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Paper Citation Record · LEDGER

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms?

As of 8 August 2026, this Paper Citation Record lists 8 of 8 outbound references and 0 inbound Pith citation observations for arXiv:2607.03158.

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

pith.paper-citation-record.v1
2607.03158 v1

Coverage vector

measured 8 of 8 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-07-12T04:28:33.393520Z

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-08T06:32:00.761636+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

8 of 8 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved8
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation c091ef33-26e3-4b84-9818-f42b8ef2a2d7 · outbound

This paper cites Program Synthesis with Large Language Models.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? Program Synthesis with Large Language Models

Reference 1

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:0bc8e0c9e338e96fdd435c6b75cceb1f37104d29db985414bdce688d04d54750

Observation cc80d0d5-dba5-45bc-9da0-deb241f6a5b2 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? Evaluating Large Language Models Trained on Code

Reference 2

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:42bac0d5c19607fccd7e68387c45714f6764877028afc9aebbc137be7b15e7ef

Observation 6bdd4d66-184a-4456-a589-19273f872a44 · outbound

This paper cites Does Prompt Formatting Have Any Impact on LLM Performance?.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? Does Prompt Formatting Have Any Impact on LLM Performance?

Reference 3

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:410e523e2c61f191169224440b2cb1db9cd95a6f51ed457b22f99cb3856380cb

Observation e6332c34-d139-4095-bae3-570a287e1d3b · outbound

This paper cites Truong, Weixin Liang, Fan-Yun Sun, and Nick Haber.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? Truong, Weixin Liang, Fan-Yun Sun, and Nick Haber

Reference 4

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:669c4991ccf91eb99126acc3bf2a78404b5cbf1ce5606ff2b66a780e37c8473b

Observation 0d4e6ead-48df-4c92-a52d-e7e63ae03dce · outbound

This paper cites SWE -bench: Can language models resolve real-world github issues? In International Conference on Learning Representations (ICLR), 2024.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? SWE -bench: Can language models resolve real-world github issues? In International Conference on Learning Representations (ICLR), 2024

Reference 5

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:91a2e3b76d1b181f37cb73d43f959f5a0ea47ead9b8ad5c4eac9ec44d0623df4

Observation ec50c64b-ab52-4ebe-a47f-10a85d49f662 · outbound

This paper cites From articles to code: on-demand generation of core algorithms from scientific publications.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? From articles to code: on-demand generation of core algorithms from scientific publications

Reference 6

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:720a47b416bdfdd922f1dfac6ec258be39f3185721cbf40c828d53fb07ac8de8

Observation 2930f41d-a84d-4745-adbd-357b70e3c544 · outbound

This paper cites Paperbench: Evaluating AI s ability to replicate AI research.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? Paperbench: Evaluating AI s ability to replicate AI research

Reference 7

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:af1644314e1a6a486ec7f02293835c510693375a15e9b4ee48f6d5018a4ec529

Observation 8f306a95-9a3c-416d-a672-d5271d076014 · outbound

This paper cites Scireplicate-bench: Benchmarking LLM s in agent-driven algorithmic reproduction from research papers.

Which Algorithm Specification Formats Help Language Models Implement Machine Learning Algorithms? Scireplicate-bench: Benchmarking LLM s in agent-driven algorithmic reproduction from research papers

Reference 8

Resolution
unresolved
no resolver link, observed 2026-07-12T04:28:33.393520Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-12T04:28:33.393520Z digest=sha256:1b505dc6b6172f51eb130983c0233489e57d8dfb226c2cc0bc5024cfac1d6d5c

Pith citing papers

No inbound Pith citation observations are available.