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Paper Citation Record · LEDGER

Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 10 inbound Pith citation observations for arXiv:2104.06599.

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

pith.paper-citation-record.v1
2104.06599 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00

measured 10 of 10 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T15:39:21.828057Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-23T18:53:21.119647Z

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 452669a2-536e-4efe-a820-439359932c60 · inbound

QLoRA: Efficient Finetuning of Quantized LLMs cites this paper.

QLoRA: Efficient Finetuning of Quantized LLMs Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 48

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:29:53.623436Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-11T13:29:53.345251Z digest=sha256:fda62e0b7341ea58859c81980fa74c1ddc0b2f7aebdeccb94d24b440d9b48e9a

Observation 5c75246c-567f-48c9-b4b1-7495e7cfa7a8 · inbound

Large Language Models as Optimizers cites this paper.

Large Language Models as Optimizers Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-15T00:04:31.323285Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-15T00:04:31.212102Z digest=sha256:5790e8ef1f06866d0685899fbd2e7425a5994d3564553c691ac9a0761f66ac8d

Observation f292be2d-3fad-462d-9db0-076771137880 · inbound

In Context Learning and Reasoning for Symbolic Regression with Large Language Models cites this paper.

In Context Learning and Reasoning for Symbolic Regression with Large Language Models Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 80

Resolution
verified exact
arxiv_id, observed 2026-05-23T18:53:21.122611Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-23T18:50:40.378720Z digest=sha256:687c559e1226353af0b99072c33bbed93598331db476830905939bd3c2c3833d

Observation beb603c2-2808-4a23-a5fb-969b1cbbac07 · inbound

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications cites this paper.

Evolutionary Computation and Large Language Models: A Survey of Methods, Synergies, and Applications Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-07T15:14:07.374599Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:14:07.374599Z digest=sha256:bf45f47e346619cd5412cfe24c37bd2ef1a18572183f96a59781bcab267dcab7

Observation 97d4131d-4ef8-4d82-b7d7-fac43c7b6fa4 · inbound

PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation cites this paper.

PAID: Pairwise Angular-Invariant Decomposition for Continual Test-Time Adaptation Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 47

Resolution
unresolved
no resolver link, observed 2026-08-07T11:29:54.518680Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T11:29:54.518680Z digest=sha256:84ec0abd85b004a37fd48cba090a1fddbda5ff89716f0b446eac5770df1f46bc

Observation b36101de-1081-4084-83e6-d166b40e41ec · inbound

Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation cites this paper.

Stabilizing Black-Box Prompt Optimization with Textual Regularization and Signal Aggregation Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-06T17:51:03.327250Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:51:03.327250Z digest=sha256:5cc8a25cc66bc82be233f4b04f39a618fb0b94e1c13c3144c46805c29f689447

Observation 0a950b3f-6d06-45f7-b688-516612026fd5 · inbound

An Efficient Evolutionary Algorithm for Few-for-Many Optimization cites this paper.

An Efficient Evolutionary Algorithm for Few-for-Many Optimization Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 32

Resolution
unresolved
no resolver link, observed 2026-08-03T11:31:03.599258Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T11:31:03.599258Z digest=sha256:9cccd7d942138954f0fab36986dad5f2f85ad823c215993cbfb72c1120e1cc91

Observation 4b3f93ef-c1e2-4a45-820d-5094c9f6a78f · inbound

DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection cites this paper.

DetPO: In-Context Learning with Multi-Modal LLMs for Few-Shot Object Detection Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 46

Resolution
unresolved
no resolver link, observed 2026-07-13T19:38:38.452093Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-13T19:38:38.452093Z digest=sha256:d9d5d05960f8c84f2d386959633a4df6ec17c5a91f635c6d136a6012689e4c58

Observation 6c527873-feb9-4a1f-89d4-45d4fea943c7 · inbound

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation cites this paper.

LWGR: Lagrangian-Constrained Personalized World Knowledge for Generative Recommendation Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 34

Resolution
verified exact
arxiv_id, observed 2026-05-21T01:33:56.637445Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-05-21T01:30:03.878197Z digest=sha256:248fa1fd5dae541953906d3144a172b53d8d282695b75d94d7b246d41ee81bbc

Observation edd45e72-dfc8-400b-bc75-dabf921b92fb · inbound

Training-Free Token-Level Steering for LLM Personalized Co-Writing cites this paper.

Training-Free Token-Level Steering for LLM Personalized Co-Writing Learning How to Ask: Querying LMs with Mixtures of Soft Prompts

Reference 13

Resolution
unresolved
no resolver link, observed 2026-08-07T15:39:21.828057Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T15:39:21.828057Z digest=sha256:73ab4b76952ae7ca31eed65f2ad7e6d7db25014bb3171c39e79f7eecb22f789b