Paper Citation Record · LEDGER

Ensuring Fair LLM Serving Amid Diverse Applications

As of 13 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 2 inbound Pith citation observations for arXiv:2411.15997.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2411.15997 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-12T13:44:18.809899Z

measured 42 of 42 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-12T06:34:41.77262+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-04T10:54:01.930191Z

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: arxiv_reference, observed 2026-05-10T06:31:30.837888Z

Reference resolution

40 of 40 outbound references displayed

verified exact1
verified fuzzy21
unresolved18
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 60c07f00-7c7a-4a67-b6a7-56c0610ced4e · outbound

This paper cites SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills.

Ensuring Fair LLM Serving Amid Diverse Applications SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.399866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 903afb31-d4ba-43f2-9d38-6ac7c92c7839 · outbound

This paper cites Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve.

Ensuring Fair LLM Serving Amid Diverse Applications Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.411726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f5f0da80-3403-46ad-b83b-33e4ff6a4dce · outbound

This paper cites Navigating llm service interruptions: Strategies for seamless transitions.

Ensuring Fair LLM Serving Amid Diverse Applications Navigating llm service interruptions: Strategies for seamless transitions

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.611287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ea7c887-6fa5-416c-8f04-77d86173aa19 · outbound

This paper cites an unresolved cited work.

Ensuring Fair LLM Serving Amid Diverse Applications Unresolved cited work

Reference 4

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:44:20.575006Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9af6994d-9086-4b3e-8ef7-4671771893fd · outbound

This paper cites Balancing efficiency and fairness in heterogeneous gpu clusters for deep learning.

Ensuring Fair LLM Serving Amid Diverse Applications Balancing efficiency and fairness in heterogeneous gpu clusters for deep learning

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.542649Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2be4ff73-fe7d-4dca-80c0-6d9436f8ceca · outbound

This paper cites Potentials of multitenancy fine-tuned llm serving.

Ensuring Fair LLM Serving Amid Diverse Applications Potentials of multitenancy fine-tuned llm serving

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.515754Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 66d67221-9fcb-4672-966e-396878c4439b · outbound

This paper cites Analysis and simulation of a fair queueing algorithm.

Ensuring Fair LLM Serving Amid Diverse Applications Analysis and simulation of a fair queueing algorithm

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.463102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3fd097d5-2271-4231-a996-aa5d086ecc41 · outbound

This paper cites Dominant resource fairness: Fair allocation of multiple resource types.

Ensuring Fair LLM Serving Amid Diverse Applications Dominant resource fairness: Fair allocation of multiple resource types

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.423731Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 87115ff7-4ddb-40de-9dc9-dbec2d56e956 · outbound

This paper cites Usage limits in gemini for google workspace.

Ensuring Fair LLM Serving Amid Diverse Applications Usage limits in gemini for google workspace

Reference 9

Resolution: verified exact; raw_fallback, observed 2026-08-12T13:44:19.384346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation bd0be550-9590-43d5-a5f1-609e1149e7a8 · outbound

This paper cites Altruistic scheduling in \ Multi-Resource \ clusters.

Ensuring Fair LLM Serving Amid Diverse Applications Altruistic scheduling in \ Multi-Resource \ clusters

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.382698Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 816fb079-be1e-4d89-b5fb-5e5ad5510036 · outbound

This paper cites DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference.

Ensuring Fair LLM Serving Amid Diverse Applications DeepSpeed-FastGen: High-throughput Text Generation for LLMs via MII and DeepSpeed-Inference

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.524777Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 509b1e95-d612-485f-a828-13fb74df2193 · outbound

This paper cites MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework.

Ensuring Fair LLM Serving Amid Diverse Applications MetaGPT: Meta Programming for A Multi-Agent Collaborative Framework

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.542696Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fd5cd4cc-16c9-404a-92f6-393625e0d11e · outbound

This paper cites Quincy: fair scheduling for distributed computing clusters.

