Deploying Foundation Model Powered Agent Services: A Survey | Paper Citation Record

Observation 6fa4800a-d355-49fe-bf6e-e2b61fe09361 · outbound

This paper cites On the Opportunities and Risks of Foundation Models,.

Deploying Foundation Model Powered Agent Services: A Survey On the Opportunities and Risks of Foundation Models,

Reference 1

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Observation 37ebb9b6-bf7b-485a-9235-9e5908e39e7c · outbound

This paper cites The Rise and Potential of Large Language Model Based Agents: A Survey,.

Deploying Foundation Model Powered Agent Services: A Survey The Rise and Potential of Large Language Model Based Agents: A Survey,

Reference 2

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Observation 1efdd7fd-6506-48ec-b5c9-c8293d551286 · outbound

This paper cites 107 Up-to-Date ChatGPT Statistics & User Numbers,.

Deploying Foundation Model Powered Agent Services: A Survey 107 Up-to-Date ChatGPT Statistics & User Numbers,

Reference 3

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Observation 33052f6b-0d67-47ae-988f-9b3c6c585b04 · outbound

This paper cites A Survey on Hardware Accelerators for Large Language Models,.

Deploying Foundation Model Powered Agent Services: A Survey A Survey on Hardware Accelerators for Large Language Models,

Reference 4

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This paper cites A Survey on Scheduling Techniques in Computing and Network Convergence,.

Deploying Foundation Model Powered Agent Services: A Survey A Survey on Scheduling Techniques in Computing and Network Convergence,

Reference 5

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Observation d2a7a6b2-3ea7-4d25-83dd-17da27509e8c · outbound

This paper cites Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems,.

Deploying Foundation Model Powered Agent Services: A Survey Towards Efficient Generative Large Language Model Serving: A Survey from Algorithms to Systems,

Reference 6

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Observation 8c2023fa-f709-4452-8055-df0d414ebf2d · outbound

This paper cites Unleashing the Power of Edge-Cloud Generative AI in Mobile Net- works: A Survey of AIGC Services,.

Deploying Foundation Model Powered Agent Services: A Survey Unleashing the Power of Edge-Cloud Generative AI in Mobile Net- works: A Survey of AIGC Services,

Reference 7

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Observation 3fed0e64-dd77-42c9-a911-0c71f61ca0e6 · outbound

This paper cites Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey,.

Deploying Foundation Model Powered Agent Services: A Survey Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey,

Reference 8

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Observation 793af5de-f3cc-4c38-b0ed-9d1eeab33b4e · outbound

This paper cites A Survey of Large Language Models,.

Deploying Foundation Model Powered Agent Services: A Survey A Survey of Large Language Models,

Reference 9

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Observation badc3e58-19fe-452c-93a1-e05524f3d910 · outbound

This paper cites A Comprehensive Overview of Large Language Models,.

Deploying Foundation Model Powered Agent Services: A Survey A Comprehensive Overview of Large Language Models,

Reference 10

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Observation 9df22d56-6622-40bc-8c33-4066fd3767dc · outbound

This paper cites A Survey on Model Compression for Large Language Models,.

Deploying Foundation Model Powered Agent Services: A Survey A Survey on Model Compression for Large Language Models,

Reference 11

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Observation effbb557-e21f-4d1d-adb0-efc5b6fadc44 · outbound

This paper cites Model Compression and Efficient Inference for Large Language Models: A Survey,.

Deploying Foundation Model Powered Agent Services: A Survey Model Compression and Efficient Inference for Large Language Models: A Survey,

Reference 12

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Observation 78f134e6-b2b7-460c-b33e-400b8e0aa1d9 · outbound

This paper cites A Survey on Knowledge Distillation of Large Language Models.

Deploying Foundation Model Powered Agent Services: A Survey A Survey on Knowledge Distillation of Large Language Models

Reference 13

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Observation b40e5f34-c181-4e85-b872-a94fb6508daf · outbound

This paper cites A survey on large language model based autonomous agents,.

