Paper Citation Record · LEDGER

Augmenting the Generality and Performance of Large Language Models for Software Engineering

As of 14 August 2026, this Paper Citation Record lists 30 of 30 outbound references and 1 inbound Pith citation observation for arXiv:2506.11548.

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

pith.paper-citation-record.v1
2506.11548 v1

Coverage vector

measured 30 of 30 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:06:07.993541Z

measured 31 of 31 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-14T06:32:32.682623+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T04:06:07.993541Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-07T04:06:08.116380Z

Reference resolution

30 of 30 outbound references displayed

verified exact1
verified fuzzy17
unresolved11
parse uncertain0
malformed identifier0
metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 625d44a6-8e80-4295-9f76-d12c51fcfc9a · outbound

This paper cites At- tention is all you need,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering At- tention is all you need,

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:11.202369Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 8e325fb9-9cfe-486b-bcf4-42914044d86d · outbound

This paper cites BERT: P re- training of deep bidirectional transformers for language u nderstanding,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering BERT: P re- training of deep bidirectional transformers for language u nderstanding,

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:11.032612Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c53fbdbb-26c8-49c9-bf50-a4313ddcc05b · outbound

This paper cites Lan- guage models are few-shot learners,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Lan- guage models are few-shot learners,

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.875372Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 96033e67-f460-4229-9ef1-1711ca1ff97c · outbound

This paper cites Exploring the limits of transfer learning with a uniﬁed text-to-text t ransformer,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Exploring the limits of transfer learning with a uniﬁed text-to-text t ransformer,

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.692821Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation ede19827-3ccc-4dab-8e07-e39d24ce0a45 · outbound

This paper cites The world’s most widely adopted ai developer to ol,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering The world’s most widely adopted ai developer to ol,

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.572196Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation e8accbde-e92b-4876-b31e-7e1d6c21cf3f · outbound

This paper cites An applied ai lab building end-to-end software ag ents,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering An applied ai lab building end-to-end software ag ents,

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.475024Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation d06a3449-300c-456c-b6c1-0fe2a652341e · outbound

This paper cites Scaling Laws for Neural Language Models.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Scaling Laws for Neural Language Models

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:05.819274Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6556afdc-46c6-4bfd-8e8f-d993a8454776 · outbound

This paper cites Training compute-optimal large language models,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Training compute-optimal large language models,

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.317760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 7c10b659-ff13-45d7-a47c-76bd0d65095a · outbound

This paper cites Evaluating large language models trained on code,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Evaluating large language models trained on code,

Reference 9

Resolution: verified exact; arxiv_id_nonexistent, observed 2026-08-07T04:06:08.795535Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 466d18bb-34b6-4086-bc96-3e7c61db432f · outbound

This paper cites Program Synthesis with Large Language Models.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Program Synthesis with Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:06.210818Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9a7772a2-acc3-4cf6-b5de-968d6f0d067c · outbound

This paper cites Is your code gene rated by chatGPT really correct? rigorous evaluation of large langu age models for code generation,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Is your code gene rated by chatGPT really correct? rigorous evaluation of large langu age models for code generation,

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.192527Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 89e0bd22-0559-4a9e-91a6-3db6319d65e2 · outbound

This paper cites SWE-bench: Can language models resolve real-world github issues?.

Augmenting the Generality and Performance of Large Language Models for Software Engineering SWE-bench: Can language models resolve real-world github issues?

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:10.096764Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5a6ed35a-4553-4f53-a659-76f2ebaf4d85 · outbound

This paper cites Large Language Models for Software Engineering: A Systematic Literature Review.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Large Language Models for Software Engineering: A Systematic Literature Review

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:06.446728Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 70da6515-87b9-43a4-8b75-e567123e3668 · outbound

This paper cites A Survey on Large Language Models for Software Engineering.

