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Paper Citation Record · LEDGER

Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

As of 15 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2403.08477.

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

pith.paper-citation-record.v1
2403.08477 v3

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T19:58:58.622270Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T00:56:26.631642Z

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 6a9a48b6-57a0-49fd-ba10-7b75351bb3a7 · inbound

Learning Model Successors cites this paper.

Learning Model Successors Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-09T19:58:58.622270Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T19:58:58.622270Z digest=sha256:fed7ccb1f18cc6417f580bece1946a54a48bbc5a749d45a42f23784ea30ca5cf

Observation 149c82aa-8745-4c92-8d97-b58920982af5 · inbound

Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts cites this paper.

Automatic Expert Discovery in LLM Upcycling via Sparse Interpolated Mixture-of-Experts Unleashing the Power of Meta-tuning for Few-shot Generalization Through Sparse Interpolated Experts

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-08-07T00:56:26.637318Z

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=arxiv_source observed=2026-08-07T00:56:26.165504Z digest=sha256:b2b81de67df524c2b7758d020a83f342555fcd665dc89857a72b0d8aa6e8c5eb