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arxiv: 2605.16867 · v1 · pith:4K5LTY6Dnew · submitted 2026-05-16 · 💻 cs.DC

GoodServe: Towards High-Goodput Serving of Agentic LLM Inferences over Heterogeneous Resources

Boxiao Du , Boning Huangfu , Yizhou Luo , Chen Chen , Zijun Li , Minchen Yu , Xiaoyi Fan , Minyi Guo This is my paper

Pith reviewed 2026-05-19 19:27 UTC · model grok-4.3

classification 💻 cs.DC
keywords agentic LLMgoodputLLM servingheterogeneous GPUsrequest routingSLO complianceruntime migration
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The pith

GoodServe routes agentic LLM requests across heterogeneous GPUs with predict-and-rectify decisions to raise goodput.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper presents GoodServe as a serving system for agentic LLM inferences, where each full request must finish within its latency target. Serving happens on mixed GPU pools, so the system must choose routes that let as many requests as possible meet their SLOs. It does this by first estimating output lengths and GPU loads, then applying a just-enough instance selection rule, and later moving active requests if violation risks appear. The result is higher goodput than prior routing approaches.

Core claim

GoodServe performs inference routing in a predict-and-rectify manner. It estimates request output lengths and GPU serving status accurately, selects routes with a just-enough instance selection heuristic, and periodically monitors active requests to trigger migrations when SLO-violation risks emerge. Evaluations show this raises goodput by up to 27.4 percent over existing methods.

What carries the argument

Predict-and-rectify routing that combines output-length estimates, GPU status checks, a just-enough instance selection heuristic, and runtime request migrations.

If this is right

  • A larger share of agentic requests finish before their end-to-end latency deadlines on mixed hardware.
  • Operators obtain higher effective throughput from the same heterogeneous GPU pool without buying extra capacity.
  • Periodic monitoring and migration reduce the impact of sudden changes in request behavior or resource load.
  • Routing quality depends directly on the accuracy of the length and status estimates used at decision time.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same estimation-plus-heuristic pattern might apply to non-agentic LLM workloads if output-length prediction stays reliable.
  • Combining the approach with dynamic GPU allocation could reduce idle time in cloud clusters serving mixed inference jobs.
  • The migration mechanism could be tested under bursty arrival patterns to see how often it activates in practice.

Load-bearing premise

Estimates of request output lengths and current GPU serving status can be obtained accurately and in a practical way.

What would settle it

Measure goodput when length predictions are replaced with random or constant values and check whether the reported gains over baselines remain.

Figures

Figures reproduced from arXiv: 2605.16867 by Boning Huangfu, Boxiao Du, Chen Chen, Minchen Yu, Minyi Guo, Xiaoyi Fan, Yizhou Luo, Zijun Li.

Figure 1
Figure 1. Figure 1: Inference latency across four GPU architectures under varying batch sizes, for a fixed sequence comprising 100 input tokens and 200 output tokens. In the coming era of agentic AI, LLM inference has be￾come a workhorse workload supporting emerging agentic applications like mathematical reasoning [35], code gen￾eration [18] and database management [22]. Compared with conventional LLM inferences supporting ch… view at source ↗
Figure 2
Figure 2. Figure 2: Performance inferiority of exist￾ing routing strategies. In total, 600 requests (with an arrival rate of 10 requests per sec￾ond) are jointly served by four heterogeneous (V100, A40, A800, H800) GPUs. Each re￾quest has 100 input tokens and has its output token length randomly sampled from [100, 500]. The E2E-SLO is set to 6s. For request routing, in practice a series of methods has already been proposed. F… view at source ↗
Figure 3
Figure 3. Figure 3: GoodServe architecture and workflow. GoodServe workflow. To solve the above optimization problem, a prerequisite is to ob￾tain the coefficients in T(r, g), i.e., qg, pg, dg and L out. We note that it is possible to esti￾mate the demand volume and hardware status in advance [14, 33, 7], yet, on the other hand, it is impossible to make 100% accurate pre￾diction. Therefore, in this paper we propose GoodServe,… view at source ↗
Figure 4
Figure 4. Figure 4: MoE-style output-length predictor. As shown in [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Effect of the EMA-smoothed, black￾box estimation method on queuing time and TPOT. Even after we have obtained both the demand￾and resource-side information, it is still challeng￾ing to find the goodput-optimal request routing scheme. First, the exact optimization problem behind Eq. 1 is NP-hard: with binary routing vari￾ables and bounded GPU memory/compute capaci￾ties, it becomes an integer linear program … view at source ↗
Figure 6
Figure 6. Figure 6: End-to-end performance under different request routing methods. [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗
Figure 9
Figure 9. Figure 9: Average migra￾tion latency under different state transferring methods. 4.2 End-to-End Performance In [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 11
Figure 11. Figure 11: Routing overheads at vary￾ing cluster size and request intensity. GoodServe’s scalability we resort to large-scale simulations. Specifically, we configure a set of virtual IPs each corresponding to a simulated local inference engine. We respectively simulate 8, 32, 128 and 512 instances, and for each case we vary the RPS from 1000 to 10000—all requests handled by a single router. As shown in [PITH_FULL_I… view at source ↗
read the original abstract

