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Paper Citation Record · LEDGER

One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2407.00256.

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

pith.paper-citation-record.v1
2407.00256 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-15T06:32:42.880941+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-11T05:33:29.829084Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T11:39:47.093974Z

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 d9a24e63-6c0f-460e-819d-978f7089815d · inbound

BrainMAP: Learning Multiple Activation Pathways in Brain Networks cites this paper.

BrainMAP: Learning Multiple Activation Pathways in Brain Networks One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-11T05:33:29.829084Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T05:33:29.829084Z digest=sha256:4de5eb3f5bdc9f9bfe8d460c46b4396e3b42eb3b646631a9f6ce54fd61ee1cd2

Observation c959e1d7-5a7d-41b8-87d4-af5c74723bc5 · inbound

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models cites this paper.

TAME: Test-Time Adversarial Prompt Tuning via Mixture-of-Experts for Vision-Language Models One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts

Reference 52

Resolution
verified exact
arxiv_id, observed 2026-05-20T14:23:21.502498Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-05-20T14:20:49.278545Z digest=sha256:05758bcf4082be7d6adf6c8b67faedfddc1b711964a06a289421a86cd54024a6

Observation 52a4c69f-903f-46f5-ac68-93efdf39484e · inbound

Towards Spec Learning: Inference-Time Alignment from Preference Pairs cites this paper.

Towards Spec Learning: Inference-Time Alignment from Preference Pairs One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts

Reference 104

Resolution
verified exact
arxiv_id, observed 2026-07-04T11:39:47.096263Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-26T07:49:36.816100Z digest=sha256:4d8bc1b541a81029f0ab9868d05076247de92377bc06225431d692b17884d33c

Observation 0ace0ac1-54b9-4ba8-98e6-b38d501d9fc4 · inbound

Towards Spec Learning: Inference-Time Alignment from Preference Pairs cites this paper.

Towards Spec Learning: Inference-Time Alignment from Preference Pairs One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts

Reference 104

Resolution
verified exact
arxiv_id, observed 2026-06-30T12:04:39.389438Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-06-30T10:17:33.176525Z digest=sha256:728d3b38f250dfbde4c61a24cb2b5b36700f9f1a9275ef38af87b3e7c48bbe9b

Observation a84c92ac-245e-47b8-b837-3d6346d65aad · inbound

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification cites this paper.

BT-APE: A Computationally Light Backtracking Approach to Automatic Prompt Engineering for Requirements Classification One Prompt is not Enough: Automated Construction of a Mixture-of-Expert Prompts

Reference 51

Resolution
verified exact
arxiv_id, observed 2026-07-02T09:16:48.651398Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.

source=pdf_text observed=2026-07-02T09:12:59.039529Z digest=sha256:db60486ac8def4f9b8c15a8f1ec4dee2036e0fc95acfc3791176bffc32ba193a