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Paper Citation Record · LEDGER

Natural Language to Code Generation in Interactive Data Science Notebooks

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2212.09248.

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

pith.paper-citation-record.v1
2212.09248 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-08T16:46:04.054718Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T23:04:44.489085Z

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 29a7cb2b-96ac-4b74-91f0-c07dffc899de · inbound

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation cites this paper.

Language Model Beats Diffusion -- Tokenizer is Key to Visual Generation Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 124

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:06:44.699695Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-13T20:06:44.480769Z digest=sha256:7ae1109cff2b56fb52f8303e6ba2f21d9a151b3aa1c1e46dcbef1f868e12f130

Observation 5db31f4b-7bb0-4cac-a2bb-e23cafece043 · inbound

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive cites this paper.

Smaug: Fixing Failure Modes of Preference Optimisation with DPO-Positive Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 128

Resolution
verified exact
arxiv_id, observed 2026-05-17T23:04:44.491270Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-17T23:04:44.287660Z digest=sha256:96aa4d20ad78f0f291dfd914bdabbe36a9669dc669b774fe299cfd337ce13d4e

Observation ed931c2c-3b4f-4706-a563-19fbf052a842 · inbound

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code cites this paper.

LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-10T17:34:42.684676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=arxiv_source observed=2026-05-10T17:34:42.565806Z digest=sha256:93d4b1527b5f2a3b34239df493a2b4f9825253d5473ad4990ae7ef9900323995

Observation be8cfd84-c410-4b06-bacd-2aa31b2d4f5e · inbound

CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science Research Repositories cites this paper.

CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science Research Repositories Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 51

Resolution
unresolved
no resolver link, observed 2026-08-08T16:46:04.054718Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T16:46:04.054718Z digest=sha256:59c3717d95d46b4baf2d330010d55b39c39d56fc078cd9a4d71ae15bbd976f14

Observation 8c362341-1348-4ed4-bc0b-e73b01dcacee · inbound

Knowledge-Enhanced Program Repair for Data Science Code cites this paper.

Knowledge-Enhanced Program Repair for Data Science Code Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 24

Resolution
unresolved
no resolver link, observed 2026-08-07T20:37:30.689106Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T20:37:30.689106Z digest=sha256:563f7dd19e1105a02602e53f94037d8524e44e49f9fdfe889fd0571dbec72cad

Observation fb373681-2cd1-4688-80b1-e656e8507ee3 · inbound

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey cites this paper.

Evolutionary Perspectives on the Evaluation of LLM-Based AI Agents: A Comprehensive Survey Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 20

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:17.290883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:00:17.290883Z digest=sha256:8075960c6d7a617db021539b6335d146fbadd348c86d46f20f654a65fd82ad16

Observation 55f43140-ffb4-4a75-b919-c308c0437bbb · inbound

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code cites this paper.

In-Context Learning as an Effective Estimator of Functional Correctness of LLM-Generated Code Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-06T19:34:36.781148Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T19:34:36.781148Z digest=sha256:70620ada95702cda5fbe2a6705cb148aee987fb6539df6ce28efb98b50619f41

Observation 77027596-2ac8-413f-a228-9f2539f77eb6 · inbound

Software Self-Extension with SelfEvolve: an Agentic Architecture for Runtime Code Generation cites this paper.

Software Self-Extension with SelfEvolve: an Agentic Architecture for Runtime Code Generation Natural Language to Code Generation in Interactive Data Science Notebooks

Reference 35

Resolution
verified exact
arxiv_id, observed 2026-05-16T06:57:29.089893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-18T06:34:40.430872+00:00.

source=pdf_text observed=2026-05-16T06:55:14.121192Z digest=sha256:d99e54644d38c964d0a27ca2759ec876cdd0992b57374d5577ea235adb05cc6e