Deploying Foundation Model Powered Agent Services: A Survey A survey on large language model based autonomous agents,

Reference 14

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Observation e73b7eed-4e37-4425-a734-0fdf64dbceb1 · outbound

This paper cites Large Language Model based Multi-Agents: A Survey of Progress and Challenges.

Deploying Foundation Model Powered Agent Services: A Survey Large Language Model based Multi-Agents: A Survey of Progress and Challenges

Reference 15

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Observation 10afd8dd-fe79-4ae4-bf9f-f99442365c87 · outbound

This paper cites FedDSE: Distribution-aware Sub-model Extraction for Fed- erated Learning over Resource-constrained Devices,.

Deploying Foundation Model Powered Agent Services: A Survey FedDSE: Distribution-aware Sub-model Extraction for Fed- erated Learning over Resource-constrained Devices,

Reference 16

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Observation 36e0b7aa-c032-4dc6-b092-48b6d5dc29b7 · outbound

This paper cites Hardware accelerator for multi-head attention and position-wise feed-forward in the trans- former,.

Deploying Foundation Model Powered Agent Services: A Survey Hardware accelerator for multi-head attention and position-wise feed-forward in the trans- former,

Reference 17

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This paper cites Mnnfast: A fast and scalable system architecture for memory-augmented neural networks,.

Deploying Foundation Model Powered Agent Services: A Survey Mnnfast: A fast and scalable system architecture for memory-augmented neural networks,

Reference 18

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Observation 6e2db958-2a28-479b-b2ac-7d27222e241f · outbound

This paper cites NPE: An FPGA-based Overlay Processor for Natural Language Processing.

Deploying Foundation Model Powered Agent Services: A Survey NPE: An FPGA-based Overlay Processor for Natural Language Processing

Reference 19

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Observation 9c3b5ec6-566f-4bf4-9f14-e5cd3356b8c2 · outbound

This paper cites Dfx: A low-latency multi-fpga appliance for accelerating transformer- based text generation,.

Deploying Foundation Model Powered Agent Services: A Survey Dfx: A low-latency multi-fpga appliance for accelerating transformer- based text generation,

Reference 20

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Observation 958d7f88-e6d5-4c14-8d05-db571355d9c0 · outbound

This paper cites Transformer- opu: An fpga-based overlay processor for transformer networks,.

Deploying Foundation Model Powered Agent Services: A Survey Transformer- opu: An fpga-based overlay processor for transformer networks,

Reference 21

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Observation 25e56444-1341-4d47-8ccb-c5297de9bf2f · outbound

This paper cites A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE.

Deploying Foundation Model Powered Agent Services: A Survey A Cost-Efficient FPGA Implementation of Tiny Transformer Model using Neural ODE

Reference 22

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Observation 8d850878-d709-4225-905f-2e22e90ae9f6 · outbound

This paper cites FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs.

Deploying Foundation Model Powered Agent Services: A Survey FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

Reference 23

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Observation 9a83747f-a5dd-475a-b5c3-16531a638ed5 · outbound

This paper cites Aˆ 3: Accelerating attention mechanisms in neural networks with approximation,.

Deploying Foundation Model Powered Agent Services: A Survey Aˆ 3: Accelerating attention mechanisms in neural networks with approximation,

Reference 24

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Observation 92be0b6b-b2b9-43a5-801f-ef4f8f2a3cad · outbound

This paper cites Elsa: Hardware-software co-design for efficient, lightweight self- attention mechanism in neural networks,.

Deploying Foundation Model Powered Agent Services: A Survey Elsa: Hardware-software co-design for efficient, lightweight self- attention mechanism in neural networks,

Reference 25

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Observation c1c09a46-5180-4830-a000-4d930dc38e50 · outbound

This paper cites Spatten: Efficient sparse attention architecture with cascade token and head pruning,.

