arXiv:2408.03326**Bo Li, Yuanhan Zhang, Dong Guo, et al.*2024 · 477 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-llava-onevision-easy-visual-task/) 3. **[LMMs-Eval: Reality Check on the Evaluation of Large Multimodal Models](https://md.research.elamur.ai/free/en/papers/zhang-2025-lmms-eval-reality-check-evaluation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Kaichen Zhang, Bo Li, Peiyuan Zhang, et al.*2025 · 115 citations* 4. **[AudioBench: A Universal Benchmark for Audio Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-audiobench-universal-benchmark-audio-large/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Bin Wang, Xunlong Zou, Geyu Lin, et al.*2025 · 77 citations* 5. **[MixEval: Deriving Wisdom of the Crowd from LLM Benchmark Mixtures](https://md.research.elamur.ai/free/en/papers/ni-2024-mixeval-deriving-wisdom-crowd-llm/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jinjie Ni, Fuzhao Xue, Xiang Yue, et al.*2024 · 47 citations* 6. **[FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions](https://md.research.elamur.ai/free/en/papers/weller-2025-followir-evaluating-teaching-information-retrieval/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Orion Weller, Benjamin Chang, Sean MacAvaney, et al.*2025 · 40 citations* 7. [**MiMo-VL Technical Report arXiv:2506.03569**Xiaomi LLM-Core Team, Zihao Yue, Zhenru Lin, et al.*2025 · 33 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-llm-mimo-vl-technical-report/) 8. **[Multimodal Needle in a Haystack: Benchmarking Long-Context Capability of Multimodal Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2025-multimodal-needle-haystack-benchmarking-long/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hengyi Wang, Haizhou Shi, Shiwei Tan, et al.*2025 · 21 citations* 9. **[Seeing is Believing, but How Much? A Comprehensive Analysis of Verbalized Calibration in Vision-Language Models](https://md.research.elamur.ai/free/en/papers/xuan-2025-seeing-is-believing-but-how/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Weihao Xuan, Qingcheng Zeng, Heli Qi, et al.*2025 · 11 citations* 10. **[Graders Should Cheat: Privileged Information Enables Expert-Level Automated Evaluations](https://md.research.elamur.ai/free/en/papers/zhou-2025-graders-should-cheat-privileged-information/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jin Peng Zhou, Séb Arnold, Nan Ding, et al.*2025 · 3 citations* 11. **[VisTW: Benchmarking Vision-Language Models for Taiwanese Mandarin in Taiwan](https://md.research.elamur.ai/free/en/papers/tam-2026-vistw-benchmarking-vision-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zhi Rui Tam, Yung-Yu Shih, Yen-Wei Lee, et al.*2026 · 1 citation* 12. **[On the Cultural Anachronism and Temporal Reasoning in Vision Language Models](https://md.research.elamur.ai/free/en/papers/ranjan-2026-cultural-anachronism-temporal-reasoning-vision/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mukul Ranjan, Prince Jha, Khushboo Kumari, et al.*2026* 7 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/padlewski-2024-vibe-eval-hard-evaluation-suite/) — Vibe-Eval: A hard evaluation suite for measuring progress of multimodal language models