{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:25:05Z","timestamp":1787689505571,"version":"build-2784847793"},"publisher-location":"New York, NY, USA","reference-count":58,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,8,9]]},"DOI":"10.1145\/3770854.3780283","type":"proceedings-article","created":{"date-parts":[[2026,7,31]],"date-time":"2026-07-31T12:07:40Z","timestamp":1785499660000},"page":"819-830","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["HyFunc: Accelerating LLM-based Function Calls for Agentic AI through Hybrid-Model Cascade and Dynamic Templating"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9682-9934","authenticated-orcid":false,"given":"Weibin","family":"Liao","sequence":"first","affiliation":[{"name":"Peking University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8496-033X","authenticated-orcid":false,"given":"Jian-Guang","family":"Lou","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5451-3253","authenticated-orcid":false,"given":"Haoyi","family":"Xiong","sequence":"additional","affiliation":[{"name":"Microsoft Corporation, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2026,4,20]]},"reference":[{"key":"e_1_3_2_2_1_1","first-page":"1131","article-title":"Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks","author":"Abdelaziz Ibrahim","year":"2024","unstructured":"Ibrahim Abdelaziz, Kinjal Basu, Mayank Agarwal, Sadhana Kumaravel, Matthew Stallone, Rameswar Panda, Yara Rizk, GP Shrivatsa Bhargav, Maxwell Crouse, Chulaka Gunasekara, et al., 2024. Granite-Function Calling Model: Introducing Function Calling Abilities via Multi-task Learning of Granular Tasks. In Empirical Methods in Natural Language Processing (EMNLP). 1131-1139.","journal-title":"Empirical Methods in Natural Language Processing (EMNLP)."},{"key":"e_1_3_2_2_2_1","volume-title":"Agentic ai: Autonomous intelligence for complex goals-a comprehensive survey","author":"Acharya Deepak Bhaskar","year":"2025","unstructured":"Deepak Bhaskar Acharya, Karthigeyan Kuppan, and B Divya. 2025. Agentic ai: Autonomous intelligence for complex goals-a comprehensive survey. IEEE Access (2025)."},{"key":"e_1_3_2_2_3_1","volume-title":"Sarathi: Efficient llm inference by piggybacking decodes with chunked prefills. arXiv preprint arXiv:2308.16369","author":"Agrawal Amey","year":"2023","unstructured":"Amey Agrawal, Ashish Panwar, Jayashree Mohan, Nipun Kwatra, Bhargav S Gulavani, and Ramachandran Ramjee. 2023. Sarathi: Efficient llm inference by piggybacking decodes with chunked prefills. arXiv preprint arXiv:2308.16369 (2023)."},{"key":"e_1_3_2_2_4_1","first-page":"4895","article-title":"GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints","author":"Ainslie Joshua","year":"2023","unstructured":"Joshua Ainslie, James Lee-Thorp, Michiel de Jong, Yury Zemlyanskiy, Federico Lebron, and Sumit Sanghai. 2023. GQA: Training Generalized Multi-Query Transformer Models from Multi-Head Checkpoints. In Empirical Methods in Natural Language Processing (EMNLP). 4895-4901.","journal-title":"Empirical Methods in Natural Language Processing (EMNLP)."},{"key":"e_1_3_2_2_5_1","unstructured":"Jinze Bai Shuai Bai Yunfei Chu Zeyu Cui Kai Dang Xiaodong Deng Yang Fan Wenbin Ge Yu Han Fei Huang et al. 2023. Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)."},{"key":"e_1_3_2_2_6_1","unstructured":"BitAgent. 2025. BitAgent-8B. https:\/\/huggingface.co\/BitAgent\/BitAgent-8B. Accessed: 2025-05-16."},{"key":"e_1_3_2_2_7_1","volume-title":"Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, and Bing Li.","author":"Chen Chuangtao","year":"2024","unstructured":"Chuangtao Chen, Grace Li Zhang, Xunzhao Yin, Cheng Zhuo, Ulf Schlichtmann, and Bing Li. 2024. Livemind: Low-latency large language models with simultaneous inference. arXiv preprint arXiv:2406.14319 (2024)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-1189"},{"key":"e_1_3_2_2_9_1","volume-title":"Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques. arXiv preprint arXiv:2505.02309","author":"Girija Sanjay Surendranath","year":"2025","unstructured":"Sanjay Surendranath Girija, Shashank Kapoor, Lakshit Arora, Dipen Pradhan, Aman Raj, and Ankit Shetgaonkar. 