Paper Citation Record · LEDGER

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

As of 20 August 2026, this Paper Citation Record lists 100 of 180 outbound references and 4 inbound Pith citation observations for arXiv:2503.08223.

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

pith.paper-citation-record.v1
2503.08223 v3

Coverage vector

measured 100 of 180 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-23T01:03:26.037233Z

measured 104 of 104 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-20T06:33:59.587034+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:19:19.224623Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-19T05:42:06.116850Z

Reference resolution

100 of 180 outbound references displayed

verified exact42
verified fuzzy56
unresolved0
parse uncertain1
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b5fba773-4515-49ae-8fab-4bca1155f151 · outbound

This paper cites Scaling Laws for Neural Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Scaling Laws for Neural Language Models

Reference 1

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.460975Z

Source-reported events for the cited work

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Observation 94999a6e-dad0-46d3-8871-95e63684b8b0 · outbound

This paper cites Training Compute-Optimal Large Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Training Compute-Optimal Large Language Models

Reference 2

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.456726Z

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Observation 422a34e9-4906-40a6-8bba-1df54979dc48 · outbound

This paper cites GPT-4 Technical Report.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices GPT-4 Technical Report

Reference 4

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.465038Z

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Observation 889deac4-d20d-41b2-9a99-b5203d687f27 · outbound

This paper cites Language models are few-shot learners.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Language models are few-shot learners

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.263553Z

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Observation c332d663-49ae-4d54-8c96-eac3068689e6 · outbound

This paper cites PaLM: Scaling Language Modeling with Pathways.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices PaLM: Scaling Language Modeling with Pathways

Reference 6

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.452546Z

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Observation 9e67cc35-395a-4347-ade3-f2c8d65de660 · outbound

This paper cites Carbon Emissions and Large Neural Network Training.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Carbon Emissions and Large Neural Network Training

Reference 7

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.500759Z

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Observation 74470511-edd3-417b-b487-5522f7280b44 · outbound

This paper cites Imagenet: A large- scale hierarchical image database.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Imagenet: A large- scale hierarchical image database

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.093664Z

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Observation 54483555-024f-4cdd-bc3c-0581056f79b6 · outbound

This paper cites LLaMA: Open and Efficient Foundation Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices LLaMA: Open and Efficient Foundation Language Models

Reference 10

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.425244Z

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Observation 89a19af0-78c5-4e72-b99f-e10f9686cab3 · outbound

This paper cites DeepSeek-V3 Technical Report.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices DeepSeek-V3 Technical Report

Reference 11

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.447706Z

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Observation fce5a582-6ca3-494d-9281-283869b8319f · outbound

This paper cites Introducing llama 3.1: Our most capable models to date.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Introducing llama 3.1: Our most capable models to date

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.321978Z

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Observation 35450742-f973-4dd2-b2e3-c14e48b8a076 · outbound

This paper cites Llama 2: Open Foundation and Fine-Tuned Chat Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Llama 2: Open Foundation and Fine-Tuned Chat Models

Reference 13

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.496833Z

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Observation 7d1ecab9-7280-467c-9f31-f9d3ce0dde50 · outbound

This paper cites Exploring the limits of transfer learning with a unified text-to-text transformer.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Exploring the limits of transfer learning with a unified text-to-text transformer

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.324954Z

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Observation 7b1056e2-d47b-4bec-8605-bb65f50141cd · outbound

This paper cites Introduction to federated learning.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Introduction to federated learning

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.313232Z

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Observation 8deab416-dafc-4838-9351-f49889595cd6 · outbound

This paper cites Deduplicating Training Data Makes Language Models Better.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Deduplicating Training Data Makes Language Models Better

Reference 16

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.219164Z

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Observation ff74ee56-9be4-431b-a30c-423054776439 · outbound

This paper cites Short-range order and compositional phase stability in refractory high-entropy alloys via first principles theory and atomistic modelling: NbMoTa, NbMoTaW and VNbMoTaW.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Short-range order and compositional phase stability in refractory high-entropy alloys via first principles theory and atomistic modelling: NbMoTa, NbMoTaW and VNbMoTaW