Deploying Foundation Model Powered Agent Services: A Survey Spatten: Efficient sparse attention architecture with cascade token and head pruning,

Reference 26

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Deploying Foundation Model Powered Agent Services: A Survey Sanger: A co-design framework for enabling sparse attention using reconfigurable architecture,

Reference 27

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Deploying Foundation Model Powered Agent Services: A Survey Energon: Toward efficient acceleration of transformers using dynamic sparse attention,

Reference 28

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Deploying Foundation Model Powered Agent Services: A Survey Att: A fault-tolerant reram accelerator for attention-based neural networks,

Reference 29

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Deploying Foundation Model Powered Agent Services: A Survey In-memory com- puting based accelerator for transformer networks for long sequences,

Reference 30

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Observation d268528a-6776-44ed-bacb-755be315ab66 · outbound

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Deploying Foundation Model Powered Agent Services: A Survey Work in progress: Real-time transformer inference on edge ai accelerators,

Reference 31

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Observation 1165d6dd-7cfd-4073-a9aa-474fb5fe93d7 · outbound

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Deploying Foundation Model Powered Agent Services: A Survey Simplifying Transformer Blocks

Reference 32

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Deploying Foundation Model Powered Agent Services: A Survey Accelerating transformer networks through recomposing softmax layers,

Reference 33

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Observation 1d4ce839-6ebc-417f-8c23-3babb8e89872 · outbound

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Deploying Foundation Model Powered Agent Services: A Survey Inference with Reference: Lossless Acceleration of Large Language Models

Reference 34

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Deploying Foundation Model Powered Agent Services: A Survey Exponentially Faster Language Modelling

Reference 35

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Deploying Foundation Model Powered Agent Services: A Survey Efficient LLM Inference on CPUs

Reference 36

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Deploying Foundation Model Powered Agent Services: A Survey PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU

Reference 37

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This paper cites HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices.

Deploying Foundation Model Powered Agent Services: A Survey HeteGen: Heterogeneous Parallel Inference for Large Language Models on Resource-Constrained Devices

Reference 38

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Deploying Foundation Model Powered Agent Services: A Survey Flexgen: High-throughput generative inference of large language models with a single gpu,

Reference 39

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Deploying Foundation Model Powered Agent Services: A Survey Deja vu: Contextual sparsity MANUSCRIPT 35 for efficient llms at inference time,

Reference 40

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Deploying Foundation Model Powered Agent Services: A Survey LLM in a flash: Efficient Large Language Model Inference with Limited Memory

Reference 41

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Deploying Foundation Model Powered Agent Services: A Survey Fast Transformer Decoding: One Write-Head is All You Need

Reference 42

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Deploying Foundation Model Powered Agent Services: A Survey Gqa: Training generalized multi-query transformer models from multi-head checkpoints,

Reference 43

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This paper cites Efficient memory management for large language model serving with pagedattention,.

Deploying Foundation Model Powered Agent Services: A Survey Efficient memory management for large language model serving with pagedattention,

Reference 44

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Deploying Foundation Model Powered Agent Services: A Survey Flashattention: Fast and memory-efficient exact attention with io-awareness,

Reference 45

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This paper cites FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning.

Deploying Foundation Model Powered Agent Services: A Survey FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning

Reference 46

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This paper cites FlashDecoding++: Faster Large Language Model Inference on GPUs.

Deploying Foundation Model Powered Agent Services: A Survey FlashDecoding++: Faster Large Language Model Inference on GPUs

Reference 47

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Deploying Foundation Model Powered Agent Services: A Survey Bminf: An efficient toolkit for big model inference and tuning,

Reference 48

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Observation a3b902e7-1f47-49c9-bd53-b728007f5130 · outbound

This paper cites Splitwise: Efficient generative LLM inference using phase splitting.

Deploying Foundation Model Powered Agent Services: A Survey Splitwise: Efficient generative LLM inference using phase splitting

Reference 49

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This paper cites Fast Distributed Inference Serving for Large Language Models.