Large Language Models (LLMs) play a critical role in emerging agentic applications, where the timely completion of each entire inference is critical. Meanwhile, agentic LLM inferences are increasingly served on heterogeneous GPUs in operator's resource pools. Therefore, it is crucial to route incoming inference requests to appropriate GPUs so that their end-to-end latency requirements are satisfied whenever possible, thereby achieving high goodput. In this paper, we propose GoodServe, a goodput-optimized serving system for agentic inferences over heterogeneous resources. GoodServe performs inference routing in a predict-and-rectify manner. It estimates the request output lengths as well as the GPU serving status in an accurate and also practical manner. Based on information from both the demand and resource sides, it then makes high-quality routing decisions using a just-enough instance selection heuristic. It also periodically monitors SLO-violation risks of active requests and triggers runtime request migrations to address unexpected dynamics. Our evaluations show that GoodServe improves goodput by up to 27.4% over existing routing methods.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript introduces GoodServe, a serving system for agentic LLM inferences over heterogeneous GPUs. It routes requests via a predict-and-rectify approach that estimates output lengths and GPU status, applies a just-enough instance selection heuristic, and uses periodic monitoring plus runtime migrations to mitigate SLO violations. The central empirical claim is an improvement in goodput of up to 27.4% relative to existing routing methods.

Significance. If the reported goodput gains are shown to be robust to realistic prediction error, the work would address a practical need in serving variable, multi-turn agentic workloads on mixed hardware while respecting end-to-end latency targets. The predict-and-rectify plus migration design offers a concrete heuristic that could be adopted in production serving stacks.

major comments (2)
  1. [Evaluation] Evaluation section: the headline 27.4% goodput improvement is presented without any reported accuracy metrics (MAPE, quantile error, etc.) for the output-length predictor on the agentic traces used. Because the just-enough selection heuristic and migration trigger both depend directly on these estimates, the absence of a sensitivity sweep (e.g., injecting 30-40% error) leaves open whether the measured delta survives realistic non-stationary, heavy-tailed output distributions.
  2. [§3] §3 (Design): the claim that output lengths and GPU serving status can be estimated 'in an accurate and also practical manner' is load-bearing for the routing decisions, yet the manuscript supplies neither the concrete prediction model nor its training/validation procedure on multi-turn agentic traces. Without this, it is impossible to judge whether the heuristic remains stable when tool calls or conditional branching alter length distributions mid-execution.
minor comments (2)
  1. The abstract would benefit from a one-sentence summary of the workloads, number of GPUs, and baseline systems used to obtain the 27.4% figure.
  2. [§4] Notation for 'goodput' and 'SLO-violation risk' should be defined at first use and kept consistent with any equations in §4.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. The comments identify areas where additional details and analyses would strengthen the presentation of GoodServe's predict-and-rectify routing approach. We address each major comment below and will revise the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: [Evaluation] Evaluation section: the headline 27.4% goodput improvement is presented without any reported accuracy metrics (MAPE, quantile error, etc.) for the output-length predictor on the agentic traces used. Because the just-enough selection heuristic and migration trigger both depend directly on these estimates, the absence of a sensitivity sweep (e.g., injecting 30-40% error) leaves open whether the measured delta survives realistic non-stationary, heavy-tailed output distributions.

