Papers with vLLM
FlagEvalMM: A Flexible Framework for Comprehensive Multimodal Model Evaluation (2025.acl-demo)
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| Challenge: | FlagEvalMM is an evaluation framework designed to assess multimodal models . it is designed to be used for vision-language understanding and generation tasks . |
| Approach: | They propose an evaluation framework that decouples model inference from evaluation through an independent evaluation service. |
| Outcome: | The evaluation framework offers accurate and efficient insights into model strengths and limitations. |
Fast-MIA: Efficient and Scalable Membership Inference for LLMs (2026.acl-demo)
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| Challenge: | a library for evaluating membership inference attacks against large language models (LLMs) has emerged as a crucial technique for auditing privacy risks and copyright infringement in LLMs. |
| Approach: | They propose a Python library for efficiently evaluating membership inference attacks against large language models (LLMs) they use a high-throughput batch inference via vLLM and a cross-method caching architecture that computes intermediate results once and shares them across methods. |
| Outcome: | The proposed library performs a 5 speedup in inference and a cross-method caching architecture that computes intermediate results once and shares them across methods. |
Rankify: A Comprehensive Python Toolkit for Retrieval, Re-Ranking, and Retrieval-Augmented Generation (2026.acl-demo)
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Abdelrahman Abdallah, Bhawna Piryani, Jamshid Mozafari, Andreas Herzinger, Jamie Holdcroft, Adam Jatowt
| Challenge: | Rankify unifies retrieval-augmented generation (RAG) and retrieval based question answering systems. |
| Approach: | They propose an open-source Python toolkit that unifies retrieval-augmented generation in a single modular framework. |
| Outcome: | The proposed framework unifies retrieval-augmented generation (RAG) tools in a single modular framework. |
ScaleLLM: A Resource-Frugal LLM Serving Framework by Optimizing End-to-End Efficiency (2024.emnlp-industry)
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Yuhang Yao, Han Jin, Alay Shah, Shanshan Han, Zijian Hu, Dimitris Stripelis, Yide Ran, Zhaozhuo Xu, Salman Avestimehr, Chaoyang He
| Challenge: | Large language models (LLMs) are widely used in commercial applications . low latency is crucial due to system latency, query concurrency, and computational resources constraints. |
| Approach: | They propose a system that can be resource-efficiently served by addressing bottlenecks beyond LLM inference . they propose 4.3 speed up over vLLM and 1.5 higher throughput . |
| Outcome: | The proposed system outperforms state-of-the-arts with 1.5 higher throughput . it achieves 4.3 speed up with 64 concurrent requests on Mixtral 8x7B . |
OpenRLHF: A Ray-based Easy-to-use, Scalable and High-performance RLHF Framework (2025.emnlp-demos)
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Jian Hu, Xibin Wu, Wei Shen, Jason Klein Liu, Weixun Wang, Songlin Jiang, Haoran Wang, Hao Chen, Bin Chen, Wenkai Fang, null Xianyu, Yu Cao, Haotian Xu, Yiming Liu
| Challenge: | Existing RLHF frameworks face inference bottlenecks and complexity barriers restricting their accessibility for newcomers. |
| Approach: | They propose an open-source RLHF framework that can be used to train large language models. |
| Outcome: | The proposed framework achieves superior training efficiency with speedups ranging from 1.22 to 1.68 across different model sizes compared to state-of-the-art frameworks, while requiring significantly fewer lines of code for implementation. |
AutoChecklist: Composable Pipelines for Checklist Generation and Scoring with LLM-as-a-Judge (2026.acl-demo)
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| Challenge: | AutoChecklist is an open-source library that unifies checklist-based evaluation into composable pipelines. |
| Approach: | They propose an open-source library that unifies checklist-based evaluation into composable pipelines. |
| Outcome: | The open-source library unifies checklist-based evaluation into composable pipelines. |
PagedEviction: Structured Block-wise KV Cache Pruning for Efficient Large Language Model Inference (2026.findings-eacl)
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Krishna Teja Chitty-Venkata, Jie Ye, Siddhisanket Raskar, Anthony Kougkas, Xian Sun, Murali Emani, Venkatram Vishwanath, Bogdan Nicolae
| Challenge: | Large Language Models (LLMs) are exploding to large sizes, including GPT, LLaMA, and DeepSeek. |
| Approach: | They propose a fine-grained, structured KV cache pruning strategy that enhances the memory efficiency of vLLM’s PagedAttention. |
| Outcome: | The proposed method integrates seamlessly with PagedAttention without any modifications to its CUDA attention kernels. |
RelayAttention for Efficient Large Language Model Serving with Long System Prompts (2024.acl-long)
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| Challenge: | a long system prompt causes throughput/latency bottlenecks as the cost of generating the next token increases w.r.t the sequence length. |
| Approach: | They propose an attention algorithm that reads hidden state from DRAM exactly once for a batch of input tokens. |
| Outcome: | The proposed algorithm reduces the need for redundant memory accesses in existing algorithms. |
“Give Me BF16 or Give Me Death”? Accuracy-Performance Trade-Offs in LLM Quantization (2025.acl-long)
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| Challenge: | despite popularity of large language model quantization, there are significant accuracy-performance trade-offs associated with quantization formats. |
| Approach: | They evaluate popular quantization formats across academic benchmarks and real-world tasks . they also examine the difference in text generated by quantized models versus their uncompressed counterparts . |
| Outcome: | The proposed format is lossless across all model scales and incurs low accuracy degradation when properly tuned. |
H-MAS: Hierarchical Multi-Agent Scheduling for Multi-Tenant LLM Serving (2026.findings-acl)
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Yuhan Liu, Cong Xu, Qi Jia, Yihua Wang, Feiyu Chen, Liang Jin, Lu Liu, Yaqian Zhao, Yuting Ding, Xiang Li
| Challenge: | Multi-tenant Model-as-a-Service (MaaS) workloads exhibit non-stationarity across multiple time scales . existing request schedulers often rely on a fixed policy that remains unchanged at runtime . |
| Approach: | They propose a hierarchical multi-agent scheduler that operates in a layered closed loop . they propose to maintain 1.2–3.0 higher Goodput than SGLang and vLLM . |
| Outcome: | Experiments show that H-MAS achieves 1.2–3.0 higher Goodput than SGLang and vLLM . it maintains more stable QoS under diverse request lengths and heterogeneous SLO targets . |