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Paper Citation Record · LEDGER

G-Core: A Simple, Scalable and Balanced RLHF Trainer

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:2507.22789.

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

pith.paper-citation-record.v1
2507.22789 v2

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 2026-08-06T06:34:29.942622+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-03T18:47:46.719344Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-03T18:48:49.523099Z

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 1e57fe2f-f729-4ea0-8c46-e4f89cb1fdbe · inbound

Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents cites this paper.

Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents G-Core: A Simple, Scalable and Balanced RLHF Trainer

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-02T06:06:40.605523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-07-02T05:58:39.107614Z digest=sha256:7358190d8e5521ae0d3c5b80c14ed0f706b2ea675dcaeb2ee09e28cb9f8bc1dc

Observation 9487bfb8-2fb1-45b3-a0a8-28adfd9e3a54 · inbound

Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents cites this paper.

Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents G-Core: A Simple, Scalable and Balanced RLHF Trainer

Reference 21

Resolution
verified exact
arxiv_id, observed 2026-07-03T18:48:49.524813Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-06T06:34:29.942622+00:00.

source=pdf_text observed=2026-07-03T18:47:46.719344Z digest=sha256:0840e11306d4b9d754e35406437ffd1bc1a6943bc196daa0a82d9e68a163da48