Paper Citation Record · LEDGER

Calibration and Correctness of Language Models for Code

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 12 inbound Pith citation observations for arXiv:2402.02047.

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

pith.paper-citation-record.v1
2402.02047 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 12 of 12 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 12 of 12 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:07:50.196341Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-29T13:23:27.706067Z

Reference resolution

0 of 0 outbound references displayed

verified exact0
verified fuzzy0
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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 9458fe60-c486-49fc-b3a6-071c6512f2fa · inbound

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap cites this paper.

From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmap Calibration and Correctness of Language Models for Code

Reference 96

Resolution: verified exact; arxiv_id, observed 2026-05-23T19:08:20.930402Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 91bc21d5-70dd-44ca-84c2-54c45129581b · inbound

Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models cites this paper.

Precision or Peril: A PoC of Python Code Quality from Quantized Large Language Models Calibration and Correctness of Language Models for Code

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-05-23T17:33:15.817779Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation d6335b55-a209-4ab6-b61e-d45eb2a16e93 · inbound

Maximizing Confidence Alone Improves Reasoning cites this paper.

Maximizing Confidence Alone Improves Reasoning Calibration and Correctness of Language Models for Code

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T13:07:50.196341Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3b788c2e-7345-4ba5-9535-a00bdc02dd74 · inbound

HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization cites this paper.

HASHIRU: Hierarchical Agent System for Hybrid Intelligent Resource Utilization Calibration and Correctness of Language Models for Code

Reference 60

Resolution: unresolved; no resolver link, observed 2026-08-07T11:54:57.245537Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0ac83f00-e400-42df-ac6d-d2ded0746aee · inbound

SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL cites this paper.

SQLens: An End-to-End Framework for Error Detection and Correction in Text-to-SQL Calibration and Correctness of Language Models for Code

Reference 37

Resolution: unresolved; no resolver link, observed 2026-08-07T10:46:27.137079Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74954589-49b8-41f1-8aa4-c9aae5fc6359 · inbound

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation cites this paper.

Are They All Good? Evaluating the Quality of CoTs in LLM-based Code Generation Calibration and Correctness of Language Models for Code

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-06T18:57:13.102050Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 433b2156-d3f4-4802-bf9a-d7ec79fb0192 · inbound

From Noise to Knowledge: Interactive Summaries for Developer Alerts cites this paper.

From Noise to Knowledge: Interactive Summaries for Developer Alerts Calibration and Correctness of Language Models for Code

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-05T22:22:15.505584Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3a0f64a5-1db7-4819-80bd-e76c0aeef5be · inbound

Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows cites this paper.

Are LLM Agents the New RPA? A Comparative Study with RPA Across Enterprise Workflows Calibration and Correctness of Language Models for Code

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-05T10:22:31.442315Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f8d6a8c1-7b9d-4d22-b4cd-4d5728ccf61e · inbound

When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning cites this paper.

When Models Know When They Do Not Know: Calibration, Cascading, and Cleaning Calibration and Correctness of Language Models for Code

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-03T11:01:24.795273Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b676da9b-7f28-41d6-af13-c2881c99fec2 · inbound

Uncertainty-aware Generative Recommendation cites this paper.

Uncertainty-aware Generative Recommendation Calibration and Correctness of Language Models for Code

Reference 38

Resolution: unresolved; no resolver link, observed 2026-08-03T00:05:10.800151Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 797d6605-8ed0-40f0-8e55-545c27118169 · inbound

When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions cites this paper.

When to Answer and When to Defer: A Decision Framework for Reliable Code Predictions Calibration and Correctness of Language Models for Code

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-20T04:58:05.246101Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

Observation 21630938-1ef1-4a55-b707-a43089dd6d27 · inbound

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification cites this paper.

Functional Entropy: Predicting Functional Correctness in LLM-Generated Code with Uncertainty Quantification Calibration and Correctness of Language Models for Code

Reference 23

Resolution: metadata mismatch; arxiv_id, observed 2026-06-29T13:23:27.712897Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.