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Paper Citation Record · LEDGER

Identifying Policy Gradient Subspaces

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2401.06604.

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

pith.paper-citation-record.v1
2401.06604 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 2026-08-19T06:32:44.657259+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T18:50:49.868279Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-08T18:50:50.322446Z

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 7503de68-73e4-4433-8fef-2710cef3574d · inbound

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning cites this paper.

Low-Rank Agent-Specific Adaptation (LoRASA) for Multi-Agent Policy Learning Identifying Policy Gradient Subspaces

Reference 30

Resolution
verified exact
local_arxiv, observed 2026-08-08T18:50:50.327339Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-08-08T18:50:49.868279Z digest=sha256:dd9cd6b34e60dd0d693757b63c571157ee895ed838801fc499f3a55f259f258a

Observation 9f63228f-05f1-4404-95b5-336a40a2c9e9 · inbound

Geometrically Principled Randomized Optimization for Efficient LLM Training cites this paper.

Geometrically Principled Randomized Optimization for Efficient LLM Training Identifying Policy Gradient Subspaces

Reference 28

Resolution
unresolved
no resolver link, observed 2026-08-04T12:51:26.253702Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-04T12:51:26.253702Z digest=sha256:173b10d0ebb96f77275d64abc7ba7e02148200c22e1eca99ac6d9343d26a95c5