Paper Citation Record · LEDGER

DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 6 inbound Pith citation observations for arXiv:2409.06669.

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

pith.paper-citation-record.v1
2409.06669 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 6 of 6 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 6 of 6 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:28:45.717663Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T21:56:15.934324Z

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 7a8c5970-9f6e-496b-97aa-2c95eb41a2ba · 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 DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Reference 17

Resolution: verified exact; arxiv_id, observed 2026-05-12T09:01:24.528936Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4a8fb054-bfc3-4ea0-be7d-502359e8d4e1 · inbound

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE cites this paper.

BEAM: Binary Expert Activation Masking for Dynamic Routing in MoE DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-15T01:59:38.700573Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 8d27f3e0-0598-4e9a-9ae4-314ca7c41741 · inbound

dMoE: dLLMs with Learnable Block Experts cites this paper.

dMoE: dLLMs with Learnable Block Experts DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Reference 27

Resolution: verified exact; arxiv_id, observed 2026-06-28T22:52:45.059089Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 0e5bb5cd-6211-4981-b10c-b7fb395c78d8 · inbound

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts cites this paper.

ProbMoE: Differentiable Probabilistic Routing for Mixture-of-Experts DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Reference 1

Resolution: metadata mismatch; arxiv_id, observed 2026-07-01T21:56:15.935894Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation fda4b290-a52a-4661-86a3-43af9198c253 · inbound

Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference cites this paper.

Beyond Uniform Experts: Cost-Aware Expert Execution for Efficient Multi-Device MoE Inference DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-30T17:14:57.478784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

Observation 4b475ee3-b301-4d0b-a27e-7bccad20a828 · inbound

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding cites this paper.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.717663Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.