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.395309Z

Source-reported events for the cited work

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Observation 1a1a64c1-3270-46dd-906a-b98130b33fd4 · outbound

This paper cites Position: Will we run out of data? limits of llm scaling based on human-generated data.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Position: Will we run out of data? limits of llm scaling based on human-generated data

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.316396Z

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Observation de6d5b97-cada-44eb-b0e1-02327be1b867 · outbound

This paper cites On the Diversity of Synthetic Data and its Impact on Training Large Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices On the Diversity of Synthetic Data and its Impact on Training Large Language Models

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.443483Z

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Observation 48330d8b-63b3-432b-900c-6e4e56a51f87 · outbound

This paper cites Ai produces gibberish when trained on too much ai-generated data.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Ai produces gibberish when trained on too much ai-generated data

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.292259Z

Source-reported events for the cited work

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Observation e43a6c45-28b9-477b-89b5-5c9742ebb637 · outbound

This paper cites Bias of ai-generated content: an examination of news produced by large language models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Bias of ai-generated content: an examination of news produced by large language models

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.310654Z

Source-reported events for the cited work

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Observation 700df413-c57d-4b6f-87c5-3f7cf5dc0db1 · outbound

This paper cites General data protection regulation.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices General data protection regulation

Reference 22

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.283589Z

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Observation 29b91d36-60d7-42c7-ba43-413e7feed8d0 · outbound

This paper cites Are ai scaling laws hitting a wall? https://www.linkedin.com/ pulse/ai-scaling-laws-hitting-wall-dean-hardy-white-xchfe/.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Are ai scaling laws hitting a wall? https://www.linkedin.com/ pulse/ai-scaling-laws-hitting-wall-dean-hardy-white-xchfe/

Reference 23

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Observation 6c3fe473-7f22-4334-b396-fd617c4515a5 · outbound

This paper cites Introducing grok-3.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Introducing grok-3

Reference 24

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Observation cd716e03-3edd-47ad-92be-6c87851e3d29 · outbound

This paper cites Deep learning’s diminishing returns.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Deep learning’s diminishing returns

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.274530Z

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Observation 32becdb3-cf51-4728-8ae6-718acd5704bc · outbound

This paper cites The Cost of Training NLP Models: A Concise Overview.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The Cost of Training NLP Models: A Concise Overview

Reference 26

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Observation 823ad7c0-507b-4d2e-9055-37578fb0aa59 · outbound

This paper cites Green ai.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Green ai

Reference 27

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Observation 1ff45ea2-8592-4430-bc23-2ed33ee31e34 · outbound

This paper cites Artificial intelligence and competition policy.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Artificial intelligence and competition policy

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.280237Z

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Observation 1085fe9b-9e4e-4996-bb0f-63486783077c · outbound

This paper cites Accelerating Certifiable Estimation with Preconditioned Eigensolvers.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Accelerating Certifiable Estimation with Preconditioned Eigensolvers

Reference 29

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.525471Z

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Observation cae01baf-5aea-411f-9904-4d12ab66498d · outbound

This paper cites Trends in training dataset sizes.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Trends in training dataset sizes

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.307596Z

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Observation a01342c8-52f5-49ce-9c6e-f7f9a1e75a02 · outbound

This paper cites Will we run out of data? Limits of LLM scaling based on human-generated data.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Will we run out of data? Limits of LLM scaling based on human-generated data

Reference 31

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.560310Z

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Observation 4757fd18-3724-429f-9b72-e56c10ca8dae · outbound

This paper cites Compute trends across three eras of machine learning.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Compute trends across three eras of machine learning

Reference 32

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Observation 5bbdd053-50ee-4d60-be4e-ecfcd4802608 · outbound

This paper cites Pixel Aligned Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Pixel Aligned Language Models

Reference 33

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.461164Z

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Observation cd3ce785-9a80-48c3-b774-60383cc2f983 · outbound

This paper cites The Curse of Recursion: Training on Generated Data Makes Models Forget.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The Curse of Recursion: Training on Generated Data Makes Models Forget

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.448204Z

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Observation 96c6db6c-dc2c-49ec-aaf2-2097805ff81e · outbound

