Paper Citation Record · LEDGER

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs

As of 7 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 2 inbound Pith citation observations for arXiv:2508.15036.

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

pith.paper-citation-record.v1
2508.15036 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T18:11:10.267219Z

measured 32 of 32 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-06-27T16:49:14.243931Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T01:07:30.144002Z

Reference resolution

30 of 30 outbound references displayed

verified exact4
verified fuzzy16
unresolved10
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation f7d1ab20-78cb-4bf0-91d1-2c023815b789 · outbound

This paper cites Deep residual learning for image recognition,.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Deep residual learning for image recognition,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:14.905246Z

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 a923ea6e-4bb3-46f4-985b-66148248a957 · outbound

This paper cites CNN Feature Map Augmentation for Single-Source Domain Generaliza- tion.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs CNN Feature Map Augmentation for Single-Source Domain Generaliza- tion

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:14.644492Z

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 b1cb0650-4c7b-45f7-80fc-a6005bef8400 · outbound

This paper cites Feature Map Augmentation to Improve Rotation Invariance in Convolutional Neural Networks.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Feature Map Augmentation to Improve Rotation Invariance in Convolutional Neural Networks

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:14.442609Z

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 0f0c5226-d480-4838-b119-79f5cc7f9672 · outbound

This paper cites Visualizing and Understanding Convolutional Networks.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Visualizing and Understanding Convolutional Networks

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:07.771874Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b550f29a-9aed-47ac-8969-43e5795d045b · outbound

This paper cites An Efficient CNN Inference Accelerator Based on Intra- and Inter-Channel Feature Map Compression.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs An Efficient CNN Inference Accelerator Based on Intra- and Inter-Channel Feature Map Compression

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:14.263166Z

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 1cb32afb-a6f4-4572-a1f2-57717fa5ed7f · outbound

This paper cites GACT: Activation Compressed Training for Generic Network Architectures.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs GACT: Activation Compressed Training for Generic Network Architectures

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:14.113587Z

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 35e11ed5-b73f-4ec4-9c48-9e9c9486d0e2 · outbound

This paper cites ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs ActNN: Reducing Training Memory Footprint via 2-Bit Activation Compressed Training

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:13.932658Z

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 1b26f767-8cc4-4699-99d9-2179e2ce2625 · outbound

This paper cites Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Egeria: Efficient DNN Training with Knowledge-Guided Layer Freezing

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:13.749562Z

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 f5463539-7244-4ff3-8ff0-fdd1443d726c · outbound

This paper cites SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs SmartFRZ: An Efficient Training Framework using Attention-Based Layer Freezing

Reference 9

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:11:11.114496Z

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 eaa82b9c-284e-423a-a121-6d8490eec2df · outbound

This paper cites Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:13.638915Z

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 cf59379f-de74-4ea4-9f67-205ba03678da · outbound

This paper cites AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:08.387528Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 34031b2b-88d2-4c9b-a088-3f6c6ad03381 · outbound

This paper cites LayerOut: Freezing Layers in Deep Neural Networks.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs LayerOut: Freezing Layers in Deep Neural Networks

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:13.506268Z

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 2384074e-1fc6-4345-bf64-4ff67428de20 · outbound

This paper cites What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs What Would Elsa Do? Freezing Layers During Transformer Fine-Tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:08.573875Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation aa7f1628-683d-4f2a-8900-34a66b7513cf · outbound

This paper cites FreezeOut: Accelerate Training by Progressively Freezing Layers.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs FreezeOut: Accelerate Training by Progressively Freezing Layers

Reference 14

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:11:10.823484Z

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 37c2ffe3-2a3e-4707-a57c-152aeee783fd · outbound

This paper cites Training data-efficient image transformers & distillation through attention.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Training data-efficient image transformers & distillation through attention

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:08.807726Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3df2a5c0-4bc8-4ef1-9259-67d5ae0dca44 · outbound

This paper cites NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs NeurLZ: An Online Neural Learning-Based Method to Enhance Scientific Lossy Compression

