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Paper Citation Record · LEDGER

TorchBench: Benchmarking PyTorch with High API Surface Coverage

As of 17 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2304.14226.

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

pith.paper-citation-record.v1
2304.14226 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 2026-08-17T06:30:58.91139+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:13:44.042068Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T01:46:26.606302Z

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 65b6f77f-29ea-4fc4-8d59-3d2313534e4f · inbound

High-performance training and inference for deep equivariant interatomic potentials cites this paper.

High-performance training and inference for deep equivariant interatomic potentials TorchBench: Benchmarking PyTorch with High API Surface Coverage

Reference 38

Resolution
unresolved
no resolver link, observed 2026-08-16T11:13:44.042068Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:13:44.042068Z digest=sha256:0621c3c9706a57298702ce1ef3a2f506f659eeec43ddba7a289b1fef5cd5a5d8

Observation 7af15db0-2968-4d52-a9ce-a34cbc5f429f · inbound

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency cites this paper.

MOSAIC: Efficient Mixture-of-Agent Scheduling via Adaptive Aggregation and Inference Concurrency TorchBench: Benchmarking PyTorch with High API Surface Coverage

Reference 102

Resolution
verified exact
arxiv_id, observed 2026-07-02T01:46:26.608213Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=arxiv_source observed=2026-06-28T11:34:24.019560Z digest=sha256:5676cd2536fc270bc3cab34ba74f760ee595ddcc22abdc43341f98e6b0ee1350