---
title: "Who cites Chang Liu — 246 papers of our library"
description: "246 papers of the Elamur Research library cite the works of Chang Liu, found in their bibliographies; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/chang-liu/cited-by/"
---
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# Who cites Chang Liu

246 citations of their works from 246 papers of our library (of the 149,439 whose bibliographies we read) · 59 with a page here, the most cited first · [← Chang Liu](https://md.research.elamur.ai/free/en/authors/chang-liu/)

1. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
2. **[RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,306 citations*
3. [**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/)
4. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
5. [**Extracting Training Data from Large Language Models arXiv:2012.07805**Nicholas Carlini, Florian Tramer, Eric Wallace, et al.*2020 · 955 citations*](https://md.research.elamur.ai/free/en/papers/carlini-2020-extracting-training-data-large-language/)
6. **[Red Teaming Language Models with Language Models](https://md.research.elamur.ai/free/en/papers/perez-2022-red-teaming-language-models-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ethan Perez, Saffron Huang, Francis Song, et al.*2022 · 700 citations*
7. [**Ethical and social risks of harm from Language Models arXiv:2112.04359**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 456 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
8. [**Large Language Models Struggle to Learn Long-Tail Knowledge arXiv:2211.08411**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 366 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/)
9. [**Discovering Language Model Behaviors with Model-Written Evaluations arXiv:2212.09251**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/)
10. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
11. **[DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 259 citations*
12. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
13. [**Scalable Extraction of Training Data from (Production) Language Models arXiv:2311.17035**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/)
14. **[Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 190 citations*
15. [**Deduplicating Training Data Mitigates Privacy Risks in Language Models arXiv:2202.06539**Nikhil Kandpal, Eric Wallace, Colin Raffel*2022 · 172 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-deduplicating-training-data-mitigates-privacy/)
16. **[Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://md.research.elamur.ai/free/en/papers/razeghi-2022-impact-pretraining-term-frequencies-few/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yasaman Razeghi, Robert L Logan IV, Matt Gardner, et al.*2022 · 165 citations*
17. [**The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset arXiv:2303.03915**Hugo Laurençon, Lucile Saulnier, Thomas Wang, et al.*2023 · 150 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2023-bigscience-roots-corpus-1-6tb/)
18. **[Quantifying Privacy Risks of Masked Language Models Using Membership Inference Attacks](https://md.research.elamur.ai/free/en/papers/mireshghallah-2022-quantifying-privacy-risks-masked-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fatemehsadat Mireshghallah, Kartik Goyal, Archit Uniyal, et al.*2022 · 146 citations*
19. [**RAFT: Adapting Language Model to Domain Specific RAG arXiv:2403.10131**Tianjun Zhang, Shishir G. Patil, Naman Jain, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-raft-adapting-language-model-domain/)
20. **[How Much Do Language Models Copy From Their Training Data? Evaluating Linguistic Novelty in Text Generation Using RAVEN](https://md.research.elamur.ai/free/en/papers/mccoy-2023-how-much-do-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**R. Thomas McCoy, Paul Smolensky, Tal Linzen, et al.*2023 · 117 citations*
21. [**A Survey on Data Selection for Language Models arXiv:2402.16827**Alon Albalak, Yanai Elazar, Sang Michael Xie, et al.*2024 · 95 citations*](https://md.research.elamur.ai/free/en/papers/albalak-2024-survey-data-selection-language-models/)
22. [**Do Membership Inference Attacks Work on Large Language Models? arXiv:2402.07841**Michael Duan, Anshuman Suri, Niloofar Mireshghallah, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/duan-2024-membership-inference-attacks-work-large/)