Augmenting the Generality and Performance of Large Language Models for Software Engineering A Survey on Large Language Models for Software Engineering

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:06.535514Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4ef9d7ee-3324-45da-afaa-7028a5c1cb9e · outbound

This paper cites Large language models for software engineering: Survey an d open problems,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Large language models for software engineering: Survey an d open problems,

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.964958Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 358e7269-12ff-4026-8184-d759397aff5a · outbound

This paper cites A revision of bloom’s taxonomy: An ove rview,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering A revision of bloom’s taxonomy: An ove rview,

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.808674Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation a387e5b3-eb37-4309-84d9-d452b8ec5363 · outbound

This paper cites Position: Levels of AGI for operationalizing progress on t he path to AGI,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Position: Levels of AGI for operationalizing progress on t he path to AGI,

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.717100Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation c80b2edf-03fb-4f22-822c-ab5b715c993d · outbound

This paper cites Suleyman and M.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Suleyman and M

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.594750Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 63baa2b0-5620-4725-b44c-bb9b3252d1b0 · outbound

This paper cites Hallucination is Inevitable: An Innate Limitation of Large Language Models.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Hallucination is Inevitable: An Innate Limitation of Large Language Models

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:06.881976Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6257a753-25ad-40f6-96c8-2ddec3e6d5f6 · outbound

This paper cites Survey of hallucination in natural language generation,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Survey of hallucination in natural language generation,

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.471169Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 29ca56db-af31-4245-8d58-ccff674cbff4 · outbound

This paper cites A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions.

Augmenting the Generality and Performance of Large Language Models for Software Engineering A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:06.996196Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fc94e9eb-3a01-4c73-bd7e-4023fe323005 · outbound

This paper cites From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future.

Augmenting the Generality and Performance of Large Language Models for Software Engineering From LLMs to LLM-based Agents for Software Engineering: A Survey of Current, Challenges and Future

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:07.035785Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation cfe96f87-b82f-4bc6-b93f-77b71cc77a25 · outbound

This paper cites Agents in Software Engineering: Survey, Landscape, and Vision.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Agents in Software Engineering: Survey, Landscape, and Vision

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:07.180480Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2add6a3b-1603-4a23-a1bb-33b122ae11b8 · outbound

This paper cites Large Language Model-Based Agents for Software Engineering: A Survey.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Large Language Model-Based Agents for Software Engineering: A Survey

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:07.337705Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 63ea57aa-708d-4042-bfd6-75f1a7689482 · outbound

This paper cites RoBERTa: A Robustly Optimized BERT Pretraining Approach.

Augmenting the Generality and Performance of Large Language Models for Software Engineering RoBERTa: A Robustly Optimized BERT Pretraining Approach

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:07.411164Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fbf6740d-c943-4059-933b-52b47a039373 · outbound

This paper cites Language models are unsupervised multitask learners,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Language models are unsupervised multitask learners,

Reference 26

Resolution: unresolved; no resolver link, observed 2026-08-07T04:06:07.608807Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4b2a23a2-c713-44af-ab65-a9d563c8ba0a · outbound

This paper cites ISO/IEC/IEEE 24 765:2017(E), 2 017.

Augmenting the Generality and Performance of Large Language Models for Software Engineering ISO/IEC/IEEE 24 765:2017(E), 2 017

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.353461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 95eca949-f331-4409-bc94-3a1d30ed0981 · outbound

This paper cites ISTQB Glossary,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering ISTQB Glossary,

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.258651Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 5ec419f0-b63c-4356-b748-0a1a810e1523 · outbound

This paper cites CPRE Glossary,.

Augmenting the Generality and Performance of Large Language Models for Software Engineering CPRE Glossary,

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T04:06:09.049667Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Observation 28d1a506-c1ed-4fb3-b43d-50efc896cdf5 · outbound

This paper cites Augmenting the Generality and Performance of Large Language Models for Software Engineering.

Augmenting the Generality and Performance of Large Language Models for Software Engineering Augmenting the Generality and Performance of Large Language Models for Software Engineering

Reference 30

Resolution: metadata mismatch; local_arxiv, observed 2026-08-07T04:06:08.204742Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-14T06:32:32.682623+00:00.

Pith citing papers

Observation 28d1a506-c1ed-4fb3-b43d-50efc896cdf5 · inbound