2025. Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques. arXiv preprint arXiv:2505.02309 (2025)."},{"key":"e_1_3_2_2_10_1","volume-title":"Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.12793","author":"Aohan Zeng Team GLM","year":"2024","unstructured":"Team GLM, Aohan Zeng, Bin Xu, Bowen Wang, Chenhui Zhang, Da Yin, Dan Zhang, Diego Rojas, Guanyu Feng, Hanlin Zhao, et al., 2024. Chatglm: A family of large language models from glm-130b to glm-4 all tools. arXiv preprint arXiv:2406.12793 (2024)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2006.100"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00975"},{"key":"e_1_3_2_2_13_1","volume-title":"Tool documentation enables zero-shot tool-usage with large language models. arXiv preprint arXiv:2308.00675","author":"Hsieh Cheng-Yu","year":"2023","unstructured":"Cheng-Yu Hsieh, Si-An Chen, Chun-Liang Li, Yasuhisa Fujii, Alexander Ratner, Chen-Yu Lee, Ranjay Krishna, and Tomas Pfister. 2023. Tool documentation enables zero-shot tool-usage with large language models. arXiv preprint arXiv:2308.00675 (2023)."},{"key":"e_1_3_2_2_14_1","volume-title":"LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations (ICLR).","author":"Hu Edward J.","year":"2021","unstructured":"Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. 2021. LoRA: Low-Rank Adaptation of Large Language Models. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_15_1","unstructured":"Aaron Hurst Adam Lerer Adam P Goucher Adam Perelman Aditya Ramesh Aidan Clark AJ Ostrow Akila Welihinda Alan Hayes Alec Radford et al. 2024. Gpt-4o system card. arXiv preprint arXiv:2410.21276 (2024)."},{"key":"e_1_3_2_2_16_1","volume-title":"Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 897-912","author":"Kamath Aditya K","year":"2025","unstructured":"Aditya K Kamath, Ramya Prabhu, Jayashree Mohan, Simon Peter, Ramachandran Ramjee, and Ashish Panwar. 2025. Pod-attention: Unlocking full prefill-decode overlap for faster llm inference. In Proceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS). 897-912."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-031-20083-0_37"},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Machine Learning (ICML). PMLR","author":"Leviathan Yaniv","year":"2023","unstructured":"Yaniv Leviathan, Matan Kalman, and Yossi Matias. 2023. Fast inference from transformers via speculative decoding. In International Conference on Machine Learning (ICML). PMLR, 19274-19286."},{"key":"e_1_3_2_2_19_1","volume-title":"A survey on large language model acceleration based on kv cache management. arXiv preprint arXiv:2412.19442","author":"Li Haoyang","year":"2024","unstructured":"Haoyang Li, Yiming Li, Anxin Tian, Tianhao Tang, Zhanchao Xu, Xuejia Chen, Nicole Hu, Wei Dong, Qing Li, and Lei Chen. 2024. A survey on large language model acceleration based on kv cache management. arXiv preprint arXiv:2412.19442 (2024)."},{"key":"e_1_3_2_2_20_1","volume-title":"Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP). 4582-4597","author":"Li Xiang Lisa","year":"2021","unstructured":"Xiang Lisa Li and Percy Liang. 2021. Prefix-Tuning: Optimizing Continuous Prompts for Generation. In Association for Computational Linguistics and International Joint Conference on Natural Language Processing (ACL-IJCNLP). 4582-4597."},{"key":"e_1_3_2_2_21_1","unstructured":"Yuchen Li Hengyi Cai Rui Kong Xinran Chen Jiamin Chen Jun Yang Haojie Zhang Jiayi Li Jiayi Wu Yiqun Chen et al. 2025a. Towards AI Search Paradigm. arXiv preprint arXiv:2506.17188 (2025)."