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Paper Citation Record · LEDGER

Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2504.09685.

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

pith.paper-citation-record.v1
2504.09685 v1

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 2026-08-18T06:34:40.430872+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T21:11:55.393434Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T22:03:10.019297Z

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 e68ac95d-4f0d-4b59-89fb-7b68e777aa81 · inbound

SEAL: Searching Expandable Architectures for Incremental Learning cites this paper.

SEAL: Searching Expandable Architectures for Incremental Learning Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?

Reference 46

Resolution
unresolved
no resolver link, observed 2026-08-15T21:11:55.393434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-15T21:11:55.393434Z digest=sha256:5bec9eb38cc2d0eb0eadfd34af2c30f30fb34c3dbaf968e9121f0e13cbbe30b6

Observation abc2b83a-0e26-4210-9c0f-511043ce0a14 · inbound

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning cites this paper.

SHeRL-FL: When Representation Learning Meets Split Learning in Hierarchical Federated Learning Can LLMs Revolutionize the Design of Explainable and Efficient TinyML Models?

Reference 164

Resolution
verified exact
local_arxiv, observed 2026-08-05T22:03:10.022044Z

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.

source=pdf_text observed=2026-08-05T22:03:09.914167Z digest=sha256:1925c151baea10856dd5d1a10d6c7f68e1145f165e05672fe51e64ac59b1bd1d