Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-05T22:58:28.674042Z
Paper Citation Record · LEDGER
Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments
As of 8 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.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+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 2026-08-07T06:34:17.273281+00:00.
Pith citing papers
No inbound Pith citation observations are available.