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Paper Citation Record · LEDGER

Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2112.06263.

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

pith.paper-citation-record.v1
2112.06263 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 2026-08-07T06:34:17.273281+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-05-12T04:01:42.265205Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T06:41:45.077264Z

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 0afb7762-f67f-44b6-9a09-b215adb3db14 · inbound

ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism cites this paper.

ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-11T21:31:15.729009Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-08T05:15:09.654940Z digest=sha256:2c19fdf8af37b360019f85dbf38e95f3fe6e32687346ddc97c784f4241b609d5

Observation 8f531f66-2b36-4c76-9666-23ebe6328abd · inbound

ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism cites this paper.

ResiHP: Taming LLM Training Failures with Dynamic Hybrid Parallelism Sage: Leveraging ML to Diagnose Unpredictable Performance in Cloud Microservices

Reference 8

Resolution
verified exact
arxiv_id, observed 2026-05-12T06:41:45.094643Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=pdf_text observed=2026-05-12T04:01:42.265205Z digest=sha256:807a79bcdf061facfe9da8842e3fceb697c822f8625e7df602c37be99c25bc38