This paper cites Strong Model Collapse.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Strong Model Collapse

Reference 35

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.331326Z

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Observation 14d144f6-84dc-42bd-bb9d-f609f2e577db · outbound

This paper cites Self-Consuming Generative Models Go MAD.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Self-Consuming Generative Models Go MAD

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.243082Z

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Observation d39135e0-bb4f-4eb1-8cae-ba16845ab1db · outbound

This paper cites Quintessences Universe in $f(R, L_m)$ gravity with special form of deceleration parameter.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Quintessences Universe in $f(R, L_m)$ gravity with special form of deceleration parameter

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.304469Z

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Observation 175bddad-d779-4996-aa47-df4a7a781899 · outbound

This paper cites Trends in machine learning hardware.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Trends in machine learning hardware

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.240678Z

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Observation af69e408-0917-488f-8a82-aa533b821d68 · outbound

This paper cites an unresolved cited work.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Unresolved cited work

Reference 39

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Observation 740acc98-f9bd-459f-b098-2a1565ce0f17 · outbound

This paper cites Attention is all you need.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Attention is all you need

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.178358Z

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Observation 32a5f4b0-65f6-46a3-b5ff-9a25b57017b1 · outbound

This paper cites The end of moore’s law? innovation in computer systems continues.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The end of moore’s law? innovation in computer systems continues

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.223910Z

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Observation ddd2f357-90be-46c4-b1e6-373f388e29d0 · outbound

This paper cites Apple, nvidia secure future with taiwan semi’s advanced chips as ai demand soars.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Apple, nvidia secure future with taiwan semi’s advanced chips as ai demand soars

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.334874Z

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Observation c2f9071b-670e-4b5f-a303-2b61efe5f09d · outbound

This paper cites Ai’s hardware hunger: The global semiconductor supply chain under pressure.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Ai’s hardware hunger: The global semiconductor supply chain under pressure

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.121996Z

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Observation fbd6b4a9-c453-4877-948c-cb55bac37680 · outbound

This paper cites V olume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices V olume of data/information created, captured, copied, and consumed worldwide from 2010 to 2025

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.188847Z

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Observation cff3a0d3-65b2-49f5-be69-5df570d9aac7 · outbound

This paper cites Internet of things (iot) connected devices data size worldwide from 2019 to 2025.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Internet of things (iot) connected devices data size worldwide from 2019 to 2025

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.069641Z

Source-reported events for the cited work

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Observation 31396446-4051-4e60-8186-bd6788fe4b61 · outbound

This paper cites Edge computing market size & share analysis report, 2023-2030.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Edge computing market size & share analysis report, 2023-2030

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.075375Z

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Observation 117cf150-55a1-41fa-ba10-986c512e95d9 · outbound

This paper cites How many smartphones are in the world?.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices How many smartphones are in the world?

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.257282Z

Source-reported events for the cited work

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Observation 4ebaaf3c-c174-4b01-aceb-376799dca47b · outbound

This paper cites Dataage white paper: The digitization of the world – from edge to core.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Dataage white paper: The digitization of the world – from edge to core

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.168860Z

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Observation 4bf32d7e-4fb5-4d43-a6d8-031fdccf0b40 · outbound

This paper cites Rethink data report 2020.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Rethink data report 2020

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.232590Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 674e92d8-8d4d-46b6-b270-0f9246b0a504 · outbound

This paper cites A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices A review on edge analytics: Issues, challenges, opportunities, promises, future directions, and applications

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.175207Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c9742289-426b-463b-b326-0d604da2d917 · outbound

This paper cites Edge Computing for IoT, Real-Time Data and Low Latency Processing.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Edge Computing for IoT, Real-Time Data and Low Latency Processing

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.195639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ccdda23a-90e3-4cc8-ba52-d2b53fc1f73d · outbound

This paper cites Small Language Model as Data Prospector for Large Language Model.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Small Language Model as Data Prospector for Large Language Model

Reference 52

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.529736Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7e1edec9-9ebe-4085-9a7b-eee2bc226a28 · outbound