},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709395"},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3711896.3737443"},{"key":"e_1_3_2_2_24_1","volume-title":"RankExpert: A Mixture of Textual-and-Behavioral Experts for Multi-Objective Learning-to-Rank in Web Search. In Conference on Knowledge Discovery and Data Mining (KDD). 4437-4449","author":"Li Yuchen","year":"2025","unstructured":"Yuchen Li, Hao Zhang, Yongqi Zhang, Hengyi Cai, Mingxin Cai, Shuaiqiang Wang, Haoyi Xiong, Dawei Yin, and Lei Chen. 2025c. RankExpert: A Mixture of Textual-and-Behavioral Experts for Multi-Objective Learning-to-Rank in Web Search. In Conference on Knowledge Discovery and Data Mining (KDD). 4437-4449."},{"key":"e_1_3_2_2_25_1","volume-title":"TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees. In International Conference on Learning Representations (ICLR).","author":"Liao Weibin","year":"2025","unstructured":"Weibin Liao, Xu Chu, and Yasha Wang. 2025a. TPO: Aligning Large Language Models with Multi-branch & Multi-step Preference Trees. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_26_1","volume-title":"LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Models. arXiv preprint arXiv:2504.02327","author":"Liao Weibin","year":"2025","unstructured":"Weibin Liao, Xin Gao, Tianyu Jia, Rihong Qiu, Yifan Zhu, Yang Lin, Xu Chu, Junfeng Zhao, and Yasha Wang. 2025b. LearNAT: Learning NL2SQL with AST-guided Task Decomposition for Large Language Models. arXiv preprint arXiv:2504.02327 (2025)."},{"key":"e_1_3_2_2_27_1","volume-title":"Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation.","author":"Liao Weibin","year":"2025","unstructured":"Weibin Liao, Tianlong Wang, Yinghao Zhu, Yasha Wang, Junyi Gao, and Liantao Ma. 2025c. Magical: Medical Lay Language Generation via Semantic Invariance and Layperson-tailored Adaptation. (2025)."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709166"},{"key":"e_1_3_2_2_29_1","first-page":"87","article-title":"Awq: Activation-aware weight quantization for on-device llm compression and acceleration","volume":"6","author":"Lin Ji","year":"2024","unstructured":"Ji Lin, Jiaming Tang, Haotian Tang, Shang Yang, Wei-Ming Chen, Wei-Chen Wang, Guangxuan Xiao, Xingyu Dang, Chuang Gan, and Song Han. 2024. Awq: Activation-aware weight quantization for on-device llm compression and acceleration. Proceedings of machine learning and systems (MLSys), Vol. 6 (2024), 87-100.","journal-title":"Proceedings of machine learning and systems (MLSys)"},{"key":"e_1_3_2_2_30_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Lin Qiqiang","year":"2025","unstructured":"Qiqiang Lin, Muning Wen, Qiuying Peng, Guanyu Nie, Junwei Liao, Jun Wang, Xiaoyun Mo, Jiamu Zhou, Cheng Cheng, Yin Zhao, et al., 2025. Robust function-calling for on-device language model via function masking. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-1516"},{"key":"e_1_3_2_2_32_1","volume-title":"ToolACE: Winning the Points of LLM Function Calling. In International Conference on Learning Representations (ICLR).","author":"Liu Weiwen","year":"2025","unstructured":"Weiwen Liu, Xu Huang, Xingshan Zeng, Shuai Yu, Dexun Li, Shuai Wang, Weinan Gan, Zhengying Liu, Yuanqing Yu, Zezhong WANG, et al., 2025. ToolACE: Winning the Points of LLM Function Calling. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_33_1","first-page":"61","article-title":"P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks","author":"Liu Xiao","year":"2022","unstructured":"Xiao Liu, Kaixuan Ji, Yicheng Fu, Weng Tam, Zhengxiao Du, Zhilin Yang, and Jie Tang. 2022. P-Tuning: Prompt Tuning Can Be Comparable to Fine-tuning Across Scales and Tasks. In Association for Computational Linguistics (ACL). 61-68.","journal-title":"Association for Computational Linguistics (ACL)."},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-1725"},{"key":"e_1_3_2_2_35_1","volume-title":"Conference on Language Modeling (COLM).","author":"Luohe Shi","year":"2024","unstructured":"Shi Luohe, Hongyi Zhang, Yao Yao, Zuchao Li, et al., 2024. Keep the Cost Down: A Review on Methods to Optimize LLM's KV-Cache Consumption. In Conference on Language Modeling (COLM)."