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Paper Citation Record · LEDGER

Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2404.04566.

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

pith.paper-citation-record.v1
2404.04566 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 5 of 5 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-21T06:32:19.484+00:00

measured 5 of 5 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T20:26:06.199598Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-06T11:27:32.402928Z

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 063262c0-fa03-4925-a36e-e9b23b2bec0b · inbound

Less is More: Towards Green Code Large Language Models via Unified Structural Pruning cites this paper.

Less is More: Towards Green Code Large Language Models via Unified Structural Pruning Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

Reference 53

Resolution
unresolved
no resolver link, observed 2026-08-11T11:02:15.457585Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T11:02:15.457585Z digest=sha256:22938b91624540d23dfa4586d82816ef99f554c37e2e0f2cf2033f04ec664d47

Observation 2001d7d5-6d55-4490-a1c7-582b44a7afcf · inbound

ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models cites this paper.

ACECode: A Reinforcement Learning Framework for Aligning Code Efficiency and Correctness in Code Language Models Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

Reference 33

Resolution
unresolved
no resolver link, observed 2026-08-11T05:44:25.343620Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T05:44:25.343620Z digest=sha256:1b4a44f93e3f85451d6a17c2ad926ab925870f091a3d2658bdc12ab9a9738c75

Observation cfe3901b-84cf-4a17-b780-865ad683f929 · inbound

EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code cites this paper.

EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

Reference 61

Resolution
unresolved
no resolver link, observed 2026-08-15T20:26:06.199598Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T20:26:06.199598Z digest=sha256:2edb02582dc4157500e5738edaf29fbc3b0e28ec56340f1874b1c450026690e3

Observation 564cc66c-78cb-4f7c-9a6d-36d52c42892d · inbound

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization cites this paper.

Afterburner: Reinforcement Learning Facilitates Self-Improving Code Efficiency Optimization Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

Reference 49

Resolution
unresolved
no resolver link, observed 2026-08-07T12:50:27.702240Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T12:50:27.702240Z digest=sha256:2cc6c30a1f1dfeb4d2f1fcc1baf752c6768553ca8636c27189a689346c423452

Observation 1eeb5ba7-f677-4287-ac08-19fb409ead02 · inbound

Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimization cites this paper.

Resource-Efficient Automatic Software Vulnerability Assessment via Knowledge Distillation and Particle Swarm Optimization Efficient and Green Large Language Models for Software Engineering: Literature Review, Vision, and the Road Ahead

Reference 12

Resolution
verified exact
local_arxiv, observed 2026-08-06T11:27:32.405447Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-21T06:32:19.484+00:00.

source=pdf_text observed=2026-08-06T11:27:32.123884Z digest=sha256:43c8e3eb435eca16eec26c951ad2d14748c87377945fcd652f2ea29df11113d5