Deploying Foundation Model Powered Agent Services: A Survey Fast Distributed Inference Serving for Large Language Models

Reference 50

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This paper cites Specinfer: Accelerating generative large language model serving with speculative inference and token tree verification,.

Deploying Foundation Model Powered Agent Services: A Survey Specinfer: Accelerating generative large language model serving with speculative inference and token tree verification,

Reference 51

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Observation 31ee100c-7934-4068-9c2a-4b4055b25329 · outbound

This paper cites LLMCad: Fast and Scalable On-device Large Language Model Inference.

Deploying Foundation Model Powered Agent Services: A Survey LLMCad: Fast and Scalable On-device Large Language Model Inference

Reference 52

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This paper cites Deepspeed-moe: Advancing mixture- of-experts inference and training to power next-generation ai scale,.

Deploying Foundation Model Powered Agent Services: A Survey Deepspeed-moe: Advancing mixture- of-experts inference and training to power next-generation ai scale,

Reference 53

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Observation 07db7bd8-db2d-4a4b-a1ab-19d0b9b6def2 · outbound

This paper cites EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices.

Deploying Foundation Model Powered Agent Services: A Survey EdgeMoE: Empowering Sparse Large Language Models on Mobile Devices

Reference 54

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Observation 05763e97-7237-497d-8947-ffbd0783a2d1 · outbound

This paper cites Fast Inference of Mixture-of-Experts Language Models with Offloading.

Deploying Foundation Model Powered Agent Services: A Survey Fast Inference of Mixture-of-Experts Language Models with Offloading

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Observation b8ffe746-a7fa-4456-b87f-0f53b7471532 · outbound

This paper cites MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache.

Deploying Foundation Model Powered Agent Services: A Survey MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache

Reference 56

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Observation ffd3fa2d-16e1-4bf8-b077-17f94695450d · outbound

This paper cites Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models.

Deploying Foundation Model Powered Agent Services: A Survey Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models

Reference 57

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Observation 14284966-53a6-48d0-bcf5-ea3fbd966e5c · outbound

This paper cites Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference.

Deploying Foundation Model Powered Agent Services: A Survey Pre-gated MoE: An Algorithm-System Co-Design for Fast and Scalable Mixture-of-Expert Inference

Reference 58

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Observation 861938d7-6021-4c73-925d-0e00fa6017a8 · outbound

This paper cites Intelligence-endogenous management platform for computing and network convergence,.

Deploying Foundation Model Powered Agent Services: A Survey Intelligence-endogenous management platform for computing and network convergence,

Reference 59

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Observation 01cd9dab-518c-438f-8e77-076f2480bfad · outbound

This paper cites Resource Allocation in Large Language Model Integrated 6G Vehicular Networks.

Deploying Foundation Model Powered Agent Services: A Survey Resource Allocation in Large Language Model Integrated 6G Vehicular Networks

Reference 60

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Observation 732159fa-0d3a-4387-926b-34cf931f7aaa · outbound

This paper cites LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices.

Deploying Foundation Model Powered Agent Services: A Survey LinguaLinked: A Distributed Large Language Model Inference System for Mobile Devices

Reference 61

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Observation f77681b2-d7a4-4b67-afc8-0caeb7c5479d · outbound

This paper cites {MegaScale}: Scaling large language model training to more than 10,000 {GPUs},.

Deploying Foundation Model Powered Agent Services: A Survey {MegaScale}: Scaling large language model training to more than 10,000 {GPUs},

Reference 62

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Observation efb56433-4b92-4de0-a47c-80068e1bf697 · outbound

This paper cites LOSP: Overlap synchronization parallel with local compensation for fast dis- tributed training,.

Deploying Foundation Model Powered Agent Services: A Survey LOSP: Overlap synchronization parallel with local compensation for fast dis- tributed training,

Reference 63

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Observation 8867df43-c025-4684-909d-8fdcb7c72fc3 · outbound

This paper cites Heterogeneous semantic and bit communications: A semi-noma scheme,.

