Paper Citation Record · LEDGER

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments

As of 10 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2508.09194.

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

pith.paper-citation-record.v1
2508.09194 v1

Coverage vector

measured 15 of 15 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T22:58:28.674042Z

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+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

15 of 15 outbound references displayed

verified exact0
verified fuzzy4
unresolved8
parse uncertain0
malformed identifier0
metadata mismatch3

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0bd04299-7814-4556-90b3-cc5b3df6e280 · outbound

This paper cites Adaptive Orchestration for Large-Scale Inference on Heterogeneous Accelerator Systems Balancing Cost, Performance, and Resilience.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Adaptive Orchestration for Large-Scale Inference on Heterogeneous Accelerator Systems Balancing Cost, Performance, and Resilience

Reference 2

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T22:58:29.244909Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 2fdcd900-b1ae-4549-815c-96c551da399f · outbound

This paper cites Jerome H Friedman.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Jerome H Friedman

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:27.500867Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d9ad31b5-b720-456d-ba2d-e4f1c0369ccd · outbound

This paper cites Scaling Laws for Autoregressive Generative Modeling.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Scaling Laws for Autoregressive Generative Modeling

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:27.636541Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66a56cb4-f034-4beb-bafa-c9da545ae895 · outbound

This paper cites Hierarchical Text-Conditional Image Generation with CLIP Latents.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Hierarchical Text-Conditional Image Generation with CLIP Latents

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:28.068412Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2110afe0-67c5-4140-9e9b-df5b4b7e3dc5 · outbound

This paper cites ZeRO-Offload: Democratizing Billion-Scale Model Training.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments ZeRO-Offload: Democratizing Billion-Scale Model Training

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:28.214083Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a1af5989-bc41-4591-8e70-9b1c4dc583ed · outbound

This paper cites Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:28.347994Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 98ce9784-1589-461b-bcba-a66cb3431239 · outbound

This paper cites PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments PrivacyRestore: Privacy-Preserving Inference in Large Language Models via Privacy Removal and Restoration

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:28.517009Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bdd78b9-2124-4835-9e08-5891e2b00b46 · outbound

This paper cites ,n} do 3: Extract data embedding Edata i = ψ(Di) 4: for j ∈ {1,.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments ,n} do 3: Extract data embedding Edata i = ψ(Di) 4: for j ∈ {1,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:29.809356Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 80d05131-9a1d-4e14-bec7-e1ad88f5301e · outbound

This paper cites Language models are few-shot learners.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Language models are few-shot learners

Reference 2001

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:30.640860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation a455050e-fdc1-43b2-bc44-9786076ce2ac · outbound

This paper cites The number of cyclic subgroups of finite abelian groups and Menon's identity.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments The number of cyclic subgroups of finite abelian groups and Menon's identity

Reference 2017

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T22:58:28.983729Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 0d952b37-be0d-4716-9e37-4975fd835c85 · outbound

This paper cites Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Geeps: Scalable deep learning on distributed gpus with a gpu-specialized parameter server

Reference 2019

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:30.048318Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation 8cd7a208-ecf5-465e-b67d-6f4788a074d0 · outbound

This paper cites Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:27.369206Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 25bdca14-efd1-4de2-97e0-6a0367612653 · outbound

This paper cites EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments EAGLE: Speculative Sampling Requires Rethinking Feature Uncertainty

Reference 2022

Resolution: unresolved; no resolver link, observed 2026-08-05T22:58:27.832575Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1b9ee2b7-3498-440e-914d-ad8c0bedef42 · outbound

This paper cites Zero: Memory optimization towards training a trillion parameter models.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Zero: Memory optimization towards training a trillion parameter models

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T22:58:30.350419Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Observation f596016f-e67d-42ec-863b-249541c29e9d · outbound

This paper cites Load Balancing with Network Latencies via Distributed Gradient Descent.

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments Load Balancing with Network Latencies via Distributed Gradient Descent

Reference 2025

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T22:58:29.511885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

Pith citing papers

No inbound Pith citation observations are available.