23. **[Machine Unlearning of Pre-trained Large Language Models](https://md.research.elamur.ai/free/en/papers/yao-2024-machine-unlearning-pre-trained-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jin Yao, Eli Chien, Minxin Du, et al.*2024 · 86 citations*
24. **[MoBA: Mixture of Block Attention for Long-Context LLMs](https://md.research.elamur.ai/free/en/papers/lu-2025-moba-mixture-block-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Enzhe Lu, Zhejun Jiang, Jingyuan Liu, et al.*2025 · 80 citations*
25. **[Apertus: Democratizing Open and Compliant LLMs for Global Language Environments](https://md.research.elamur.ai/free/en/papers/cano-2026-apertus-democratizing-open-compliant-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Alejandro Hernández-Cano, Alexander Hägele, Allen Hao Huang, et al.*2026 · 68 citations*
26. **[Preventing Generation of Verbatim Memorization in Language Models Gives a False Sense of Privacy](https://md.research.elamur.ai/free/en/papers/ippolito-2023-preventing-generation-verbatim-memorization-language/) [International Natural Language Generation Conference (INLG)](https://md.research.elamur.ai/free/en/venues/inlg/)**Daphne Ippolito, Florian Tramer, Milad Nasr, et al.*2023 · 68 citations*
27. [**MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention arXiv:2506.13585**MiniMax, Aili Chen, Aonian Li, et al.*2025 · 66 citations*](https://md.research.elamur.ai/free/en/papers/minimax-2025-minimax-m1-scaling-test/)
28. [**A Comprehensive Survey on Long Context Language Modeling arXiv:2503.17407**Jiaheng Liu, Dawei Zhu, Zhiqi Bai, et al.*2025 · 64 citations*](https://md.research.elamur.ai/free/en/papers/liu-2025-comprehensive-survey-long-context-language/)
29. **[Understanding Transformer Memorization Recall Through Idioms](https://md.research.elamur.ai/free/en/papers/haviv-2023-understanding-transformer-memorization-recall-through/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Adi Haviv, Ido Cohen, Jacob Gidron, et al.*2023 · 62 citations*
30. [**MiMo-V2-Flash Technical Report arXiv:2601.02780**Xiaomi LLM-Core Team; Bangjun Xiao; Bingquan Xia; Bo Yang; Bofei Gao; Bowen Shen; Chen Zhang; Chenhong He; Chiheng Lou; Fuli Luo; Gang Wang; Gang Xie; Hailin Zhang; Hanglong Lv; Hanyu Li; Heyu Chen; Hongshen Xu; Houbin Zhang; Huaqiu Liu; Jiangshan Duo; Jianyu Wei; Jiebao Xiao; Jinhao Dong; Jun Shi; Junhao Hu; Kainan Bao; Kang Zhou; Lei Li; Liang Zhao; Linghao Zhang; Peidian Li; Qianli Chen; Shaohui Liu; Shihua Yu; Shijie Cao; Shimao Chen; Shouqiu Yu; Shuo Liu; Tianling Zhou; Weijiang Su; Weikun Wang; Wenhan Ma; Xiangwei Deng; Bohan Mao; Bowen Ye; Can Cai; Chenghua Wang; Chengxuan Zhu; Chong Ma; Chun Chen; Chunan Li; Dawei Zhu; Deshan Xiao; Dong Zhang; Duo Zhang; Fangyue Liu; Feiyu Yang; Fengyuan Shi; Guoan Wang; Hao Tian; Hao Wu; Heng Qu; Hongfei Yi; Hongxu An; Hongyi Guan; Xing Zhang; Yifan Song; Yihan Yan; Yihao Zhao; Yingchun Lai; Yizhao Gao; Yu Cheng; Yuanyuan Tian; Yudong Wang; Zhen Tang; Zhengju Tang; Zhengtao Wen; Zhichao Song; Zhixian Zheng; Zihan Jiang; Jian Wen; Jiarui Sun; Jiawei Li; Jinlong Xue; Jun Xia; Kai Fang; Menghang Zhu; Nuo Chen; Qian Tu; Qihao Zhang; Qiying Wang; Rang Li; Rui Ma; Shaolei Zhang; Shengfan Wang; Shicheng Li; Shuhao Gu; Shuhuai Ren; Sirui Deng; Tao Guo; Tianyang Lu; Weiji Zhuang; Weikang Zhang; Weimin Xiong; Wenshan Huang; Wenyu Yang; Xin Zhang; Xing Yong; Xu Wang; Xueyang Xie; Yilin Jiang; Yixin Yang; Yongzhe He; Yu Tu; Yuanliang Dong; Yuchen Liu; Yue Ma; Yue Yu; Yuxing Xiang; Zhaojun Huang; Zhenru Lin; Zhipeng Xu; Zhiyang Chen; Zhonghua Deng; Zihan Zhang;, Zihao Yue*2026 · 60 citations*](https://md.research.elamur.ai/free/en/papers/team-2026-llm-mimo-v2-flash-technical/)
31. [**A Survey of Efficient Reasoning for Large Reasoning Models: Language, Multimodality, and Beyond arXiv:2503.21614**Xiaoye Qu, Yafu Li, Zhao-Chen Su, et al.*2025 · 55 citations*](https://md.research.elamur.ai/free/en/papers/qu-2025-survey-efficient-reasoning-large-reasoning/)
32. [**Kimi Linear: An Expressive, Efficient Attention Architecture arXiv:2510.26692**Kimi Team, Yu Zhang, Zongyu Lin, et al.*2025 · 51 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-linear-expressive-efficient/)
