Paper Citation Record · LEDGER

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper

As of 18 August 2026, this Paper Citation Record lists 13 of 13 outbound references and 0 inbound Pith citation observations for arXiv:2509.00996.

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

pith.paper-citation-record.v1
2509.00996 v2

Coverage vector

measured 13 of 13 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-05T13:07:16.719365Z

measured 13 of 13 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

13 of 13 outbound references displayed

verified exact1
verified fuzzy5
unresolved6
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation bef4a97f-0dca-45b6-929f-03b948508f58 · outbound

This paper cites an unresolved cited work.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:18.160144Z

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 24291a6c-b628-497a-84d7-432a002c3271 · outbound

This paper cites an unresolved cited work.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:18.113996Z

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 13a9f20e-a66c-4ba9-8b5a-d3caf31cb03a · outbound

This paper cites These phenomena motivate our method.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper These phenomena motivate our method

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:17.830629Z

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 b719fa23-1f78-4aab-a927-17547542492d · outbound

This paper cites Mixture of Expert Prompt Tuning as a Manifold Mapper.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Mixture of Expert Prompt Tuning as a Manifold Mapper

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:18.258000Z

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 3b882d89-9322-40c0-9843-fa52f0353db0 · outbound

This paper cites an unresolved cited work.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:17.567768Z

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 e2321bcc-4cb0-41ad-a110-ce5846528572 · outbound

This paper cites common knowledge.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper common knowledge

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:18.185458Z

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 37d6c7a0-cbde-4473-946d-16627c976d2c · outbound

This paper cites PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper PT-MoE: An Efficient Finetuning Framework for Integrating Mixture-of-Experts into Prompt Tuning

Reference 2012

Resolution: verified exact; local_arxiv, observed 2026-08-05T13:07:16.990337Z

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 3a4385f7-7aef-45a8-b6c9-e7c5d855b7dc · outbound

This paper cites LoRA+: Efficient Low Rank Adaptation of Large Models.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper LoRA+: Efficient Low Rank Adaptation of Large Models

Reference 2014

Resolution: unresolved; no resolver link, observed 2026-08-05T13:07:15.588822Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9bac447c-7fb6-4862-bcbe-dd0e9367417d · outbound

This paper cites SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper SortedNet: A Scalable and Generalized Framework for Training Modular Deep Neural Networks

Reference 2019

Resolution: unresolved; no resolver link, observed 2026-08-05T13:07:16.051235Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d7b6bf48-cf34-4aa6-994c-36a39c857d7b · outbound

This paper cites Matthew E Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Matthew E Peters, Mark Neumann, Luke Zettlemoyer, and Wen-tau Yih

Reference 2020

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:18.294413Z

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 3088efdf-4418-4f19-8b17-c5c77a268f5a · outbound

This paper cites an unresolved cited work.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Unresolved cited work

Reference 2022

Resolution: unresolved; raw_fallback, observed 2026-08-05T13:07:18.335092Z

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 180cf9db-c78b-4872-8830-fad77d15cc1c · outbound

This paper cites Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper Understanding Neural Coding on Latent Manifolds by Sharing Features and Dividing Ensembles

Reference 2023

Resolution: metadata mismatch; local_arxiv, observed 2026-08-05T13:07:17.401770Z

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 6c376ea3-0bc0-4367-8eb5-8186125bdc95 · outbound

This paper cites XPrompt (Ma et al., 2022) enhances efficiency by prun- ing less informative token-level and piece-level prompts.

MEPT: Mixture of Expert Prompt Tuning as a Manifold Mapper XPrompt (Ma et al., 2022) enhances efficiency by prun- ing less informative token-level and piece-level prompts

Reference 2025

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T13:07:18.231437Z

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.

Pith citing papers

No inbound Pith citation observations are available.