},{"key":"e_1_3_2_2_36_1","article-title":"Augmented Language Models: a Survey","volume":"2023","author":"Mialon Gr\u00e9goire","year":"2023","unstructured":"Gr\u00e9goire Mialon, Roberto Dess`i, Maria Lomeli, Christoforos Nalmpantis, Ram Pasunuru, Roberta Raileanu, Baptiste Rozi\u00e8re, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, et al., 2023. Augmented Language Models: a Survey. Transactions on Machine Learning Research (TMLR), Vol. 2023 (2023).","journal-title":"Transactions on Machine Learning Research (TMLR)"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV51070.2023.01822"},{"key":"e_1_3_2_2_38_1","unstructured":"NVIDIA. 2025. The Triton TensorRT-LLM Backend. https:\/\/github.com\/triton-inference-server\/tensorrtllm_backend. Accessed: 2025-07-31."},{"key":"e_1_3_2_2_39_1","volume-title":"Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748","author":"van den Oord Aaron","year":"2018","unstructured":"Aaron van den Oord, Yazhe Li, and Oriol Vinyals. 2018. Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748 (2018)."},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Machine Learning (ICML).","author":"Patil Shishir G","year":"2025","unstructured":"Shishir G Patil, Huanzhi Mao, Fanjia Yan, Charlie Cheng-Jie Ji, Vishnu Suresh, Ion Stoica, and Joseph E Gonzalez. 2025. The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models. In International Conference on Machine Learning (ICML)."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-4020"},{"key":"e_1_3_2_2_42_1","volume-title":"UserBench: An Interactive Gym Environment for User-Centric Agents. arXiv preprint arXiv:2507.22034","author":"Qian Cheng","year":"2025","unstructured":"Cheng Qian, Zuxin Liu, Akshara Prabhakar, Zhiwei Liu, Jianguo Zhang, Haolin Chen, Heng Ji, Weiran Yao, Shelby Heinecke, Silvio Savarese, Caiming Xiong, and Huan Wang. 2025. UserBench: An Interactive Gym Environment for User-Centric Agents. arXiv preprint arXiv:2507.22034 (2025)."},{"key":"e_1_3_2_2_43_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Qin Yujia","year":"2024","unstructured":"Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, et al., 2024. ToolLLM: Facilitating Large Language Models to Master 16000 Real-world APIs. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_44_1","volume-title":"NeurIPS 2023 Foundation Models for Decision Making Workshop.","author":"Ruan Jingqing","year":"2023","unstructured":"Jingqing Ruan, Yihong Chen, Bin Zhang, Zhiwei Xu, Tianpeng Bao, Hangyu Mao, Ziyue Li, Xingyu Zeng, Rui Zhao, et al., 2023. Tptu: Task planning and tool usage of large language model-based ai agents. In NeurIPS 2023 Foundation Models for Decision Making Workshop."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-2997"},{"key":"e_1_3_2_2_46_1","unstructured":"Sky Computing Lab UC Berekely. 2025. vLLM: Easy fast and cheap LLM serving for everyone. https:\/\/docs.vllm.ai\/en\/latest\/. Accessed: 2025-07-31."},{"key":"e_1_3_2_2_47_1","volume-title":"Toolalpaca: Generalized tool learning for language models with 3000 simulated cases. arXiv preprint arXiv:2306.05301","author":"Tang Qiaoyu","year":"2023","unstructured":"Qiaoyu Tang, Ziliang Deng, Hongyu Lin, Xianpei Han, Qiao Liang, Boxi Cao, and Le Sun. 2023. Toolalpaca: Generalized tool learning for language models with 3000 simulated cases. arXiv preprint arXiv:2306.05301 (2023)."},{"key":"e_1_3_2_2_48_1","first-page":"3350","article-title":"HammerBench","author":"Wang Jun","year":"2025","unstructured":"Jun Wang, Jiamu Zhou, Xihuai Wang, Xiaoyun Mo, Haoyu Zhang, Qiqiang Lin, Jincheng Jincheng, Muning Wen, Weinan Zhang, and Qiuying Peng. 2025. HammerBench: Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios. In Findings of Association for Computational Linguistics (ACL). 