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Paper Citation Record · LEDGER

Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 3 inbound Pith citation observations for arXiv:2305.13795.

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

pith.paper-citation-record.v1
2305.13795 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 3 of 3 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 3 of 3 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-09T22:47:04.271108Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T13:54:32.236831Z

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 5f052417-ff91-4f72-94c4-07b2ac1afbc6 · inbound

Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations cites this paper.

Scaling Policy Gradient Quality-Diversity with Massive Parallelization via Behavioral Variations Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

Reference 2

Resolution
unresolved
no resolver link, observed 2026-08-09T22:47:04.271108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T22:47:04.271108Z digest=sha256:7aa90ab1c60ddb2a5829efadf4a1e32f358b0c1aa0f84ebb67eb4b4ecf4eef91

Observation 41211386-c2b8-45cf-89c0-3064f6f3bde8 · inbound

Multi-Objective Covariance Matrix Adaptation MAP-Annealing cites this paper.

Multi-Objective Covariance Matrix Adaptation MAP-Annealing Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

Reference 2

Resolution
verified exact
local_arxiv, observed 2026-08-07T13:54:32.285974Z

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=pdf_text observed=2026-08-07T13:54:27.397155Z digest=sha256:024397cebceddb00b81efca4d7cac636f546036560a321b5f95b2cbd6d5c17c9

Observation bb2e82be-4255-4d4c-b409-7cd9924d7fc4 · inbound

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization cites this paper.

GAE: Graph-Augmented Evolution for Scientific Discovery via Reinforcement Optimization Proximal Policy Gradient Arborescence for Quality Diversity Reinforcement Learning

Reference 25

Resolution
unresolved
no resolver link, observed 2026-07-14T14:06:35.756620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T14:06:35.756620Z digest=sha256:ada152da8cc2db6243ba5ced5c47d14e99c4083cb605041075c7e78c273a14e1