Paper Citation Record · LEDGER

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding

As of 18 August 2026, this Paper Citation Record lists 34 of 34 outbound references and 0 inbound Pith citation observations for arXiv:2608.02989.

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

pith.paper-citation-record.v1
2608.02989 v1

Coverage vector

measured 34 of 34 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-08T04:28:45.737861Z

measured 34 of 34 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 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

34 of 34 outbound references displayed

verified exact0
verified fuzzy9
unresolved23
parse uncertain0
malformed identifier0
metadata mismatch2

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 40891e98-c9b6-40e4-bbbd-599796115124 · outbound

This paper cites Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Making Every Verified Token Count: Adaptive Verification for MoE Speculative Decoding

Reference 1

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T04:28:46.463927Z

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 f982ab5e-4fbe-4dbe-b715-5723364ae50c · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Advances in Neural Information Processing Systems , volume=

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.249203Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation eaa22819-ae9f-4525-b4ef-8b42c05e1c1e · outbound

This paper cites Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Proceedings of the 62nd annual meeting of the association for computational linguistics (volume 1: Long papers) , pages=

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.422643Z

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 ba906e9e-ccf1-4d2b-83ef-ab80e51e217b · outbound

This paper cites International Conference on Machine Learning , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding International Conference on Machine Learning , pages=

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.326826Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c2967bb7-6d58-4591-9511-e227af83b518 · outbound

This paper cites Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.376233Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4d026f86-d32e-478c-80c7-9b31df619ec4 · outbound

This paper cites Proceedings of the 41st International Conference on Machine Learning , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Proceedings of the 41st International Conference on Machine Learning , pages=

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.407865Z

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 197e7dc6-7e38-4a04-85f7-86e3cece165e · outbound

This paper cites Proceedings of the 2024 conference on empirical methods in natural language processing , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Proceedings of the 2024 conference on empirical methods in natural language processing , pages=

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.480710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cd67aca6-68ae-4a82-ae65-3519d8285285 · outbound

This paper cites an unresolved cited work.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Unresolved cited work

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.503248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c66d6c16-b8bc-4bb5-904c-45c69943f671 · outbound

This paper cites arXiv preprint arXiv:2602.00879 , year=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding arXiv preprint arXiv:2602.00879 , year=

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.506728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 72af2458-dedb-4f58-a9eb-2b890fe3f011 · outbound

This paper cites arXiv preprint arXiv:2602.07265 , year=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding arXiv preprint arXiv:2602.07265 , year=

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.510282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 09e1ad2c-f11c-400f-b4d9-d3694b06e949 · outbound

This paper cites arXiv preprint arXiv:2602.16052 , year=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding arXiv preprint arXiv:2602.16052 , year=

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.514248Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 261d7fbf-fe80-4230-96e7-29c56164caae · outbound

This paper cites Advances in Neural Information Processing Systems , volume=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Advances in Neural Information Processing Systems , volume=

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.386696Z

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 d2e98d84-367e-4c92-bfcd-7f427db58668 · outbound

This paper cites Utility-Driven Speculative Decoding for Mixture-of-Experts.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Utility-Driven Speculative Decoding for Mixture-of-Experts

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.522404Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 05f779c5-1e18-45ee-a7c4-86f9e68ca0bd · outbound

This paper cites arXiv preprint arXiv:2510.10302 , year=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding arXiv preprint arXiv:2510.10302 , year=

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.526228Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feb84e96-8b1e-459c-a140-ca894955dc31 · outbound

This paper cites arXiv preprint arXiv:2511.14102 , year=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding arXiv preprint arXiv:2511.14102 , year=

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.529649Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 297cf30c-4967-4e5e-afe4-89dd6058c12d · outbound

This paper cites Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Less Experts, Faster Decoding: Cost-Aware Speculative Decoding for Mixture-of-Experts

Reference 16

Resolution: metadata mismatch; local_arxiv, observed 2026-08-08T04:28:45.963003Z

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 b8c0daca-02c6-47b7-8a09-66a6e7a13747 · outbound

This paper cites gpt-oss-120b & gpt-oss-20b Model Card.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding gpt-oss-120b & gpt-oss-20b Model Card

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.536387Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a7ac98e-ddeb-42a4-bccd-366a1d57bd7a · outbound

This paper cites Qwen3 Technical Report.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Qwen3 Technical Report

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.539970Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e9ed16b7-e723-4dce-8711-2f377fd87a48 · outbound

This paper cites DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.543636Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 43d2ea65-847b-47c0-a16d-36c36afef197 · outbound

This paper cites Task-Specific Expert Pruning for Sparse Mixture-of-Experts.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Task-Specific Expert Pruning for Sparse Mixture-of-Experts

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.546886Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 1e68fcb3-c105-4fc6-942c-ddbbdd580b73 · outbound

This paper cites Findings of the Association for Computational Linguistics: ACL 2025 , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Findings of the Association for Computational Linguistics: ACL 2025 , pages=

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.375132Z

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 868d3776-de7f-4b0e-8bf8-20bb08ed6e10 · outbound

This paper cites Mixtral of Experts.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Mixtral of Experts

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.555055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7b3357a0-f2c3-47e3-8aeb-1e1099804456 · outbound

This paper cites , author=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding , author=

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.364766Z

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 2bc1aa3c-32b7-4f8f-b306-6683cefe1102 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Evaluating Large Language Models Trained on Code

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.567902Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2d3118eb-73ee-476f-bd0f-39ec16a5406a · outbound

This paper cites Program Synthesis with Large Language Models.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Program Synthesis with Large Language Models

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.622086Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 27e1af3d-e322-4312-98a5-d5b5aa25af59 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Training Verifiers to Solve Math Word Problems

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.667947Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ad90437c-bbd7-467b-8cc1-5ed87c28d249 · outbound

This paper cites International Conference on Learning Representations , volume=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding International Conference on Learning Representations , volume=

Reference 27

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.714220Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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

This paper cites DA-MoE: Towards Dynamic Expert Allocation for Mixture-of-Experts Models.

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.

Observation 17e6196b-3d7a-40cc-9c4d-958ad1f4c081 · outbound

This paper cites arXiv preprint arXiv:2511.02237 , year=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding arXiv preprint arXiv:2511.02237 , year=

Reference 29

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.721255Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation a12c9ff6-734e-46d3-9dff-3e6967b724d1 · outbound

This paper cites Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , pages=

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.256213Z

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 752e7dcd-4143-48e0-b7cc-4de765aeee9c · outbound

This paper cites Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , pages=

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:47.035331Z

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 b59d7a5c-a467-42d3-aaf7-1572bdba0ea2 · outbound

This paper cites International Conference on Learning Representations , volume=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding International Conference on Learning Representations , volume=

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:46.751206Z

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 20c36199-48e3-47df-baa1-3606736b90bb · outbound

This paper cites Proceedings of the 15th European Signal Processing Conference (EUSIPCO) , pages=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Proceedings of the 15th European Signal Processing Conference (EUSIPCO) , pages=

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-08T04:28:46.551897Z

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 abb4f435-3d02-49a2-826e-000c21b0be87 · outbound

This paper cites Advances in neural information processing systems , volume=.

AcceptMoE: Commitment-Weighted Self-Sizing Verifier Expert Sets for Efficient MoE Speculative Decoding Advances in neural information processing systems , volume=

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-08T04:28:45.737861Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.