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Paper Citation Record · LEDGER

ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2010.06398.

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

pith.paper-citation-record.v1
2010.06398 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 2026-08-10T06:31:04.303077+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T21:00:31.235746Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T18:43:12.980090Z

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 3b576a71-426c-4d9e-9676-c0349208f3e5 · inbound

Advancing Differentiable Economics: A Neural Network Framework for Revenue-Maximizing Combinatorial Auction Mechanisms cites this paper.

Advancing Differentiable Economics: A Neural Network Framework for Revenue-Maximizing Combinatorial Auction Mechanisms ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning

Reference 17

Resolution
unresolved
no resolver link, observed 2026-08-09T21:00:31.235746Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-09T21:00:31.235746Z digest=sha256:b1767449179a9dd76202f0e0ca48a3d3e9fa73c481640a5fe5546fb6041291ed

Observation f6443c50-e05c-42f1-a566-582767953592 · inbound

Hybrid Advertising in the Sponsored Search cites this paper.

Hybrid Advertising in the Sponsored Search ProportionNet: Balancing Fairness and Revenue for Auction Design with Deep Learning

Reference 16

Resolution
verified exact
local_arxiv, observed 2026-08-06T18:43:13.080292Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-08-06T18:43:11.765952Z digest=sha256:30694a52290d9f4b3c9592fc258a330e7c52ab636053ce9cb3dd9cd0e2514c7e