3350-3376.","journal-title":"Fine-Grained Function-Calling Evaluation in Real Mobile Assistant Scenarios. In Findings of Association for Computational Linguistics (ACL)."},{"key":"e_1_3_2_2_49_1","first-page":"1","volume-title":"Conference on Information Retrieval (SIGIR)","volume":"57","author":"Wang Liang","year":"2024","unstructured":"Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, and Furu Wei. 2024. Large search model: Redefining search stack in the era of llms. In Conference on Information Retrieval (SIGIR), Vol. 57. ACM New York, NY, USA, 1-16."},{"key":"e_1_3_2_2_50_1","volume-title":"Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771","author":"Wolf T","year":"2019","unstructured":"T Wolf. 2019. Huggingface's transformers: State-of-the-art natural language processing. arXiv preprint arXiv:1910.03771 (2019)."},{"key":"e_1_3_2_2_51_1","unstructured":"An Yang Anfeng Li Baosong Yang Beichen Zhang Binyuan Hui Bo Zheng Bowen Yu Chang Gao Chengen Huang Chenxu Lv et al. 2025. Qwen3 technical report. arXiv preprint arXiv:2505.09388 (2025)."},{"key":"e_1_3_2_2_52_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Yao Shunyu","year":"2023","unstructured":"Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, and Yuan Cao. 2023. React: Synergizing reasoning and acting in language models. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"crossref","unstructured":"Jingyang Yuan Huazuo Gao Damai Dai Junyu Luo Liang Zhao Zhengyan Zhang Zhenda Xie YX Wei Lean Wang Zhiping Xiao et al. 2025a. Native sparse attention: Hardware-aligned and natively trainable sparse attention. In Association for Computational Linguistics (ACL).","DOI":"10.18653\/v1\/2025.acl-long.1126"},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMC.2025.3588430"},{"key":"e_1_3_2_2_55_1","volume-title":"Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, et al.","author":"Zhang Jianguo","year":"2025","unstructured":"Jianguo Zhang, Tian Lan, Ming Zhu, Zuxin Liu, Thai Quoc Hoang, Shirley Kokane, Weiran Yao, Juntao Tan, Akshara Prabhakar, Haolin Chen, et al., 2025. xLAM: A Family of Large Action Models to Empower AI Agent Systems. In North American Chapter of the Association for Computational Linguistics (NAACL). 11583-11597."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2000"},{"key":"e_1_3_2_2_57_1","volume-title":"18th USENIX Symposium on Operating Systems Design and Implementation (OSDI). 193-210","author":"Zhong Yinmin","year":"2024","unstructured":"Yinmin Zhong, Shengyu Liu, Junda Chen, Jianbo Hu, Yibo Zhu, Xuanzhe Liu, Xin Jin, and Hao Zhang. 2024. : Disaggregating prefill and decoding for goodput-optimized large language model serving. In 18th USENIX Symposium on Operating Systems Design and Implementation (OSDI). 193-210."},{"key":"e_1_3_2_2_58_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Zhu Deyao","year":"2024","unstructured":"Deyao Zhu, Jun Chen, Xiaoqian Shen, Xiang Li, and Mohamed Elhoseiny. 2024. MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Models. In International Conference on Learning Representations (ICLR)."}],"event":{"name":"KDD '26: The 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Jeju Island Republic of Korea","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.1"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3770854.3780283","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,25]],"date-time":"2026-08-25T20:04:49Z","timestamp":1787688289000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3770854.3780283"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,20]]},"references-count":58,"alternative-id":["10.1145\/3770854.3780283","10.1145\/3770854"],"URL":"https:\/\/doi.org\/10.1145\/3770854.3780283","relation":{},"subject":[],"published":{"date-parts":[[2026,4,20]]},"assertion":[{"value":"2026-04-20","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}