Pith. sign in

Paper Citation Record · LEDGER

Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2406.08115.

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

pith.paper-citation-record.v1
2406.08115 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 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T18:12:33.240343Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-04T08:39:42.055567Z

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 c78a9129-8bb1-4f4a-b0c1-039e2171c0d9 · inbound

Secure Resource Allocation via Constrained Deep Reinforcement Learning cites this paper.

Secure Resource Allocation via Constrained Deep Reinforcement Learning Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-10T18:12:33.240343Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T18:12:33.240343Z digest=sha256:15861b971b0339dddd4152d9bc6b550b571b08b450ac7e5163b43066b9e5375a

Observation a2c0f376-dda4-4713-b736-60ee8643faad · inbound

StickyInvoc: Rethinking Task Models for High-throughput Workflows in the LLM Era cites this paper.

StickyInvoc: Rethinking Task Models for High-throughput Workflows in the LLM Era Resource Allocation and Workload Scheduling for Large-Scale Distributed Deep Learning: A Survey

Reference 32

Resolution
verified exact
arxiv_id, observed 2026-07-04T08:39:42.057048Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=pdf_text observed=2026-06-26T11:16:53.967397Z digest=sha256:e6f5917d835fb579bf1fc31c7044fd6dc59d24851b430541cceda8ea3b73e1c4