33. **[Future Lens: Anticipating Subsequent Tokens from a Single Hidden State](https://md.research.elamur.ai/free/en/papers/pal-2023-future-lens-anticipating-subsequent-tokens/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Koyena Pal, Jiuding Sun, Andrew Yuan, et al.*2023 · 49 citations*
34. **[Demons in the Detail: On Implementing Load Balancing Loss for Training Specialized Mixture-of-Expert Models](https://md.research.elamur.ai/free/en/papers/qiu-2025-demons-detail-implementing-load-balancing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zihan Qiu, Zeyu Huang, Bo Zheng, et al.*2025 · 21 citations*
35. [**GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context Length and Reasoning Complexity? arXiv:2502.05252**Yang Zhou, Hongyi Liu, Zhuoming Chen, et al.*2025 · 21 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2025-gsm-infinite-llms-behave-over/)
36. [**Pangu Ultra MoE: How to Train Your Big MoE on Ascend NPUs arXiv:2505.04519**Yehui Tang, Yichun Yin, Yaoyuan Wang, et al.*2025 · 8 citations*](https://md.research.elamur.ai/free/en/papers/tang-2025-pangu-ultra-moe-train/)
37. **[FAME: Fictional Actors for Multilingual Erasure](https://md.research.elamur.ai/free/en/papers/savelli-2026-fame-fictional-actors-multilingual-erasure/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Claudio Savelli, Moreno La Quatra, Alkis Koudounas, et al.*2026 · 7 citations*
38. **[Context Reasoner: Incentivizing Reasoning Capability for Contextualized Privacy and Safety Compliance via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/hu-2025-context-reasoner-incentivizing-reasoning-capability/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Wenbin Hu, Haoran Li, Huihao Jing, et al.*2025 · 7 citations*
39. [**MoM: Linear Sequence Modeling with Mixture-of-Memories arXiv:2502.13685**Jusen Du, Weigao Sun, Disen Lan, et al.*2025 · 7 citations*](https://md.research.elamur.ai/free/en/papers/du-2025-mom-linear-sequence-modeling-mixture/)
40. **[Recitation over Reasoning: How Cutting-Edge Language Models Can Fail on Elementary School-Level Reasoning Problems?](https://md.research.elamur.ai/free/en/papers/yan-2025-recitation-over-reasoning-how-cutting/) [International Joint Conference on Natural Language Processing (IJCNLP)](https://md.research.elamur.ai/free/en/venues/ijcnlp/)**Kai Yan, Yufei Xu, Zhengyin Du, et al.*2025 · 5 citations*
41. **[HiddenGuard: Fine-Grained Safe Generation with Specialized Representation Router](https://md.research.elamur.ai/free/en/papers/mei-2026-hiddenguard-fine-grained-safe-generation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lingrui Mei, Shenghua Liu, Yiwei Wang, et al.*2026 · 4 citations*
42. [**Every FLOP Counts: Scaling a 300B Mixture-of-Experts LING LLM without Premium GPUs arXiv:2503.05139**Ling Team; Binwei Zeng; Chao Huang; Chao Zhang; Changxin Tian; Cong Chen; Dingnan Jin; Feng Yu; Feng Zhu; Feng Yuan; Fakang Wang; Gangshan Wang; Guangyao Zhai; Haitao Zhang; Huizhong Li; Jun Zhou; Jia Liu; Junpeng Fang; Junjie Ou; Jun Hu; Ji Luo; Ji Zhang; Jian Liu; Jian Sha; Jianxue Qian; Jiewei Wu; Junping Zhao; Jianguo Li; Jubao Feng; Jingchao Di; Junming Xu; Jinghua Yao; Kuan Xu; Kewei Du; Longfei Li; Lei Liang; Lu Yu; Li Tang; Lin Ju; Peng Xu; Qing Cui; Song Liu; Shicheng Li; Shun Song; Song Yan; Tengwei Cai; Tianyi Chen; Ting Guo; Ting Huang; Tao Feng; Tao Wu; Wei Wu; Xiaolu Zhang; Xueming Yang; Xin Zhao; Xiaobo Hu; Xin Lin; Yao Zhao; Yilong Wang; Yongzhen Guo; Yuanyuan Wang; Yue Yang; Yang Cao; Yuhao Fu; Yi Xiong; Yanzhe Li; Zhe Li; Zhiqiang Zhang; Ziqi Liu; Zhaoxin Huan; Zujie Wen; Zhenhang Sun; Zhuoxuan Du;, Zhengyu He*2025 · 4 citations*](https://md.research.elamur.ai/free/en/papers/ling-team-2025-every-flop-counts-scaling/)
43. **[Too Good to be Bad: On the Failure of LLMs to Role-Play Villains](https://md.research.elamur.ai/free/en/papers/yi-2026-too-good-be-bad-failure/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Zihao Yi, Qingxuan Jiang, Ruotian Ma, et al.*2026 · 3 citations*
44. **[a1: Steep Test-time Scaling Law via Environment Augmented Generation](https://md.research.elamur.ai/free/en/papers/mei-2026-a1-steep-test-time-scaling/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lingrui Mei, Shenghua Liu, Yiwei Wang, et al.*2026 · 2 citations*
