Paper Citation Record · LEDGER

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration

As of 19 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:2505.06481.

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

pith.paper-citation-record.v1
2505.06481 v1

Coverage vector

measured 31 of 31 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-15T22:45:12.253030Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

31 of 31 outbound references displayed

verified exact1
verified fuzzy3
unresolved27
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3cb88a68-5a9c-499f-a41d-47a13801a8cd · outbound

This paper cites GPT-4 Technical Report.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration GPT-4 Technical Report

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.108866Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2977fc75-2541-4c08-9382-1b5a6ebbfae7 · outbound

This paper cites Instruction Tuning for Secure Code Generation.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Instruction Tuning for Secure Code Generation

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.140853Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 926e7fdb-af0b-4f6d-a7fd-264c558c3acf · outbound

This paper cites Mixture of Experts with Mixture of Precisions for Tuning Quality of Service.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Mixture of Experts with Mixture of Precisions for Tuning Quality of Service

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.154979Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ad6bc08-7f39-478d-90c6-43f97f14df6f · outbound

This paper cites Averaging Weights Leads to Wider Optima and Better Generalization.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Averaging Weights Leads to Wider Optima and Better Generalization

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.159574Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 67993dd1-3c23-42bc-81de-66d53d5ebf02 · outbound

This paper cites Dataless Knowledge Fusion by Merging Weights of Language Models.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Dataless Knowledge Fusion by Merging Weights of Language Models

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.168712Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 180a2d0f-3b78-4c18-a98c-20115a7492bd · outbound

This paper cites Scalable and Efficient MoE Training for Multitask Multilingual Models.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Scalable and Efficient MoE Training for Multitask Multilingual Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.177984Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bd693f6d-3a48-4236-8e8e-3dc39cbb3c5f · outbound

This paper cites K., El-Araby, E., and El-Ghazawi, T.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration K., El-Araby, E., and El-Ghazawi, T

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:12.977588Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 1fc3f0ee-fe10-4aa9-a96a-fa192e03b7b9 · outbound

This paper cites TruthfulQA: Measuring How Models Mimic Human Falsehoods.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration TruthfulQA: Measuring How Models Mimic Human Falsehoods

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.191723Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e50477da-2adb-4129-8311-01b9b4a8f7cb · outbound

This paper cites A., MacIntyre, R., Bies, A., Ferguson, M., Katz, K., and Schasberger, B.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration A., MacIntyre, R., Bies, A., Ferguson, M., Katz, K., and Schasberger, B

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:12.961418Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation e75e35e0-ff73-4501-ab77-ae34bc89b0ca · outbound

This paper cites Pointer Sentinel Mixture Models.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Pointer Sentinel Mixture Models

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.201264Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f706776d-9051-4c0e-9b59-a2ef268d3098 · outbound

This paper cites Learning More Generalized Experts by Merging Experts in Mixture-of-Experts.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Learning More Generalized Experts by Merging Experts in Mixture-of-Experts

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.210312Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 28b23937-7fa0-437e-a296-b90d895a38f0 · outbound

This paper cites Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.214771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 675643da-3237-4297-8020-47c5b86e76c0 · outbound

This paper cites Wortsman, M., Ilharco, G., Gadre, S.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Wortsman, M., Ilharco, G., Gadre, S

Reference 26

Resolution: verified exact; raw_fallback, observed 2026-08-15T22:45:12.562004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 97984be8-d51f-433c-b35f-b5b268f12e12 · outbound

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

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration MoE-Infinity: Efficient MoE Inference on Personal Machines with Sparsity-Aware Expert Cache

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.233874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 90bcabaa-a40b-4ee2-80a9-1e0eaeb73d1f · outbound

This paper cites TIES-Merging: Resolving Interference When Merging Models.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration TIES-Merging: Resolving Interference When Merging Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.238873Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4635aa8b-5752-4e4d-be90-438bdaba0c1d · outbound

This paper cites MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration MoE-I$^2$: Compressing Mixture of Experts Models through Inter-Expert Pruning and Intra-Expert Low-Rank Decomposition

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.243625Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 51732976-2fb8-4f82-a106-ec094726bdf9 · outbound

This paper cites SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration SurgeryV2: Bridging the Gap Between Model Merging and Multi-Task Learning with Deep Representation Surgery

Reference 30

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.248175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4cbcd22f-5233-4ee4-ac32-627f4265ded2 · outbound

This paper cites Taming Sparsely Activated Transformer with Stochastic Experts.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Taming Sparsely Activated Transformer with Stochastic Experts

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.253030Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d90afda7-6451-4fa6-a7cc-66e96165ae23 · outbound

This paper cites Mixtral of Experts.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Mixtral of Experts

Reference 1991

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.164099Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ebd18d54-e81c-405d-a247-9128159767ba · outbound

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

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Fiddler: CPU-GPU Orchestration for Fast Inference of Mixture-of-Experts Models

Reference 1994

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.173343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e9d518d-105e-415c-bf48-de4c9ac13204 · outbound

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

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Fast Inference of Mixture-of-Experts Language Models with Offloading

Reference 2009

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.135322Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e560976b-51aa-4a73-8b09-283b64fb4c1c · outbound

This paper cites BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 2010

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.150327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1c64c3ed-d511-460e-a6db-c20dfd1dabe7 · outbound

This paper cites M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration M6-10T: A Sharing-Delinking Paradigm for Efficient Multi-Trillion Parameter Pretraining

Reference 2011

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.187026Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 58335d6e-ac25-4847-819c-062afed78d62 · outbound

This paper cites SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration SEER-MoE: Sparse Expert Efficiency through Regularization for Mixture-of-Experts

Reference 2016

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.205697Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2f83e62e-312f-41df-b7c3-fb75f1f40703 · outbound

This paper cites Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Efficient and Effective Weight-Ensembling Mixture of Experts for Multi-Task Model Merging

Reference 2018

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.219620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7000e4ab-0d46-4197-a195-5babe867c87d · outbound

This paper cites Exploring in-memory accelerators and fp- gas for latency-sensitive dnn inference on edge servers.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Exploring in-memory accelerators and fp- gas for latency-sensitive dnn inference on edge servers

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-15T22:45:12.946045Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation a47a333e-07c6-4081-a2f7-b1d752c1d7cd · outbound

This paper cites A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration A Provably Effective Method for Pruning Experts in Fine-tuned Sparse Mixture-of-Experts

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.119688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fcd730d3-e048-4e14-97a4-e59d216fa2a9 · outbound

This paper cites FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.125138Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b72950c3-4cfe-439e-af6d-2931c5b715e3 · outbound

This paper cites Parameter Competition Balancing for Model Merging.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Parameter Competition Balancing for Model Merging

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.130419Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ac90b88-ef90-4698-8c4e-5138cb000122 · outbound

This paper cites Measuring Massive Multitask Language Understanding.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Measuring Massive Multitask Language Understanding

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.145498Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b4c9814-32b5-4088-869a-dafd627ad67b · outbound

This paper cites Task-Specific Expert Pruning for Sparse Mixture-of-Experts.

QoS-Efficient Serving of Multiple Mixture-of-Expert LLMs Using Partial Runtime Reconfiguration Task-Specific Expert Pruning for Sparse Mixture-of-Experts

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-15T22:45:12.114554Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.