Ensuring Fair LLM Serving Amid Diverse Applications Quincy: fair scheduling for distributed computing clusters

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.329634Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 470366b7-ffa5-4ebd-8a50-39bd91d7872e · outbound

This paper cites an unresolved cited work.

Ensuring Fair LLM Serving Amid Diverse Applications Unresolved cited work

Reference 14

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:44:20.288355Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 09afd792-3144-49da-9f1d-6641821a07c8 · outbound

This paper cites s^ 3 : Increasing gpu utilization during generative inference for higher throughput.

Ensuring Fair LLM Serving Amid Diverse Applications s^ 3 : Increasing gpu utilization during generative inference for higher throughput

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.239887Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 166e92a7-239c-4341-b576-3a0edf126529 · outbound

This paper cites an unresolved cited work.

Ensuring Fair LLM Serving Amid Diverse Applications Unresolved cited work

Reference 16

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:44:20.207348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 4ce09eab-c25b-4e51-bf89-23cc5715dd85 · outbound

This paper cites an unresolved cited work.

Ensuring Fair LLM Serving Amid Diverse Applications Unresolved cited work

Reference 17

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:44:20.176484Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8ddfb100-a8e9-4c65-a3e9-73f5bee0a35b · outbound

This paper cites H., Gonzalez, J., Zhang, H., and Stoica, I.

Ensuring Fair LLM Serving Amid Diverse Applications H., Gonzalez, J., Zhang, H., and Stoica, I

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.603181Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ffa58328-475e-4fcc-ad1a-d427c35df3c5 · outbound

This paper cites LightLLM.

Ensuring Fair LLM Serving Amid Diverse Applications LightLLM

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.096368Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 95a500e5-698f-421e-9f1a-5c67ea4bcab2 · outbound

This paper cites Parrot: Efficient serving of llm-based applications with semantic variable.

Ensuring Fair LLM Serving Amid Diverse Applications Parrot: Efficient serving of llm-based applications with semantic variable

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.063487Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2b22737d-2cd7-4540-9d82-bf927c1e92d3 · outbound

This paper cites Themis: Fair and efficient \ GPU \ cluster scheduling.

Ensuring Fair LLM Serving Amid Diverse Applications Themis: Fair and efficient \ GPU \ cluster scheduling

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:20.024396Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 401f6451-0a4e-4ee0-be40-8df903679a55 · outbound

This paper cites Copilot for microsoft 365.

Ensuring Fair LLM Serving Amid Diverse Applications Copilot for microsoft 365

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.999472Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 83eb44c4-765b-4a34-91a4-20d09edac198 · outbound

This paper cites Microsoft graph throttling guidance.

Ensuring Fair LLM Serving Amid Diverse Applications Microsoft graph throttling guidance

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.967282Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f797e49e-a2a7-40d5-96a1-29e465b662f7 · outbound

This paper cites On packet switches with infinite storage.

Ensuring Fair LLM Serving Amid Diverse Applications On packet switches with infinite storage

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.939245Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 515ced8b-8a35-4cd4-8503-d07cdf071c75 · outbound

This paper cites \ Heterogeneity-Aware \ cluster scheduling policies for deep learning workloads.

Ensuring Fair LLM Serving Amid Diverse Applications \ Heterogeneity-Aware \ cluster scheduling policies for deep learning workloads

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.885100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5c0ed0d9-8cd9-4559-9d8d-6b9b71c8b6ce · outbound

This paper cites Fastertransformer.

Ensuring Fair LLM Serving Amid Diverse Applications Fastertransformer

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.849615Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 2fcbb608-00da-4ec3-aa1c-56fe72101281 · outbound

This paper cites an unresolved cited work.

Ensuring Fair LLM Serving Amid Diverse Applications Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-12T13:44:19.815127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e828dff6-6d86-48a4-b75e-339890dfc5b8 · outbound

This paper cites Why’s gpt 4o insanely limited to free users and even plus users? it literally barely gives you 5 messages in 5-6 hours to the free users.