Deploying Foundation Model Powered Agent Services: A Survey Heterogeneous semantic and bit communications: A semi-noma scheme,

Reference 64

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Observation 5b693c08-7272-44e3-801e-307799372b04 · outbound

This paper cites Computing networks enabled semantic communications,.

Deploying Foundation Model Powered Agent Services: A Survey Computing networks enabled semantic communications,

Reference 65

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.358217Z

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Observation ca6f87d5-ed48-48c3-b37d-5f7c7f2e949f · outbound

This paper cites ggerganov/llama.cpp: Port of facebook’s llama model in c/c++.

Deploying Foundation Model Powered Agent Services: A Survey ggerganov/llama.cpp: Port of facebook’s llama model in c/c++

Reference 66

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.366842Z

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Observation 355bf72e-8e52-436a-957a-85c990b00ab7 · outbound

This paper cites MLC-LLM,.

Deploying Foundation Model Powered Agent Services: A Survey MLC-LLM,

Reference 67

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Observation 98ccf142-2d37-402e-839a-b6cda2190e1f · outbound

This paper cites mnn-llm: llm deploy project based mnn.

Deploying Foundation Model Powered Agent Services: A Survey mnn-llm: llm deploy project based mnn

Reference 68

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Observation 3c0dadb3-33c4-4dc4-af41-ded901776204 · outbound

This paper cites Judging llm-as-a-judge with mt-bench and chatbot arena,.

Deploying Foundation Model Powered Agent Services: A Survey Judging llm-as-a-judge with mt-bench and chatbot arena,

Reference 69

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.399188Z

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Observation c4b2c57f-13c2-48b3-904f-b38640a1a19a · outbound

This paper cites Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale,.

Deploying Foundation Model Powered Agent Services: A Survey Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale,

Reference 70

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.407081Z

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Observation 76dd67d8-c208-490e-aa92-810664184221 · outbound

This paper cites Openvino deep learning workbench: Comprehensive analysis and tuning of neural networks inference,.

Deploying Foundation Model Powered Agent Services: A Survey Openvino deep learning workbench: Comprehensive analysis and tuning of neural networks inference,

Reference 71

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.417285Z

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Observation b6b6abe1-10ed-42f3-a287-6dd4b7d766ba · outbound

This paper cites mllm is a fast and lightweight multimodal llm inference engine for mobile and edge devices.

Deploying Foundation Model Powered Agent Services: A Survey mllm is a fast and lightweight multimodal llm inference engine for mobile and edge devices

Reference 72

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.428842Z

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Observation a60b3c2a-b243-4907-8aaa-b2139f5b397e · outbound

This paper cites FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design.

Deploying Foundation Model Powered Agent Services: A Survey FP6-LLM: Efficiently Serving Large Language Models Through FP6-Centric Algorithm-System Co-Design

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.435290Z

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Observation dad5ea92-7201-457d-948a-d4a6b5fbf9e8 · outbound

This paper cites Colossal-ai: A unified deep learning system for large-scale parallel training,.

Deploying Foundation Model Powered Agent Services: A Survey Colossal-ai: A unified deep learning system for large-scale parallel training,

Reference 74

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.442991Z

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Observation bcbcdd4f-44a3-46f7-8ca4-ededf8da6f03 · outbound

This paper cites Efficient large-scale language model training on gpu clusters using megatron-lm,.

Deploying Foundation Model Powered Agent Services: A Survey Efficient large-scale language model training on gpu clusters using megatron-lm,

Reference 75

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.451469Z

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Observation 96fc2a21-cc3a-4522-9d46-b0e91f7abd4d · outbound

This paper cites A tensorrt toolbox for optimized large language model inference,.

Deploying Foundation Model Powered Agent Services: A Survey A tensorrt toolbox for optimized large language model inference,

Reference 76

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Observation b23bdc69-310f-4295-a139-8d59289bf32c · outbound

This paper cites Langchain,.

