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Paper Citation Record · LEDGER

PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 15 inbound Pith citation observations for arXiv:2307.14936.

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

pith.paper-citation-record.v1
2307.14936 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 15 of 15 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 15 of 15 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T00:45:04.845561Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-17T04:58:10.262372Z

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 732f9e12-fdae-465a-ada6-0755974b8599 · 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 PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 53

Resolution
metadata mismatch
arxiv_id, observed 2026-05-10T17:34:43.025678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=arxiv_source observed=2026-05-10T17:34:42.565806Z digest=sha256:73361ba98c9340870bcba36c0ae02590b34dfd07b421b2f7211d309589cd5231

Observation 83c5b9f4-8e4f-40e4-b98b-ba80f24daaec · inbound

A Survey on Large Language Models for Code Generation cites this paper.

A Survey on Large Language Models for Code Generation PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 239

Resolution
verified exact
arxiv_id, observed 2026-05-13T20:18:06.664385Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-13T20:18:06.304134Z digest=sha256:c3021ba64173a09204e892131c2465011a5107b5c4b5f54ecd599b804b3e4ba1

Observation 0557e36f-ef1d-472e-881d-c0e862fb66ec · inbound

Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms cites this paper.

Generalist Virtual Agents: A Survey on Autonomous Agents Across Digital Platforms PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 127

Resolution
unresolved
no resolver link, observed 2026-08-12T19:10:14.737003Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-12T19:10:14.737003Z digest=sha256:424721afae3903e1a54d858db81d1cdfc2e4d3be4c059a24ca006d9d6c3441cb

Observation 4bc90412-dacc-495a-abc0-e0142d1141b7 · inbound

Unseen Horizons: Unveiling the Real Capability of LLM Code Generation Beyond the Familiar cites this paper.

Unseen Horizons: Unveiling the Real Capability of LLM Code Generation Beyond the Familiar PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 26

Resolution
unresolved
no resolver link, observed 2026-08-11T18:16:26.737213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T18:16:26.737213Z digest=sha256:56cd9dbdbbeed448fad52038ec5dacb67b5ac3f3b42fa8ca8e8dd0d7b8131a5c

Observation 4ce2cddf-733d-441c-86e4-df272013cb6e · inbound

ViUniT: Visual Unit Tests for More Robust Visual Programming cites this paper.

ViUniT: Visual Unit Tests for More Robust Visual Programming PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-11T17:34:37.837656Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T17:34:37.837656Z digest=sha256:a8768147f6fcd188258859aa41b110f59e4bba160487f7acea70c67d5716bd54

Observation 6649c208-2919-4480-bfba-4735dee65c93 · inbound

CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation cites this paper.

CoopetitiveV: Leveraging LLM-powered Coopetitive Multi-Agent Prompting for High-quality Verilog Generation PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-11T15:27:03.628325Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-11T15:27:03.628325Z digest=sha256:6f44bb59280611865c7719a489c54c1a7de13ffeba7b9b74557a787edc1630fb

Observation 65e15eea-dee8-4ac2-93d5-955c323ccf77 · inbound

The Current Challenges of Software Engineering in the Era of Large Language Models cites this paper.

The Current Challenges of Software Engineering in the Era of Large Language Models PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 129

Resolution
unresolved
no resolver link, observed 2026-08-11T12:10:14.244170Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-11T12:10:14.244170Z digest=sha256:b5fa71bed9b2d20850b9cecfccf1f66af62d3e7e578bc08de18329cbbc808947

Observation 77213e48-620d-4493-8657-29e6b2f47b98 · inbound

Distilling Desired Comments for Enhanced Code Review with Large Language Models cites this paper.

Distilling Desired Comments for Enhanced Code Review with Large Language Models PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 42

Resolution
unresolved
no resolver link, observed 2026-08-10T23:28:51.284899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T23:28:51.284899Z digest=sha256:74a401ff7025b7c5f7dbc59bf5d1e394aa82145d0469d25e469b093963c20135

Observation cfb8730d-5064-45be-bdf3-fa531b02d325 · inbound

LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks cites this paper.

LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83 Software Engineering Benchmarks PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 70

Resolution
unresolved
no resolver link, observed 2026-08-08T16:26:32.346001Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-08T16:26:32.346001Z digest=sha256:32ea5f471133b0f37c229861a3b7ecf5c7cde38db4b0b99d88c595b2ccd33c61

Observation 456f5238-c5f9-4f6b-9796-d301d8f75d35 · inbound

The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models cites this paper.

The Art of Repair: Optimizing Iterative Program Repair with Instruction-Tuned Models PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-16T00:45:04.845561Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T00:45:04.845561Z digest=sha256:dfb4c02d9e814d315d0495e279037ebb9fc046cb0e7f28ffafbf1c4d24f79da0

Observation e06830c2-dd1a-4aa0-9ee6-1e5ce3ab12cc · inbound

Towards Mitigating API Hallucination in Code Generated by LLMs with Hierarchical Dependency Aware cites this paper.

Towards Mitigating API Hallucination in Code Generated by LLMs with Hierarchical Dependency Aware PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 35

Resolution
unresolved
no resolver link, observed 2026-08-15T23:19:28.857165Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T23:19:28.857165Z digest=sha256:f9f9c01a5b1bee84408520a7fb680a6583edefe0e1d051b7226fc29600339f77

Observation 908c5082-4476-4ad8-9006-e8db809235fb · inbound

Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing cites this paper.

Reinforcing Spatial Reasoning in Vision-Language Models with Interwoven Thinking and Visual Drawing PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 55

Resolution
verified exact
arxiv_id, observed 2026-05-17T04:58:10.264392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-17T04:58:10.202784Z digest=sha256:d090dc550cbc18b4d6b332001d4a0ba5d0630a268f9653c006f9cc6b94d09a94

Observation 9d10f4eb-44d4-460e-9528-fb71be934b6f · inbound

MemoCoder: Automated Function Synthesis using LLM-Supported Agents cites this paper.

MemoCoder: Automated Function Synthesis using LLM-Supported Agents PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 2023

Resolution
unresolved
no resolver link, observed 2026-08-15T18:13:23.309373Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-15T18:13:23.309373Z digest=sha256:5b86971e9e10ae6cea544876f40106fe74021411d6223d2870711e3d441a5b7d

Observation fa35b624-ca18-436f-a0b3-1c5f63be7234 · inbound

WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning cites this paper.

WebGen-R1: Incentivizing Large Language Models to Generate Functional and Aesthetic Websites with Reinforcement Learning PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 39

Resolution
verified exact
arxiv_id, observed 2026-05-10T00:19:46.747414Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

source=pdf_text observed=2026-05-10T00:18:18.340391Z digest=sha256:2f3d14f628f773f8a5f0a214953ad9a74044097a4a15dc656f74d226f3871d22

Observation 4a4b2b2b-6971-4a42-9cbf-8cf63365dfb3 · inbound

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality cites this paper.

Large Language Models for Code Generation from Multilingual Prompts: A Curated Benchmark and a Study on Code Quality PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedback

Reference 86

Resolution
unresolved
no resolver link, observed 2026-08-02T01:01:33.494401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-02T01:01:33.494401Z digest=sha256:03087850fc03d0fdfa6329f824166aa4d8184591e60d1c92359fee529b74ed96