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Paper Citation Record · LEDGER

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

As of 5 August 2026, this Paper Citation Record lists 6 of 6 outbound references and 4 inbound Pith citation observations for arXiv:2604.10547.

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

pith.paper-citation-record.v1
2604.10547 v2

Coverage vector

measured 6 of 6 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-05-14T21:29:37.074038Z

measured 10 of 10 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-05T06:32:48.257954+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-01T01:15:07.163699Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-07-01T13:25:45.976376Z

Reference resolution

6 of 6 outbound references displayed

  • verified exact1
  • verified fuzzy1
  • unresolved3
  • parse uncertain0
  • malformed identifier1
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 57176002-f145-48fb-a9c0-c79379df2fa7 · outbound

This paper cites Training Verifiers to Solve Math Word Problems.

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? Training Verifiers to Solve Math Word Problems

Reference 1

Resolution
verified exact
local_arxiv, observed 2026-05-14T21:32:59.792226Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:29:37.074038Z digest=sha256:bb007939ea343f6bd62cefcb187f5c14d0a1a799418103db55148bb67dd6a98b

Observation a6ab0289-1ee5-4b57-9ed0-4734c2eee54b · outbound

This paper cites an unresolved cited work.

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? Unresolved cited work

Reference 2

Resolution
unresolved
raw_fallback, observed 2026-05-14T23:39:36.493523Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:29:37.074038Z digest=sha256:30c898a006072b073ff21d5852af0caf4968d5b36c9566a94a6f9f4cb39db19f

Observation 4999ad64-d883-46bc-8167-f9e79a492354 · outbound

This paper cites an unresolved cited work.

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? Unresolved cited work

Reference 3

Resolution
unresolved
raw_fallback, observed 2026-05-14T23:39:36.482755Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:29:37.074038Z digest=sha256:909e8e440a68692c4afbcd8f98fc0f156a336f44c0a8cd94d82b5107bf8a3d87

Observation ab775dc2-c5d4-4ea0-89fe-5938d34c14dc · outbound

This paper cites an unresolved cited work.

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? Unresolved cited work

Reference 4

Resolution
unresolved
raw_fallback, observed 2026-05-14T23:39:36.490165Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:29:37.074038Z digest=sha256:436a740a11e42110a9572194e127b3bab592afe13ecfd30137578f1d0a984e6d

Observation 91394683-1dc1-40e3-a6c0-e380b9129474 · outbound

This paper cites model_path.

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? model_path

Reference 5

Resolution
verified fuzzy
raw_fallback, observed 2026-05-14T23:39:36.497821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:29:37.074038Z digest=sha256:43b7ce90cc22439b6fce85fc88d99f5cc0e01e9af4b7bc49087a35904b92daae

Observation 1e0feeb1-2183-46ae-beaa-39037cbcaa51 · outbound

This paper cites iteration.

Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training? iteration

Reference 6

Resolution
malformed identifier
raw_fallback, observed 2026-05-14T23:39:36.486687Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-14T21:29:37.074038Z digest=sha256:5643d56d374d82ba95e1cf614070e6288d63c84cf02240e94c0b27ebeb8ddff3

Pith citing papers

Observation 4d433d91-f58d-40a1-8fea-ac20547a348c · inbound

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI cites this paper.

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

Reference 17

Resolution
verified exact
local_arxiv, observed 2026-05-12T08:21:25.053639Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-05-12T01:13:35.990078Z digest=sha256:9e1691a7f78fcde2d95c17ac0858d5b4cfbabe41992ae5988fe5f32e2f9fa999

Observation 2a815380-0379-4115-b838-aa5f63ce8d63 · inbound

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI cites this paper.

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

Reference 18

Resolution
verified exact
local_arxiv, observed 2026-07-01T13:25:45.977773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-05T06:32:48.257954+00:00.

source=pdf_text observed=2026-06-30T23:12:57.154537Z digest=sha256:d881e776de5901a1b3ce108b3c1f28ddd966d6f5e475b4115ed4da42096b7ad3

Observation e06c9802-3f74-478f-a0fd-16537fdf5aea · inbound

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI cites this paper.

MLS-Bench: A Holistic and Rigorous Assessment of AI Systems on Building Better AI Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

Reference 18

Resolution
unresolved
no resolver link, observed 2026-07-12T17:14:49.310598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-07-12T17:14:49.310598Z digest=sha256:e54c28f51240370277d5fa03aaa6023f91bb4569eb2a5b425efd7d9fba2cf62f

Observation 1d08a554-8813-483a-b451-d1484e5f8cd2 · inbound

RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement cites this paper.

RSIBench-Data: Benchmarking Data-Centric Research for Recursive Self-Improvement Agent^2 RL-Bench: Can LLM Agents Engineer Agentic RL Post-Training?

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-01T01:15:07.163699Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-01T01:15:07.163699Z digest=sha256:04c690a1fc20c717d93a7ae01e3b7c6ac2bfc287ccfaaee1dcb0b4f3d39d6124