This paper cites iphone 16 pro and 16 pro max - technical specifications.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices iphone 16 pro and 16 pro max - technical specifications

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.247608Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4424cd73-ff75-4f72-8221-7c03ea25715a · outbound

This paper cites Nvidia jetson agx orin tflops specifications.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Nvidia jetson agx orin tflops specifications

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.254275Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2385bf5-d4d2-47ff-9468-94467cf36f75 · outbound

This paper cites NanoReview.net - Gadget Specifications and Comparisons.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices NanoReview.net - Gadget Specifications and Comparisons

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.234004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9f4d2774-68d9-41b6-a9b5-613aaf58c3b7 · outbound

This paper cites Canalys Newsroom - Market Analysis and Research.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Canalys Newsroom - Market Analysis and Research

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.210714Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 56aa619b-4f94-4e3d-8582-05cd8a2843bb · outbound

This paper cites Small Language Models: Survey, Measurements, and Insights.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Small Language Models: Survey, Measurements, and Insights

Reference 57

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.508630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a3008e07-0a3c-4090-bb95-9321f52ad1f8 · outbound

This paper cites A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.135665Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7c425754-3f15-41ed-8bc9-5ae62daaf1ac · outbound

This paper cites A Survey of Small Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices A Survey of Small Language Models

Reference 59

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.488678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bc7090e6-5def-4587-8ef1-73c17f19c09e · outbound

This paper cites TinyBERT: Distilling BERT for Natural Language Understanding.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices TinyBERT: Distilling BERT for Natural Language Understanding

Reference 60

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.228134Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a7afe8d8-bded-4296-af6c-f402c080a3ff · outbound

This paper cites ALBERT: A Lite BERT for Self-supervised Learning of Language Representations.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices ALBERT: A Lite BERT for Self-supervised Learning of Language Representations

Reference 61

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.551221Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f50da84b-53ac-42d2-a2f5-4d9a43121564 · outbound

This paper cites Mamba: Linear-Time Sequence Modeling with Selective State Spaces.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Mamba: Linear-Time Sequence Modeling with Selective State Spaces

Reference 62

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.538409Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 511592ce-da80-49e7-a645-db5cee068e8c · outbound

This paper cites The Zamba2 Suite: Technical Report.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices The Zamba2 Suite: Technical Report

Reference 63

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.204147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation fea57a3b-6a94-4969-a6af-02e7ca4a10e0 · outbound

This paper cites Hymba: A Hybrid-head Architecture for Small Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Hymba: A Hybrid-head Architecture for Small Language Models

Reference 64

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.456536Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 164b05c7-3d3e-4213-9b50-620852b97f5c · outbound

This paper cites xLSTM: Extended Long Short-Term Memory.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices xLSTM: Extended Long Short-Term Memory

Reference 65

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.418223Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d5727db1-3e34-4449-9a74-80799caeed74 · outbound

This paper cites SlimPajama: A 627B token cleaned and deduplicated version of RedPajama.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices SlimPajama: A 627B token cleaned and deduplicated version of RedPajama

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.061828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4152cc87-d6b7-437b-b092-7fd1eb9cce9f · outbound

This paper cites RWKV: Reinventing RNNs for the Transformer Era.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices RWKV: Reinventing RNNs for the Transformer Era

Reference 67

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.543067Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b863800d-b895-4d19-aa95-fd42e1b867b3 · outbound

This paper cites Paloma: A benchmark for evaluating language model fit.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Paloma: A benchmark for evaluating language model fit

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.072261Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 42cec4f7-def9-4c99-bcb5-3749eece08c3 · outbound

This paper cites BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Reference 69

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.326854Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 64d70a58-5540-4113-888c-f645288487f2 · outbound

This paper cites DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter

Reference 70

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.512513Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8ff78982-9271-40c2-9c89-a619c2b5bc01 · outbound

This paper cites Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Switch transformers: Scaling to trillion parameter models with simple and efficient sparsity

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.115400Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c85c56d4-a652-44f8-b8b2-077205e1c253 · outbound

This paper cites Specializing Smaller Language Models towards Multi-Step Reasoning.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Specializing Smaller Language Models towards Multi-Step Reasoning

