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Paper Citation Record · LEDGER

PPT: Pre-trained Prompt Tuning for Few-shot Learning

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 9 inbound Pith citation observations for arXiv:2109.04332.

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

pith.paper-citation-record.v1
2109.04332 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 9 of 9 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 9 of 9 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:48:02.268463Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T13:29:51.163212Z

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 754c65ba-4863-4b82-ba55-02b056f8a380 · inbound

Towards Expert-Level Medical Question Answering with Large Language Models cites this paper.

Towards Expert-Level Medical Question Answering with Large Language Models PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-05-24T04:32:33.563953Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-24T04:32:33.271634Z digest=sha256:6c5063d4516c60896eae26038678cf8dd73097801f9f6fef86f2bd51cfacbc93

Observation 5889d7bf-c960-4931-a6f9-32273fad0234 · inbound

Towards an AI co-scientist cites this paper.

Towards an AI co-scientist PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 156

Resolution
verified exact
arxiv_id, observed 2026-05-11T13:02:45.012569Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-05-11T13:02:43.571234Z digest=sha256:591cf224aa00a0355e022e56793951de11c065e4ac56c58197f1773eddbfb7c2

Observation de659a86-02eb-4d45-882f-efbe05a8f1c7 · inbound

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation cites this paper.

ReqBrain: Task-Specific Instruction Tuning of LLMs for AI-Assisted Requirements Generation PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T14:48:02.268463Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T14:48:02.268463Z digest=sha256:a1bc2c4e63d492007609e4914f6378d678bc90de720cc21cb72904ee6ff1bc74

Observation 5ba7897d-c4d9-4803-a810-f6d225f73e52 · inbound

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering cites this paper.

Fast or Slow? Integrating Fast Intuition and Deliberate Thinking for Enhancing Visual Question Answering PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 11

Resolution
unresolved
no resolver link, observed 2026-08-07T12:02:10.520637Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T12:02:10.520637Z digest=sha256:82be4d1a3937bf600ff9fb98eaeb3ba8be97871b13456855a302240850a665a1

Observation 06241424-df58-4ad2-b92c-48ca5ff65bd8 · inbound

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives cites this paper.

Multi-level Value Alignment in Agentic AI Systems: Survey and Perspectives PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 103

Resolution
unresolved
no resolver link, observed 2026-08-07T04:46:04.414376Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T04:46:04.414376Z digest=sha256:1fa7827b9e4dcd2547ecb1459ddd5fdc9e5556135225036fe2ebc396792ee9e7

Observation 298261b1-61cc-4c71-a3eb-a464b76f3c39 · inbound

Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation cites this paper.

Mettle: Meta-Token Learning for Memory-Efficient Audio-Visual Adaptation PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-06T21:52:05.682604Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T21:52:05.682604Z digest=sha256:9100c825dff3c214fef89c6a1a752677ab3e843238506b57ac51bbd3efb63ddc

Observation ded21617-762f-4eab-9429-142acbf17f72 · inbound

GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning cites this paper.

GRID: Scaling Task-Agnostic Inference in Continual Prompt Tuning PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 12

Resolution
unresolved
no resolver link, observed 2026-08-06T15:55:28.942688Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-06T15:55:28.942688Z digest=sha256:826f7b2f354c7b8428af612fb0d37d78b0337cc3e1412086eacf6ccf8ecab4e5

Observation 8c7d8920-f901-4044-90c3-c13f23460a4d · inbound

Graph Topology Information Enhanced Heterogeneous Graph Representation Learning cites this paper.

Graph Topology Information Enhanced Heterogeneous Graph Representation Learning PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 12

Resolution
verified exact
arxiv_id, observed 2026-05-10T22:55:51.308913Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-10T19:27:45.961277Z digest=sha256:cf344c298bbd6312ca8e29fde4b2249cdc5d4cb59e056ed8472e00780ffee94c

Observation 1ed903a1-754a-4d13-b64f-95c874d4b702 · inbound

Structure Before Collapse: Transient semantic geometry in next-token prediction cites this paper.

Structure Before Collapse: Transient semantic geometry in next-token prediction PPT: Pre-trained Prompt Tuning for Few-shot Learning

Reference 177

Resolution
verified exact
arxiv_id, observed 2026-07-04T13:29:51.164630Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-26T05:14:07.208255Z digest=sha256:3335ab48408d02fe1f9c247823a1aa9af13c5ff16a0cef727162593999ecee7d