Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
$\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
As of 23 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2406.06887.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-16T11:22:02.402508Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:07:17.680258Z
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 91d1c57f-fb88-42ee-ae9b-30ecbdb44db8 · inbound
DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs cites this paper.
DSTC: Direct Preference Learning with Only Self-Generated Tests and Code to Improve Code LMs $\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
Reference 43
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T17:05:15.213296Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 9cc610f7-a633-4152-8530-f563083ae995 · inbound
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback cites this paper.
Insights from Verification: Training a Verilog Generation LLM with Reinforcement Learning with Testbench Feedback $\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
Reference 37
- Resolution
- unresolved
- no resolver link, observed 2026-08-16T11:22:02.402508Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation d4209d49-7021-4235-9d45-f70ab2455b83 · inbound
SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation cites this paper.
SkelDPO: A Skeleton-Guided Direct Preference Optimization Framework for Efficient Code Generation $\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
Reference 53
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:07:17.681641Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.
Observation 7a369617-e439-4089-a421-34eb7f6c25b3 · inbound
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning cites this paper.
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning $\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-01T15:29:55.780359Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 8ffb555d-9777-4326-b608-d62ff0e375d5 · inbound
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning cites this paper.
DHRCL:Training Code LLMs with Dense Hierarchical Rewards and Curriculum Learning $\textbf{PLUM}$: Improving Code LMs with Execution-Guided On-Policy Preference Learning Driven By Synthetic Test Cases
Reference 18
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T04:27:43.008343Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.