Reference 72

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.391216Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 723feb34-f13a-46d0-b677-c476282b2e99 · outbound

This paper cites Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Reference 73

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.167661Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 58d9afbd-154c-4c4a-9236-afea97ba7a17 · outbound

This paper cites Exo: Run your own ai cluster at home with everyday devices.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Exo: Run your own ai cluster at home with everyday devices

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.118361Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 30c4ca6f-ba83-40ff-a201-b2af170c4911 · outbound

This paper cites Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Rovinski, Trevor Mudge, Jason Mars, and Lingjia Tang

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.148433Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b1aebb35-e76a-415e-8470-1548c2eb8a77 · outbound

This paper cites MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices MoE$^2$: Optimizing Collaborative Inference for Edge Large Language Models

Reference 76

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.479630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 9842a0ef-b64f-411c-b811-792a7b37caf5 · outbound

This paper cites Edge intelligence: On-demand deep learning model co- inference with device-edge synergy.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Edge intelligence: On-demand deep learning model co- inference with device-edge synergy

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.133341Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b8c494d-9f11-4bc9-be15-6a21ced81128 · outbound

This paper cites Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.386504Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c72c8aa9-2afc-4379-8e17-f1396604e6c6 · outbound

This paper cites On- device training under 256kb memory.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices On- device training under 256kb memory

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.179508Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f3261c13-734b-405d-8e1a-88539111add5 · outbound

This paper cites Tinytl: Reduce memory, not parameters for efficient on-device learning.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Tinytl: Reduce memory, not parameters for efficient on-device learning

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.345860Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1af0f515-e4e2-4a5c-9959-069d37dabdb3 · outbound

This paper cites Zerofl: Efficient on-device training for federated learning with local sparsity.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Zerofl: Efficient on-device training for federated learning with local sparsity

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.353133Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 11f74362-0a83-4a6d-bd22-c6e35afcf617 · outbound

This paper cites ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth- and First-Order Optimization.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices ElasticZO: A Memory-Efficient On-Device Learning with Combined Zeroth- and First-Order Optimization

Reference 82

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.547354Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 12ecd87c-fcd7-4430-b268-776482ee5f35 · outbound

This paper cites Communication-efficient learning of deep networks from decentralized data.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Communication-efficient learning of deep networks from decentralized data

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.356545Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8a8a6448-cd0c-477c-9fde-75ca35675280 · outbound

This paper cites Federated fine-tuning of large language models under heterogeneous language tasks and client resources.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Federated fine-tuning of large language models under heterogeneous language tasks and client resources

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.192346Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e9afd0c6-6c89-4609-a7a4-8a2ca3f79bf5 · outbound

This paper cites Federated Adapter on Foundation Models: An Out-Of-Distribution Approach.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Federated Adapter on Foundation Models: An Out-Of-Distribution Approach

Reference 85

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.199014Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 16457c94-3ef9-455c-a1e4-e7c711026d63 · outbound

This paper cites Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre- trained language models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Fedpetuning: When federated learning meets the parameter-efficient tuning methods of pre- trained language models

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.264537Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d9f3d4d0-cc8b-4d5c-85d0-290a6747cf28 · outbound

This paper cites Feddat: an approach for foundation model finetuning in multi-modal heterogeneous federated learning.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Feddat: an approach for foundation model finetuning in multi-modal heterogeneous federated learning

Reference 87

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.106066Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2b7b23ef-0de2-4533-8a6f-f89b8aa93781 · outbound

This paper cites Fedmatch: Federated learning over heterogeneous question answering data.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Fedmatch: Federated learning over heterogeneous question answering data

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.109711Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4f1d53c0-e029-4df3-bf44-f2d21e74ccdb · outbound

This paper cites Flower: A friendly federated ai framework.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Flower: A friendly federated ai framework

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.136828Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ab2504b7-a1cd-4d62-99db-65d8d5b8c40c · outbound

This paper cites FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices FATE-LLM: A Industrial Grade Federated Learning Framework for Large Language Models

Reference 90

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.404733Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0fccb502-a642-4e4e-8f2a-5260e1a2e2f8 · outbound

