Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2412.17264.
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 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-06T14:51:53.826996Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-07-02T19:07:17.682664Z
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 a356e882-72bc-4a2a-82e4-b9caedb35f36 · inbound
Investigating Training Data Detection in AI Coders cites this paper.
Investigating Training Data Detection in AI Coders ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
Reference 24
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T14:51:53.826996Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 7bf63cef-2a6b-462f-baf3-5fe81a863774 · inbound
VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning cites this paper.
VERIRL: Boosting the LLM-based Verilog Code Generation via Reinforcement Learning ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
Reference 22
- Resolution
- unresolved
- no resolver link, observed 2026-08-05T16:28:23.667324Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 709a1600-939b-4989-b0f9-c5d26c31628e · inbound
Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation cites this paper.
Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T18:57:17.243220Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation e15706e4-20e9-4f6e-9df0-52c16cbfeb24 · 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 ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
Reference 49
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T19:07:17.684063Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 5e7f04b9-2915-4cdd-93e8-287269185b50 · inbound
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents? cites this paper.
Are Performance-Optimization Benchmarks Reliably Measuring Coding Agents? ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models
Reference 41
- Resolution
- verified exact
- arxiv_id, observed 2026-07-02T08:06:47.680799Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.