45. [**MiniMax Sparse Attention arXiv:2606.13392**Xunhao Lai, Weiqi Xu, Yufeng Yang, et al.*2026 · 2 citations*](https://md.research.elamur.ai/free/en/papers/lai-2026-minimax-sparse-attention/)
46. **[Do LLMs Really Memorize Personally Identifiable Information? Revisiting PII Leakage with a Cue-Controlled Memorization Framework](https://md.research.elamur.ai/free/en/papers/luo-2026-do-llms-really-memorize-personally/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoyu Luo, Yiyi Chen, Qiongxiu Li, et al.*2026 · 1 citation*
47. **[Image Corruption-Inspired Membership Inference Attacks against Large Vision-Language Models](https://md.research.elamur.ai/free/en/papers/wu-2026-image-corruption-inspired-membership-inference/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Zongyu Wu, Minhua Lin, Zhiwei Zhang, et al.*2026 · 1 citation*
48. **[Personal Information Parroting in Language Models](https://md.research.elamur.ai/free/en/papers/subramani-2026-personal-information-parroting-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Nishant Subramani, Kshitish Ghate, Mona T. Diab*2026 · 1 citation*
49. **[PRISP: Privacy-Safe Few-Shot Personalization via Lightweight Adaptation](https://md.research.elamur.ai/free/en/papers/park-2026-prisp-privacy-safe-few-shot/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Junho Park, Dohoon Kim, Taesup Moon*2026 · 1 citation*
50. **[StateX: Enhancing RNN Recall via Post-training State Expansion](https://md.research.elamur.ai/free/en/papers/shen-2026-statex-enhancing-rnn-recall-post/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingyu Shen, Yingfa Chen, Zhen Leng Thai, et al.*2026 · 1 citation*
51. **[The Model's Language Matters: A Comparative Privacy Analysis of LLMs](https://md.research.elamur.ai/free/en/papers/mishra-2026-model-s-language-matters-comparative/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Abhishek Kumar Mishra, Antoine Boutet, Lucas Magnana*2026 · 1 citation*
52. **[Data Foundations of Long-Context Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/sun-2026-data-foundations-long-context-language/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Zechen Sun, Yuyang Sun, Zhaochen Su, et al.*2026*
53. **[EL-MIA: Quantifying Membership Inference Risks of Sensitive Entities in LLMs](https://md.research.elamur.ai/free/en/papers/satvaty-2026-el-mia-quantifying-membership-inference/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Ali Satvaty, Suzan Verberne, Fatih Turkmen*2026*
54. **[Exploring Cross-Client Memorization of Training Data in Large Language Models for Federated Learning](https://md.research.elamur.ai/free/en/papers/udsa-2026-exploring-cross-client-memorization-training/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tinnakit Udsa, Can Udomcharoenchaikit, Patomporn Payoungkhamdee, et al.*2026*
55. **[GPT-NL Public Corpus: A Permissively Licensed, Dutch-First Dataset for LLM Pre-training](https://md.research.elamur.ai/free/en/papers/oort-2026-gpt-nl-public-corpus-permissively/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jesse J. Van Oort, Frank Brinkkemper, Erik de Graaf, et al.*2026*
56. **[Investigating Memorization in Language Models Trained via Knowledge Distillation](https://md.research.elamur.ai/free/en/papers/macking-2026-investigating-memorization-language-models-trained/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Maarten Mäcking, Michaela Regneri*2026*
57. **[Measure Twice, Click Once: Co-evolving Proposer and Visual Critic via Reinforcement Learning for GUI Grounding](https://md.research.elamur.ai/free/en/papers/wang-2026-measure-twice-click-once-co/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenkai Wang, Xiyun Li, Hongcan Guo, et al.*2026*
58. **[Mitigating Coordinate Prediction Bias from Positional Encoding Failures](https://md.research.elamur.ai/free/en/papers/tao-2026-mitigating-coordinate-prediction-bias-positional/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xingjian Tao, Yiwei Wang, Yujun Cai, et al.*2026*
59. **[Test-Time Scaling in Multimodal Foundation Models: A Comprehensive Survey of Generation and Reasoning](https://md.research.elamur.ai/free/en/papers/wan-2026-test-time-scaling-multimodal-foundation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Cong Wan, Ying He, Zhongzhan Huang, et al.*2026*

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