Deploying Foundation Model Powered Agent Services: A Survey Langchain,

Reference 77

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.466233Z

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Observation a3d30d45-072f-4778-8262-918ba436cc0d · outbound

This paper cites Parrot: Efficient Serving of LLM-based Applications with Semantic Variable,.

Deploying Foundation Model Powered Agent Services: A Survey Parrot: Efficient Serving of LLM-based Applications with Semantic Variable,

Reference 78

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Observation 8b942c15-3a46-48dc-9f52-a0090250d19e · outbound

This paper cites SGLang: Efficient Execution of Structured Language Model Pro- grams,.

Deploying Foundation Model Powered Agent Services: A Survey SGLang: Efficient Execution of Structured Language Model Pro- grams,

Reference 79

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Observation ac887d7e-c96a-413e-9f9a-e6ee15b42898 · outbound

This paper cites Clipper: A {Low-Latency} online prediction serving system,.

Deploying Foundation Model Powered Agent Services: A Survey Clipper: A {Low-Latency} online prediction serving system,

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.484686Z

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Observation 8c75d58a-cda4-4079-844a-492d2e914a6c · outbound

This paper cites {MArk}: Exploiting cloud services for {Cost-Effective},{SLO-Aware} machine learning infer- ence serving,.

Deploying Foundation Model Powered Agent Services: A Survey {MArk}: Exploiting cloud services for {Cost-Effective},{SLO-Aware} machine learning infer- ence serving,

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.491891Z

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Observation 030d2edc-7d65-408f-9de6-48f87d81d57a · outbound

This paper cites Nexus: A GPU cluster engine for accelerating DNN-based video analysis,.

Deploying Foundation Model Powered Agent Services: A Survey Nexus: A GPU cluster engine for accelerating DNN-based video analysis,

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.503290Z

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Observation ebeae4ce-07a8-4d6e-9af6-25ef18f0038c · outbound

This paper cites InferLine: latency-aware provisioning and scaling for prediction serving pipelines,.

Deploying Foundation Model Powered Agent Services: A Survey InferLine: latency-aware provisioning and scaling for prediction serving pipelines,

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.512310Z

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Observation 7ec38905-e53b-4d14-93f1-d35f6ff26610 · outbound

This paper cites Serving {DNNs} like clockwork: Performance predictability from the bottom up,.

Deploying Foundation Model Powered Agent Services: A Survey Serving {DNNs} like clockwork: Performance predictability from the bottom up,

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.523724Z

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Observation 12c97abf-1e7e-4adf-b179-3bb11cb4d26a · outbound

This paper cites {INFaaS}: Automated model-less inference serving,.

Deploying Foundation Model Powered Agent Services: A Survey {INFaaS}: Automated model-less inference serving,

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.535944Z

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Observation 79c828ba-4997-41a6-9011-2abe536cc5ef · outbound

This paper cites Morphling: Fast, near-optimal auto-configuration for cloud- native model serving,.

Deploying Foundation Model Powered Agent Services: A Survey Morphling: Fast, near-optimal auto-configuration for cloud- native model serving,

Reference 86

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.548707Z

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Observation f59425d5-cc09-42a4-b12e-e8e0c3df4370 · outbound

This paper cites Cocktail: A multidimensional optimization for model serving in cloud,.

Deploying Foundation Model Powered Agent Services: A Survey Cocktail: A multidimensional optimization for model serving in cloud,

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.561212Z

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Observation 29ff4f30-31c1-442b-9fa4-ac8997312935 · outbound

This paper cites Kairos: Building cost- efficient machine learning inference systems with heterogeneous cloud resources,.

Deploying Foundation Model Powered Agent Services: A Survey Kairos: Building cost- efficient machine learning inference systems with heterogeneous cloud resources,

Reference 88

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.571495Z

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Observation 7b50d3ce-3ab0-4234-a896-ecbe3508f2a7 · outbound

This paper cites {SHEPHERD}: Serving {DNNs} in the wild,.