Ensuring Fair LLM Serving Amid Diverse Applications Why’s gpt 4o insanely limited to free users and even plus users? it literally barely gives you 5 messages in 5-6 hours to the free users

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.772617Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 3138bde1-5e1d-445d-ba6c-6c951ad310b2 · outbound

This paper cites Pricing | openai.

Ensuring Fair LLM Serving Amid Diverse Applications Pricing | openai

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.730194Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 967294e1-468b-4cc6-94e9-371e54fbbfb2 · outbound

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

Ensuring Fair LLM Serving Amid Diverse Applications Splitwise: Efficient generative llm inference using phase splitting

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.703500Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5bc8684-bb01-4918-9114-9734b6510b47 · outbound

This paper cites Efficiently scaling transformer inference.

Ensuring Fair LLM Serving Amid Diverse Applications Efficiently scaling transformer inference

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.713196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 14e73a4e-b45a-4f5d-bd0f-d314c3193807 · outbound

This paper cites K., Subramanya, S.

Ensuring Fair LLM Serving Amid Diverse Applications K., Subramanya, S

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.662247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 98f734fb-d404-45e5-89f9-caebfbadf094 · outbound

This paper cites Slora: Scalable serving of thousands of lora adapters.

Ensuring Fair LLM Serving Amid Diverse Applications Slora: Scalable serving of thousands of lora adapters

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.622383Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation d94018e7-ffc8-4e86-8b44-e965d6f0603c · outbound

This paper cites E., and Stoica, I.

Ensuring Fair LLM Serving Amid Diverse Applications E., and Stoica, I

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T13:44:19.579320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 85903bae-7cfe-419d-87a9-0599bafea013 · outbound

This paper cites N., Kaiser, L.

Ensuring Fair LLM Serving Amid Diverse Applications N., Kaiser, L

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.757203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5992b9cb-c458-4f27-b7a7-7d3c6e3df45f · outbound

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

Ensuring Fair LLM Serving Amid Diverse Applications Fast Distributed Inference Serving for Large Language Models

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.768934Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9fb78799-c750-4eeb-a7b4-dcbf04266ff5 · outbound

This paper cites AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation.

Ensuring Fair LLM Serving Amid Diverse Applications AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.780189Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a9e9f93d-c80d-4b55-a650-2f8a9b9b5cb3 · outbound

This paper cites S., Kim, G.-W., Kim, S., and Chun, B.-G.

Ensuring Fair LLM Serving Amid Diverse Applications S., Kim, G.-W., Kim, S., and Chun, B.-G

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.790064Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1f2ed221-c1fb-4ffa-8f3b-9893e3e7d094 · outbound

This paper cites DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving.

Ensuring Fair LLM Serving Amid Diverse Applications DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving

Reference 39

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.799413Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3d71aa1d-6d6f-4727-80a4-3cbfa08e9b5f · outbound

This paper cites write newline.

Ensuring Fair LLM Serving Amid Diverse Applications write newline

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-12T13:44:18.809899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fd53b50e-bbf0-4ee4-951e-c235dc1d0ce4 · inbound

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda cites this paper.

Cloud-native and Distributed Systems for Efficient and Scalable Large Language Models -- A Research Agenda Ensuring Fair LLM Serving Amid Diverse Applications

Reference 150

Resolution: verified exact; arxiv_id, observed 2026-05-10T06:31:30.839442Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 5f8dbb02-5c22-47fd-b8be-a10e48552223 · inbound

Efficiency and Cost Alignment in Batched LLM Serving via Resource-Fair Scheduling cites this paper.

Efficiency and Cost Alignment in Batched LLM Serving via Resource-Fair Scheduling Ensuring Fair LLM Serving Amid Diverse Applications

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-04T10:54:01.930191Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.