Paper Citation Record · LEDGER

Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 19 inbound Pith citation observations for arXiv:2309.05444.

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

pith.paper-citation-record.v1
2309.05444 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 19 of 19 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 19 of 19 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T22:53:58.539289Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T06:39:37.888574Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
unresolved0
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 7fa2a2e1-44df-41fa-bdc4-6f207275614c · inbound

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey cites this paper.

Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 60

Resolution: metadata mismatch; arxiv_id, observed 2026-05-13T11:32:36.852883Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c8853ec3-6d50-424d-bdb1-dad0cb6c816a · inbound

Superposition in Transformers: A Novel Way of Building Mixture of Experts cites this paper.

Superposition in Transformers: A Novel Way of Building Mixture of Experts Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-10T22:53:58.539289Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation dbb42976-fe74-4dab-9740-ab63fe3ef16c · inbound

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning cites this paper.

Each Rank Could be an Expert: Single-Ranked Mixture of Experts LoRA for Multi-Task Learning Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-10T14:42:10.777637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8cafb0c-e0f6-48f7-b005-1ecf178f0baa · inbound

Mixture of Experts (MoE): A Big Data Perspective cites this paper.

Mixture of Experts (MoE): A Big Data Perspective Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 201

Resolution: unresolved; no resolver link, observed 2026-08-10T18:56:37.949890Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1d365237-9107-4dd7-b72d-3f21a2b96d82 · inbound

Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient cites this paper.

Joint MoE Scaling Laws: Mixture of Experts Can Be Memory Efficient Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 40

Resolution: unresolved; no resolver link, observed 2026-08-08T20:06:23.605001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea0fa4ca-9df9-4546-831d-4cdbc6f3621d · inbound

CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge cites this paper.

CLONE: Customizing LLMs for Efficient Latency-Aware Inference at the Edge Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 150

Resolution: unresolved; no resolver link, observed 2026-08-07T11:20:30.470783Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b8a0bf2-5a07-4d1e-85ee-a0d3dafc1bd1 · inbound

FlexOlmo: Open Language Models for Flexible Data Use cites this paper.

FlexOlmo: Open Language Models for Flexible Data Use Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-06T18:57:16.092024Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b4b74f0d-11a8-41c8-b54a-5c498aa59604 · inbound

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging cites this paper.

CoMoL: Efficient Mixture of LoRA Experts via Dynamic Core Space Merging Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-02T19:55:06.859701Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d0025eb2-978d-447c-967f-6d94267bdc51 · inbound

Path-Constrained Mixture-of-Experts cites this paper.

Path-Constrained Mixture-of-Experts Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 18

Resolution: metadata mismatch; arxiv_id, observed 2026-05-15T09:19:54.235420Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 093744d8-f5cd-4d69-872a-6029c0da1de8 · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-10T03:29:21.500546Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation ff7ce1a5-b382-4b94-a756-d3891ef0b49d · inbound

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts cites this paper.

Expert Upcycling: Shifting the Compute-Efficient Frontier of Mixture-of-Experts Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 57

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T02:06:15.379580Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 8f38d63d-897a-4878-9b4b-62fa0b316be7 · inbound

ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement cites this paper.

ALAS: Adaptive Long-Horizon Action Synthesis via Async-pathway Stream Disentanglement Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 39

Resolution: verified exact; arxiv_id, observed 2026-05-11T14:06:04.922147Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7b232f5a-6bc3-41bd-b406-b1bf64a4e1e2 · inbound

Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning cites this paper.

Adaptive and Fine-grained Module-wise Expert Pruning for Efficient LoRA-MoE Fine-Tuning Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 25

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T09:01:24.481168Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e9f318a2-db3c-4565-a91f-d89ddd6cecd1 · inbound

Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework cites this paper.

Efficient Handwriting-Based Alzheimer,s Disease Diagnosis Using a Low-Rank Mixture of Experts Deep Learning Framework Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 31

Resolution: metadata mismatch; arxiv_id, observed 2026-05-11T10:01:01.233155Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 0cedc80b-80d5-4038-a091-5c0c2612d871 · inbound

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning cites this paper.

CP-MoE: Consistency-Preserving Mixture-of-Experts for Continual Learning Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 15

Resolution: verified exact; arxiv_id, observed 2026-05-21T08:39:53.484596Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 97537789-3f3c-47ec-96ea-a011c8dc5189 · inbound

BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma cites this paper.

BioFact-MoE: Biologically Factorized Mixture of Experts for Vision-Language Prognostic Modeling in Hepatocellular Carcinoma Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:24:00.319105Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 008d0768-53c0-4ed7-9974-646a0eb4114e · inbound

ARIADNE: Agnostic Routing for Inference-time Adapter DyNamic sElection cites this paper.

ARIADNE: Agnostic Routing for Inference-time Adapter DyNamic sElection Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-07-04T00:49:19.520567Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e998ea05-b8cf-4ae6-9613-a89585273f03 · inbound

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models cites this paper.

Behavioral and Representational Evidence of Binomial Ordering Preferences in Large Language Models Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 123

Resolution: verified exact; arxiv_id, observed 2026-07-04T06:39:37.890111Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation e32156a8-7ccc-4df2-94e0-a83994b27623 · inbound

SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting cites this paper.

SAM+D: Parameter-Efficient Dimensional Lifting of SAM-Family Models via Depth-Routed LoRA and Depth Shifting Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning

Reference 49

Resolution: unresolved; no resolver link, observed 2026-08-03T15:00:01.944124Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.