Paper Citation Record · LEDGER

R1-RE: Cross-Domain Relation Extraction with RLVR

As of 7 August 2026, this Paper Citation Record lists 14 of 14 outbound references and 2 inbound Pith citation observations for arXiv:2507.04642.

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

pith.paper-citation-record.v1
2507.04642 v2

Coverage vector

measured 14 of 14 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-06T19:49:32.579387Z

measured 16 of 16 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 2026-08-04T10:36:23.222989Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-10T05:36:01.989610Z

Reference resolution

14 of 14 outbound references displayed

verified exact0
verified fuzzy7
unresolved7
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 3fb5e59e-8286-4782-b897-6f8b96f4ca6b · outbound

This paper cites Use X to treat Y.

R1-RE: Cross-Domain Relation Extraction with RLVR Use X to treat Y

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:34.154641Z

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.

Observation 661acfe0-3322-4041-9551-9d5c72af1e5e · outbound

This paper cites X is hyponym-of Y.

R1-RE: Cross-Domain Relation Extraction with RLVR X is hyponym-of Y

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:33.903165Z

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.

Observation 6f4d76df-47a3-4be5-bcec-b59cdfc0ae8a · outbound

This paper cites The sentence mentions that these antipsychotics are used specifically in the context of women with schizophrenia, implying a therapeutic use.

R1-RE: Cross-Domain Relation Extraction with RLVR The sentence mentions that these antipsychotics are used specifically in the context of women with schizophrenia, implying a therapeutic use

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:33.126365Z

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.

Observation 70a2099a-d2df-4908-835b-3d3503bdf940 · outbound

This paper cites being treated with,.

R1-RE: Cross-Domain Relation Extraction with RLVR being treated with,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:32.884418Z

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.

Observation 9846a3ec-a82b-4d4c-87fc-79ea6cbcd69b · outbound

This paper cites InProceedings of the conference.

R1-RE: Cross-Domain Relation Extraction with RLVR InProceedings of the conference

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:34.481507Z

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.

Observation 6ba54397-fe78-4232-b59a-9646f8f79f0a · outbound

This paper cites GPT-RE: In-context Learning for Relation Extraction using Large Language Models.

R1-RE: Cross-Domain Relation Extraction with RLVR GPT-RE: In-context Learning for Relation Extraction using Large Language Models

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:31.531164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0d99eb02-d1ea-4f4a-8389-9b1f984ff5be · outbound

This paper cites How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?.

R1-RE: Cross-Domain Relation Extraction with RLVR How to Unleash the Power of Large Language Models for Few-shot Relation Extraction?

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:31.671789Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48da0996-67c6-4b7c-b833-e7241dc2901c · outbound

This paper cites DAPO: An Open-Source LLM Reinforcement Learning System at Scale.

R1-RE: Cross-Domain Relation Extraction with RLVR DAPO: An Open-Source LLM Reinforcement Learning System at Scale

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:31.778885Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 08449154-a98a-4b5b-8ee3-e4dbcf9e8b6e · outbound

This paper cites - <e2>prolactin-increasing antipsychotics</e2> is a type of medication or treatment.

R1-RE: Cross-Domain Relation Extraction with RLVR - <e2>prolactin-increasing antipsychotics</e2> is a type of medication or treatment

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:33.695688Z

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.

Observation 67e21e60-ac4e-45af-b105-8b7928559504 · outbound

This paper cites - We need to determine if this relation is that schizophrenia leads to or is treated by these antipsychotics, or if it’s some other relation.

R1-RE: Cross-Domain Relation Extraction with RLVR - We need to determine if this relation is that schizophrenia leads to or is treated by these antipsychotics, or if it’s some other relation

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-06T19:49:33.428684Z

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.

Observation 01f709b3-d1cf-47c8-898b-b4623a334699 · outbound

This paper cites DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models.

R1-RE: Cross-Domain Relation Extraction with RLVR DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models

Reference 1999

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:31.256080Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6d87fd08-508f-4534-86a1-2d0aad383deb · outbound

This paper cites CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation.

R1-RE: Cross-Domain Relation Extraction with RLVR CORE: A Few-Shot Company Relation Classification Dataset for Robust Domain Adaptation

Reference 2023

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:30.917128Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3734e733-76b9-4cf0-a6ca-b28babee851d · outbound

This paper cites A Survey on LLM-as-a-Judge.

R1-RE: Cross-Domain Relation Extraction with RLVR A Survey on LLM-as-a-Judge

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:31.137537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 13eb2014-cd60-4b59-91e4-222656889e8e · outbound

This paper cites Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models.

R1-RE: Cross-Domain Relation Extraction with RLVR Breach in the Shield: Unveiling the Vulnerabilities of Large Language Models

Reference 2025

Resolution: unresolved; no resolver link, observed 2026-08-06T19:49:31.053849Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation 754d3dcd-93db-4b4c-9628-e35e61c1e456 · inbound

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics cites this paper.

StatEval: A Comprehensive Benchmark for Large Language Models in Statistics R1-RE: Cross-Domain Relation Extraction with RLVR

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-04T10:36:23.222989Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d41f4700-3287-4645-9187-33226ff86958 · inbound

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data cites this paper.

Too Correct to Learn: Reinforcement Learning on Saturated Reasoning Data R1-RE: Cross-Domain Relation Extraction with RLVR

Reference 42

Resolution: verified exact; arxiv_id, observed 2026-05-10T05:36:01.990762Z

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.