    Authors: We agree that explicit accuracy metrics for the output-length predictor and a sensitivity analysis to prediction errors are important for validating the robustness of the reported goodput gains. In the revised manuscript, we will add these to the Evaluation section: MAPE, quantile errors, and related metrics computed on the agentic traces. We will also include a sensitivity sweep that injects controlled prediction errors (20-50%) to simulate realistic non-stationary and heavy-tailed conditions, showing that the 27.4% improvement holds under such perturbations. revision: yes

  2. Referee: [§3] §3 (Design): the claim that output lengths and GPU serving status can be estimated 'in an accurate and also practical manner' is load-bearing for the routing decisions, yet the manuscript supplies neither the concrete prediction model nor its training/validation procedure on multi-turn agentic traces. Without this, it is impossible to judge whether the heuristic remains stable when tool calls or conditional branching alter length distributions mid-execution.

    Authors: We acknowledge that while §3 describes the estimation of output lengths and GPU status at a conceptual level, it does not provide the concrete prediction model or its training/validation details. In the revision, we will expand §3 to specify the prediction model (including its type and features), the training and validation procedures on multi-turn agentic traces, and how the model accounts for dynamics such as tool calls or conditional branching. This will allow assessment of the heuristic's stability. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical system evaluation with independent performance claims

full rationale

The paper describes a practical serving system (GoodServe) that estimates output lengths and GPU status, applies a just-enough instance selection heuristic, and performs runtime migrations. The headline result (up to 27.4% goodput improvement) is reported as an outcome of evaluations over existing routing methods. No equations, self-citations, or definitions are provided that reduce this empirical delta to a fitted parameter, self-referential prediction, or ansatz imported from prior author work. The estimation step is presented as a precondition rather than a derived result that tautologically produces the gain. This is a standard systems paper whose central claim rests on external benchmarking rather than internal reduction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Review based solely on the abstract; no free parameters, axioms, or invented entities are identifiable from the provided text.

pith-pipeline@v0.9.0 · 5736 in / 963 out tokens · 46156 ms · 2026-05-19T19:27:25.350833+00:00 · methodology

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Reference graph

Works this paper leans on

40 extracted references · 40 canonical work pages · 10 internal anchors

  1. [1]

    Efficient and scalable agentic ai with heteroge- neous systems.arXiv preprint arXiv:2507.19635, 2025

    Zain Asgar, Michelle Nguyen, and Sachin Katti. Efficient and scalable agentic ai with heteroge- neous systems.arXiv preprint arXiv:2507.19635, 2025

    work page arXiv 2025

  2. [2]

    Ai-powered chat agent: Revolutionizing online shopping

    Tina Babu, Rajesh Sharma, et al. Ai-powered chat agent: Revolutionizing online shopping. In2024 2nd International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES), pages 1–5. IEEE, 2024

    work page 2024

  3. [3]

    Optimal scheduling algorithms for llm inference: Theory and practice.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 9(3):1–43, 2025

    Agrim Bari, Parikshit Hegde, and Gustavo de Veciana. Optimal scheduling algorithms for llm inference: Theory and practice.Proceedings of the ACM on Measurement and Analysis of Computing Systems, 9(3):1–43, 2025

    work page 2025

  4. [4]

    LiteLLM: Python sdk and proxy server for unified llm api access

    BerriAI. LiteLLM: Python sdk and proxy server for unified llm api access. https://github. com/BerriAI/litellm, 2026. GitHub repository. Accessed: 2026-04-14

    work page 2026

  5. [5]

    Slice: Slo-driven scheduling for llm inference on edge computing devices.arXiv preprint arXiv:2510.18544, 2025

    Will Chow. Slice: Slo-driven scheduling for llm inference on edge computing devices.arXiv preprint arXiv:2510.18544, 2025

    work page arXiv 2025

  6. [6]

    DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

    DeepSeek-AI. Deepseek-r1: Incentivizing reasoning capability in llms via reinforcement learning.arXiv preprint arXiv:2501.12948, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  7. [7]

    Past-future scheduler for llm serving under sla guarantees

    Ruihao Gong, Shihao Bai, Siyu Wu, Yunqian Fan, Zaijun Wang, Xiuhong Li, Hailong Yang, and Xianglong Liu. Past-future scheduler for llm serving under sla guarantees. InProceedings of the 30th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 2, pages 798–813, 2025

    work page 2025

  8. [8]