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:08.898782Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 8c4062db-bb40-4e01-99b2-f44d534f5494 · outbound

This paper cites A Review of Deep Transfer Learning and Recent Advancements.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs A Review of Deep Transfer Learning and Recent Advancements

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:13.315980Z

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 8a9e2ef9-cdb5-4cf4-a4e4-f74acfadffe0 · outbound

This paper cites LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs LoRA-C: Parameter-Efficient Fine-Tuning of Robust CNN for IoT Devices

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:09.088987Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1cfb1c2-e7b8-496f-9175-43841837998f · outbound

This paper cites Explicit Inductive Bias for Transfer Learning with Convolutional Networks.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Explicit Inductive Bias for Transfer Learning with Convolutional Networks

Reference 19

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:11:10.673543Z

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 29cbabf4-a170-486d-a66a-c979486729d8 · outbound

This paper cites Fixed-Rate Compressed Floating-Point Arrays.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Fixed-Rate Compressed Floating-Point Arrays

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:13.123788Z

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 16d04ccd-ce91-4063-82c0-bbfd73a84067 · outbound

This paper cites A survey on Image Data Augmentation for Deep Learning.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs A survey on Image Data Augmentation for Deep Learning

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:12.899926Z

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 f3cddc89-3f7a-4719-8c41-b9c35cdd1fc0 · outbound

This paper cites Data Augmentation using Feature Generation for Volumetric Medical Images.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Data Augmentation using Feature Generation for Volumetric Medical Images

Reference 22

Resolution: verified exact; local_arxiv, observed 2026-08-05T18:11:10.476442Z

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 ad231a53-8243-4471-a82e-7bb793b53862 · outbound

This paper cites (2019, May).

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs (2019, May)

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:12.676691Z

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 8eb0e4a7-5c49-41fe-a278-c5c2c33d08c6 · outbound

This paper cites an unresolved cited work.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Unresolved cited work

Reference 24

Resolution: unresolved; raw_fallback, observed 2026-08-05T18:11:12.417729Z

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 d9d5c042-6a1d-4e07-ad3c-cad9aaeccb9f · outbound

This paper cites Domain Generalization with MixStyle.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Domain Generalization with MixStyle

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:09.753746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e62bac5c-c7c6-44c5-bfcf-5746fa0d04f1 · outbound

This paper cites FMix: Enhancing Mixed Sample Data Augmentation.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs FMix: Enhancing Mixed Sample Data Augmentation

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-05T18:11:09.846446Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5ba0e415-f9bd-48bc-9205-bf2dae980f4a · outbound

This paper cites an unresolved cited work.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Unresolved cited work

Reference 27

Resolution: unresolved; raw_fallback, observed 2026-08-05T18:11:12.159229Z

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 41cf56c1-d920-4a27-bf88-29ce8bac3b55 · outbound

This paper cites Learning multiple layers of features from tiny images,.

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs Learning multiple layers of features from tiny images,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:11.887895Z

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 904e9445-a497-4bcb-9fed-faf8dc9f8500 · outbound

This paper cites [Online].

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs [Online]

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:11.575641Z

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 888a9d26-c822-4295-9f78-ac3dfccb501b · outbound

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

MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs ImageNet: A large-scale hierarchical image database,

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T18:11:11.404288Z

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

Observation 628c1594-0b17-4a05-8337-7aacb8c14fea · inbound

Security Considerations for Multi-agent Systems cites this paper.

Security Considerations for Multi-agent Systems MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs

Reference 256

Resolution: verified exact; arxiv_id, observed 2026-05-15T14:15:55.054634Z

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 6cd9d6f5-0f45-439c-b752-f5b3484cdab0 · inbound

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs cites this paper.

Personalization Meets Safety:Mechanisms,Risks,and Mitigations in Personalized LLMs MoEcho: Exploiting Side-Channel Attacks to Compromise User Privacy in Mixture-of-Experts LLMs

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-07-03T01:07:30.145764Z

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.