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Paper Citation Record · LEDGER

On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2407.05622.

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

pith.paper-citation-record.v1
2407.05622 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T15:34:16.685134Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T15:34:17.012725Z

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 d7219710-d2b4-403d-be77-1e150fc454e7 · inbound

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions cites this paper.

Low-dimensional Functions are Efficiently Learnable under Randomly Biased Distributions On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries

Reference 31

Resolution
verified exact
local_arxiv, observed 2026-08-08T15:34:17.020132Z

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=arxiv_source observed=2026-08-08T15:34:16.685134Z digest=sha256:f6c5226bcd1129073b7beada7b7f718721399f177608f9cb8e42c580e2554b19

Observation 085a2522-f415-4d3f-a47d-a0ea9f0e7fa8 · inbound

The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning cites this paper.

The Multiscale Single-Index Model: A Stylized Model for Hierarchical Feature Learning On the Complexity of Learning Sparse Functions with Statistical and Gradient Queries

Reference 35

Resolution
unresolved
no resolver link, observed 2026-07-12T03:07:54.001891Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T03:07:54.001891Z digest=sha256:3344f7049f4f3e9945f40d85ed27b6390975af4e84c61ef300c48a28555455f4