    The Llama 3 Herd of Models

    Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, Abhinav Pandey, Abhishek Kadian, Ahmad Al-Dahle, Aiesha Letman, Akhil Mathur, Alan Schelten, Alex Vaughan, et al. The llama 3 herd of models.arXiv preprint arXiv:2407.21783, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  9. [9]

    M \’elange: Cost efficient large language model serving by exploiting gpu heterogeneity.arXiv preprint arXiv:2404.14527, 2024

    Tyler Griggs, Xiaoxuan Liu, Jiaxiang Yu, Doyoung Kim, Wei-Lin Chiang, Alvin Cheung, and Ion Stoica. M \’elange: Cost efficient large language model serving by exploiting gpu heterogeneity.arXiv preprint arXiv:2404.14527, 2024

    work page arXiv 2024

  10. [10]

    LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code

    Naman Jain, King Han, Alex Gu, Wen-Ding Li, Fanjia Yan, Tianjun Zhang, Sida Wang, Ar- mando Solar-Lezama, Koushik Sen, and Ion Stoica. Livecodebench: Holistic and contamination free evaluation of large language models for code.arXiv preprint arXiv:2403.07974, 2024

    work page internal anchor Pith review Pith/arXiv arXiv 2024

  11. [11]

    Serving models, fast and slow: optimizing heterogeneous llm inferencing workloads at scale

    Shashwat Jaiswal, Kunal Jain, Yogesh Simmhan, Anjaly Parayil, Ankur Mallick, Rujia Wang, Renee St Amant, Chetan Bansal, Victor Rühle, Anoop Kulkarni, et al. Sageserve: Opti- mizing llm serving on cloud data centers with forecast aware auto-scaling.arXiv preprint arXiv:2502.14617, 2025

    work page arXiv 2025

  12. [12]

    Demystifying cost-efficiency in llm serving over heterogeneous gpus

    Youhe Jiang, Fangcheng Fu, Xiaozhe Yao, Guoliang He, Xupeng Miao, Ana Klimovic, Bin Cui, Binhang Yuan, and Eiko Yoneki. Demystifying cost-efficiency in llm serving over heterogeneous gpus.arXiv preprint arXiv:2502.00722, 2025

    work page arXiv 2025

  13. [13]

    SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

    Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan. Swe-bench: Can language models resolve real-world github issues?arXiv preprint arXiv:2310.06770, 2023. 10

    work page internal anchor Pith review Pith/arXiv arXiv 2023

  14. [14]

    S3: Increasing gpu utilization during generative inference for higher throughput.Advances in Neural Information Processing Systems, 36:18015–18027, 2023

    Yunho Jin, Chun-Feng Wu, David Brooks, and Gu-Yeon Wei. S3: Increasing gpu utilization during generative inference for higher throughput.Advances in Neural Information Processing Systems, 36:18015–18027, 2023

    work page 2023

  15. [15]

    KAIROS: Stateful, Context-Aware Power-Efficient Agentic Inference Serving

    Hyungjun Kim et al. Kairos: Power-aware serving of agentic ai workloads.arXiv preprint arXiv:2604.16682, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  16. [16]

    Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls.Advances in Neural Information Processing Systems, 36:42330–42357, 2023

    Jinyang Li, Binyuan Hui, Ge Qu, Jiaxi Yang, Binhua Li, Bowen Li, Bailin Wang, Bowen Qin, Ruiying Geng, Nan Huo, et al. Can llm already serve as a database interface? a big bench for large-scale database grounded text-to-sqls.Advances in Neural Information Processing Systems, 36:42330–42357, 2023

    work page 2023

  17. [17]

    {AlpaServe}: Statistical multiplexing with model parallelism for deep learning serving

    Zhuohan Li, Lianmin Zheng, Yinmin Zhong, Vincent Liu, Ying Sheng, Xin Jin, Yanping Huang, Zhifeng Chen, Hao Zhang, Joseph E Gonzalez, et al. {AlpaServe}: Statistical multiplexing with model parallelism for deep learning serving. In17th USENIX Symposium on Operating Systems Design and Implementation (OSDI 23), pages 663–679, 2023

    work page 2023

  18. [18]

    SEW: Self-Evolving Agentic Workflows for Automated Code Generation

    Siwei Liu, Jinyuan Fang, Han Zhou, Yingxu Wang, and Zaiqiao Meng. Sew: Self-evolving agentic workflows for automated code generation.arXiv preprint arXiv:2505.18646, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  19. [19]