Deploying Foundation Model Powered Agent Services: A Survey {SHEPHERD}: Serving {DNNs} in the wild,

Reference 89

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.577939Z

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Observation 07bab909-0504-446c-9e35-980890ebb3d3 · outbound

This paper cites SpotServe: Serving Generative Large Language Models on Preemptible Instances.

Deploying Foundation Model Powered Agent Services: A Survey SpotServe: Serving Generative Large Language Models on Preemptible Instances

Reference 90

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.586601Z

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Observation 6f08276c-940a-4d38-85f2-0977bf46de00 · outbound

This paper cites Frequency resource allocation and interference management in mobile edge com- puting for an Internet of Things system,.

Deploying Foundation Model Powered Agent Services: A Survey Frequency resource allocation and interference management in mobile edge com- puting for an Internet of Things system,

Reference 91

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.596352Z

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Observation 7b68c0dc-42db-45fb-a4aa-edffeab85ec5 · outbound

This paper cites Decentralized resource auctioning for latency-sensitive edge computing,.

Deploying Foundation Model Powered Agent Services: A Survey Decentralized resource auctioning for latency-sensitive edge computing,

Reference 92

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.606093Z

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Observation 84201086-57d4-4728-b300-9db4c5e69dcd · outbound

This paper cites Joint computation partitioning and resource allocation for latency sensitive applications in mobile edge clouds,.

Deploying Foundation Model Powered Agent Services: A Survey Joint computation partitioning and resource allocation for latency sensitive applications in mobile edge clouds,

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.618571Z

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Observation 4724aa46-9daf-4365-9e04-376fe80dc311 · outbound

This paper cites Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment,.

Deploying Foundation Model Powered Agent Services: A Survey Adaptive computation offloading and resource allocation strategy in a mobile edge computing environment,

Reference 94

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.629199Z

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Observation 7c911632-5049-4b4d-9ba5-b8a73f4032fc · outbound

This paper cites Resource allocation based on deep reinforcement learning in IoT edge computing,.

Deploying Foundation Model Powered Agent Services: A Survey Resource allocation based on deep reinforcement learning in IoT edge computing,

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.639635Z

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Observation dd23b577-6374-47c8-88ce-3c09a1d6d2d5 · outbound

This paper cites CE-IoT: Cost-effective cloud- edge resource provisioning for heterogeneous IoT applications,.

Deploying Foundation Model Powered Agent Services: A Survey CE-IoT: Cost-effective cloud- edge resource provisioning for heterogeneous IoT applications,

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.655729Z

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Observation 5f2b4c56-efc9-4244-9b74-fa68f59b551f · outbound

This paper cites Dynamic resource allocation and computation offloading for IoT fog computing system,.

Deploying Foundation Model Powered Agent Services: A Survey Dynamic resource allocation and computation offloading for IoT fog computing system,

Reference 97

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.667356Z

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Observation 22f70170-8dd8-47b5-95ed-e6cd26485d8a · outbound

This paper cites Lass: Running latency sensitive serverless computations at the edge,.

Deploying Foundation Model Powered Agent Services: A Survey Lass: Running latency sensitive serverless computations at the edge,

Reference 98

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.679429Z

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Observation d0cabc15-1ee0-429e-89e6-962312b923ca · outbound

This paper cites Resource provisioning and allocation in function- as-a-service edge-clouds,.

Deploying Foundation Model Powered Agent Services: A Survey Resource provisioning and allocation in function- as-a-service edge-clouds,

Reference 99

Resolution: unresolved; no resolver link, observed 2026-08-11T13:09:45.686643Z

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Observation cb3aa6b7-dbae-48d6-ba56-9f0d6eb62aa8 · outbound

This paper cites KneeScale: Efficient resource scaling for serverless computing at the edge,.

Deploying Foundation Model Powered Agent Services: A Survey KneeScale: Efficient resource scaling for serverless computing at the edge,

Reference 100

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