This paper cites Opendiloco: An open-source framework for globally distributed low- communication training.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Opendiloco: An open-source framework for globally distributed low- communication training

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.261058Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 494d354e-4fa5-429d-8937-ec1e071be4d0 · outbound

This paper cites Photon: Federated llm pre-training.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Photon: Federated llm pre-training

Reference 92

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.399994Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d489b56c-99f3-4f88-8aed-6721e1e8cdc9 · outbound

This paper cites BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices BioMedLM: A 2.7B Parameter Language Model Trained On Biomedical Text

Reference 93

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.214995Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f9f48c14-223a-4ee6-b2ad-568554700e42 · outbound

This paper cites Advances and open problems in federated learning.Foundations and Trends® in Machine Learning, 14(1–2):1–210.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Advances and open problems in federated learning.Foundations and Trends® in Machine Learning, 14(1–2):1–210

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.257809Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e8bc9eeb-8dd4-4dd9-91e1-f21d9f88acbc · outbound

This paper cites Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Fjord: Fair and accurate federated learning under heterogeneous targets with ordered dropout

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.271389Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b44c15e5-013e-4998-bd8b-c7acf6268233 · outbound

This paper cites Fedrolex: Model-heterogeneous feder- ated learning with rolling sub-model extraction.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Fedrolex: Model-heterogeneous feder- ated learning with rolling sub-model extraction

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.253799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a3333694-ae15-4e41-87e3-cd28b54a892c · outbound

This paper cites On the effects of data heterogeneity on the convergence rates of distributed linear system solvers.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices On the effects of data heterogeneity on the convergence rates of distributed linear system solvers

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.247893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0283c3bd-ac59-406b-9e1e-5a946a85d576 · outbound

This paper cites Azizan-Ruhi, F.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Azizan-Ruhi, F

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.250227Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8f9c6e57-0471-428e-a657-bfd27d9f487b · outbound

This paper cites Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Retrieval-Augmented Mixture of LoRA Experts for Uploadable Machine Learning

Reference 99

Resolution: verified exact; arxiv_id, observed 2026-05-23T01:05:16.426768Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 61e5d846-ffda-48ae-ac83-f2c97fa2b5cb · outbound

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

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices On the Opportunities and Risks of Foundation Models

Reference 100

Resolution: verified exact; local_arxiv, observed 2026-05-23T01:05:16.381004Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 28f6685c-24bc-4575-9f0e-933bb699e1c2 · outbound

This paper cites Democratising artificial intelligence in healthcare: community-driven approaches for ethical solutions.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Democratising artificial intelligence in healthcare: community-driven approaches for ethical solutions

Reference 101

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.240875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d5a53b54-665c-4baa-9794-592e8b3af6d4 · outbound

This paper cites Edge-cloud polarization and collaboration: A comprehensive survey for ai.

Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices Edge-cloud polarization and collaboration: A comprehensive survey for ai

Reference 102

Resolution: verified fuzzy; raw_fallback, observed 2026-05-23T01:05:17.237799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Pith citing papers

Observation 8a4f4d3f-77a7-46aa-ba11-d03793a4a4d5 · inbound

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning cites this paper.

On the Surprising Effectiveness of a Single Global Merging in Decentralized Learning Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices

Reference 78

Resolution: verified exact; local_arxiv, observed 2026-05-19T05:42:06.118989Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

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

Meta-Learning for Speeding Up Large Model Inference in Decentralized Environments cites this paper.

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 e3eeaa60-005a-4ce3-9ef0-43779a4ff816 · inbound

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks cites this paper.

REACH: Reinforcement Learning for Efficient Allocation in Community and Heterogeneous Networks Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices

Reference 48

Resolution: unresolved; no resolver link, observed 2026-08-15T17:19:19.224623Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 460fe8cb-bd17-451d-8e03-ed24a3e7e857 · inbound

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems cites this paper.

LLMOrbit: A Circular Taxonomy of Large Language Models -From Scaling Walls to Agentic AI Systems Will LLMs Scaling Hit the Wall? Breaking Barriers via Distributed Resources on Massive Edge Devices

Reference 168

Resolution: verified exact; local_arxiv, observed 2026-05-16T12:47:53.693658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.