    Workload variant autoscaler

    llm-d Project. Workload variant autoscaler. https://llm-d.ai/docs/architecture/ Components/workload-variant-autoscaler, 2026. Accessed: 2026-05-05

    work page 2026

  20. [20]

    Helix: Serving large language models over heterogeneous gpus and network via max-flow

    Yixuan Mei, Yonghao Zhuang, Xupeng Miao, Juncheng Yang, Zhihao Jia, and Rashmi Vinayak. Helix: Serving large language models over heterogeneous gpus and network via max-flow. In Proceedings of the 30th ACM International Conference on Architectural Support for Program- ming Languages and Operating Systems, Volume 1, pages 586–602, 2025

    work page 2025

  21. [21]

    Hexgen-flow: Optimizing llm inference request scheduling for agentic text-to-sql

    You Peng, Youhe Jiang, Wenqi Jiang, Chen Wang, and Binhang Yuan. Hexgen-flow: Optimizing llm inference request scheduling for agentic text-to-sql.arXiv preprint arXiv:2505.05286, 2025

    work page arXiv 2025

  22. [22]

    Askdb: An llm agent for natural language interaction with relational databases.arXiv preprint arXiv:2511.16131, 2025

    Xuan-Quang Phan, Tan-Ha Mai, Thai-Duy Dinh, Minh-Thuan Nguyen, and Lam-Son Lê. Askdb: An llm agent for natural language interaction with relational databases.arXiv preprint arXiv:2511.16131, 2025

    work page arXiv 2025

  23. [23]

    Mooncake: Trading more storage for less computation—a {KVCache-centric} architecture for serving {LLM} chatbot

    Ruoyu Qin, Zheming Li, Weiran He, Jialei Cui, Feng Ren, Mingxing Zhang, Yongwei Wu, Weimin Zheng, and Xinran Xu. Mooncake: Trading more storage for less computation—a {KVCache-centric} architecture for serving {LLM} chatbot. In23rd USENIX Conference on File and Storage Technologies (FAST 25), pages 155–170, 2025

    work page 2025

  24. [24]

    Ray serve documentation

    Ray Project. Ray serve documentation. https://docs.ray.io/en/latest/serve/index. html, 2026. Accessed: 2026-01-25

    work page 2026

  25. [25]

    Ray serve llm routing policies

    Ray Project. Ray serve llm routing policies. https://docs.ray.io/en/latest/serve/ llm/architecture/routing-policies.html, 2026. Accessed: 2026-01-25

    work page 2026

  26. [26]

    Academic Press, 2014

    Pál Révész.The laws of large numbers, volume 4. Academic Press, 2014

    work page 2014

  27. [27]

    Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

    Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, and Jeff Dean. Outrageously large neural networks: The sparsely-gated mixture-of-experts layer.arXiv preprint arXiv:1701.06538, 2017

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  28. [28]

    A statistical interpretation of term specificity and its application in retrieval

    Karen Sparck Jones. A statistical interpretation of term specificity and its application in retrieval. Journal of documentation, 28(1):11–21, 1972

    work page 1972

  29. [29]

    Preble: Efficient distributed prompt scheduling for llm serving.arXiv preprint arXiv:2407.00023, 2024

    Vikranth Srivatsa, Zijian He, Reyna Abhyankar, Dongming Li, and Yiying Zhang. Preble: Efficient distributed prompt scheduling for llm serving.arXiv preprint arXiv:2407.00023, 2024

    work page arXiv 2024

  30. [30]

    Llumnix: Dynamic scheduling for large language model serving

    Biao Sun, Ziming Huang, Hanyu Zhao, Wencong Xiao, Xinyi Zhang, Yong Li, and Wei Lin. Llumnix: Dynamic scheduling for large language model serving. In18th USENIX symposium on operating systems design and implementation (OSDI 24), pages 173–191, 2024

    work page 2024

  31. [31]

    Aibrix: Towards scalable, cost-effective large language model inference infrastructure.arXiv preprint arXiv:2504.03648, 2025

    The AIBrix Team, Jiaxin Shan, Varun Gupta, Le Xu, Haiyang Shi, Jingyuan Zhang, Ning Wang, Linhui Xu, Rong Kang, Tongping Liu, et al. Aibrix: Towards scalable, cost-effective large language model inference infrastructure.arXiv preprint arXiv:2504.03648, 2025. 11

    work page arXiv 2025

  32. [32]

    vllm: A high-throughput and memory-efficient inference and serving engine for llms.https://github.com/vllm-project/vllm, 2026

    vLLM Project. vllm: A high-throughput and memory-efficient inference and serving engine for llms.https://github.com/vllm-project/vllm, 2026. Accessed: 2026-01-25

    work page 2026

  33. [33]

    STAR: Decode-Phase Rescheduling for LLM Inference

    Zhibin Wang, Zetao Hong, Xue Li, Zibo Wang, Shipeng Li, Qingkai Meng, Qing Wang, Chengying Huan, Rong Gu, Sheng Zhong, et al. Adaptive rescheduling in prefill-decode disaggregated llm inference.arXiv preprint arXiv:2510.13668, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  34. [34]

    Combating the Memory Walls: Optimization Pathways for Long-Context Agentic LLM Inference

    Haoran Wu, Can Xiao, Jiayi Nie, Xuan Guo, Binglei Lou, Jeffrey TH Wong, Zhiwen Mo, Cheng Zhang, Przemyslaw Forys, Chengyang Ai, et al. Combating the memory walls: Optimization pathways for long-context agentic llm inference.arXiv preprint arXiv:2509.09505, 2025

    work page internal anchor Pith review Pith/arXiv arXiv 2025

  35. [35]

    Mathagent: Leveraging a mixture-of-math-agent framework for real-world multimodal mathematical error detection

    Yibo Yan, Shen Wang, Jiahao Huo, Philip S Yu, Xuming Hu, and Qingsong Wen. Mathagent: Leveraging a mixture-of-math-agent framework for real-world multimodal mathematical error detection. InProceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 69–82, 2025

    work page 2025

  36. [36]

    Qwen2.5 technical report, 2025

    An Yang, Baosong Yang, Beichen Zhang, Binyuan Hui, Bo Zheng, Bowen Yu, Chengyuan Li, Dayiheng Liu, Fei Huang, Haoran Wei, Huan Lin, Jian Yang, Jianhong Tu, Jianwei Zhang, Jianxin Yang, Jiaxi Yang, Jingren Zhou, Junyang Lin, Kai Dang, Keming Lu, Keqin Bao, Kexin Yang, Le Yu, Mei Li, Mingfeng Xue, Pei Zhang, Qin Zhu, Rui Men, Runji Lin, Tianhao Li, Tianyi T...

    work page 2025

  37. [37]

    Superinfer: Slo-aware rotary schedul- ing and memory management for llm inference on superchips.arXiv preprint arXiv:2601.20309, 2026

    Jiahuan Yu, Mingtao Hu, Zichao Lin, and Minjia Zhang. Superinfer: Slo-aware rotary schedul- ing and memory management for llm inference on superchips.arXiv preprint arXiv:2601.20309, 2026

    work page internal anchor Pith review arXiv 2026

  38. [38]

    Efficient routing of inference requests across llm instances in cloud-edge computing.arXiv preprint arXiv:2507.15553, 2025

    Shibo Yu, Mohammad Goudarzi, and Adel Nadjaran Toosi. Efficient routing of inference requests across llm instances in cloud-edge computing.arXiv preprint arXiv:2507.15553, 2025

    work page arXiv 2025

  39. [39]

    Tempo: Application-aware llm serving with mixed slo requirements.arXiv preprint arXiv:2504.20068,

    Wei Zhang, Zhiyu Wu, Yi Mu, Rui Ning, Banruo Liu, Nikhil Sarda, Myungjin Lee, and Fan Lai. Jitserve: Slo-aware llm serving with imprecise request information.arXiv preprint arXiv:2504.20068, 2025

    work page arXiv 2025

  40. [40]

    Jitserve: Slo-aware llm serving with imprecise request information

    Wei Zhang, Zhiyu Wu, Yi Mu, Rui Ning, Banruo Liu, Nikhil Sarda, Myungjin Lee, and Fan Lai. Jitserve: Slo-aware llm serving with imprecise request information. 2025. 12 A Appendix A.1 Notations used inGoodServe Notation Description rRequest index gGPU index RSet of requests GSet of available GPU backends Dr End-to-end latency deadline (SLO) of requestr Lin...

    work page 2025