Paper Citation Record · LEDGER

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs

As of 18 August 2026, this Paper Citation Record lists 23 of 23 outbound references and 2 inbound Pith citation observations for arXiv:2504.15210.

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

pith.paper-citation-record.v1
2504.15210 v2

Coverage vector

measured 23 of 23 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:34:01.145392Z

measured 25 of 25 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-11T21:45:42.517559Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:26:13.916471Z

Reference resolution

23 of 23 outbound references displayed

verified exact2
verified fuzzy0
unresolved21
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 582fecea-f890-4259-bf27-e464f6beaf79 · outbound

This paper cites Program Synthesis with Large Language Models.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Program Synthesis with Large Language Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.026055Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 71857ad0-57e8-432d-bbcc-fcd29403dd5b · outbound

This paper cites an unresolved cited work.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Unresolved cited work

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.031883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e1dd0b37-99d6-4d2c-a347-7c070201ea3f · outbound

This paper cites Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Open Problems and Fundamental Limitations of Reinforcement Learning from Human Feedback

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.037432Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation feb76b16-42b5-4b80-9cb7-ec94023f3aee · outbound

This paper cites an unresolved cited work.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Unresolved cited work

Reference 4

Resolution: verified exact; doi, observed 2026-08-16T11:34:01.260116Z

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.

Observation a79f49aa-8697-4611-a2b9-a042e06b0b88 · outbound

This paper cites Evaluating Large Language Models Trained on Code.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Evaluating Large Language Models Trained on Code

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.049972Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ea30a76c-e77e-40e4-b4e9-06aaaf99fb45 · outbound

This paper cites StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.054883Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 50f17fdd-521e-4ff6-a8d7-633e4f512f08 · outbound

This paper cites an unresolved cited work.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Unresolved cited work

Reference 7

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:34:01.647598Z

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.

Observation dbe27fdc-08f4-4550-bc19-0718b17def1a · outbound

This paper cites Measuring Coding Challenge Competence With APPS.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Measuring Coding Challenge Competence With APPS

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.066056Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6ff6c071-45ac-4644-8e94-25f0cb24ab92 · outbound

This paper cites an unresolved cited work.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Unresolved cited work

Reference 9

Resolution: verified exact; doi, observed 2026-08-16T11:34:01.243171Z

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.

Observation 6620d634-c413-4214-bae2-1b1b37153a36 · outbound

This paper cites an unresolved cited work.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Unresolved cited work

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.076092Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f32b00ae-b77e-4282-b19b-6ff9c64cc8cd · outbound

This paper cites CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.080935Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41d0aa29-416c-468f-a611-61a732325f4d · outbound

This paper cites RLTF: Reinforcement Learning from Unit Test Feedback.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs RLTF: Reinforcement Learning from Unit Test Feedback

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.086282Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f38df14e-36e9-4e95-902a-b39c7ad183c2 · outbound

This paper cites Training language models to follow instructions with human feedback.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Training language models to follow instructions with human feedback

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.091896Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e8605360-fe7f-4e46-a534-502d8bdc6a7a · outbound

This paper cites Direct Preference Optimization: Your Language Model is Secretly a Reward Model.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Direct Preference Optimization: Your Language Model is Secretly a Reward Model

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.096990Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c24997c3-8e7e-4179-b065-4e5bbedb2a5b · outbound

This paper cites Execution-based Code Generation using Deep Reinforcement Learning.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Execution-based Code Generation using Deep Reinforcement Learning

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.102338Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 10b63110-9f54-4891-857f-1c037293eae6 · outbound

This paper cites Python Symbolic Execution with LLM-powered Code Generation.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Python Symbolic Execution with LLM-powered Code Generation

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.107321Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa4551ca-25ad-40d3-8fe5-9d3882ba111e · outbound

This paper cites Compilable Neural Code Generation with Compiler Feedback.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Compilable Neural Code Generation with Compiler Feedback

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.113204Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 398516e2-79c2-415d-9b20-23125f1c97bd · outbound

This paper cites CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.119213Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa1daec4-b8fe-45d6-8380-4c4c15727c86 · outbound

This paper cites Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.124724Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation faf30df7-b47f-4e34-b371-72887dd112ce · outbound

This paper cites $\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs $\mathcal{B}$-Coder: Value-Based Deep Reinforcement Learning for Program Synthesis

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.131319Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation fa58853a-e9b3-4738-b5e9-83afa419a6a9 · outbound

This paper cites an unresolved cited work.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs Unresolved cited work

Reference 21

Resolution: unresolved; raw_fallback, observed 2026-08-16T11:34:01.631352Z

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.

Observation f840f13b-3232-443b-b9d7-27e37de85a87 · outbound

This paper cites online" 'onlinestring :=.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs online" 'onlinestring :=

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.140491Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 818e70fc-5439-4c19-a5be-974b11f2761a · outbound

This paper cites write newline.

Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs write newline

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-16T11:34:01.145392Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Pith citing papers

Observation fb274bd0-e345-4d1a-a6e6-9e8cfca61111 · inbound

Teaching LLMs Program Semantics via Symbolic Execution Traces cites this paper.

Teaching LLMs Program Semantics via Symbolic Execution Traces Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:26:13.927352Z

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.

Observation 8d77a167-7348-45de-83c5-9de290b816ab · inbound

SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging cites this paper.

SEDCoT: Enhancing LLM-Based COBOL Code Translation via Symbolic Execution and Delta Debugging Integrating Symbolic Execution into the Fine-Tuning of Code-Generating LLMs

Reference 54

Resolution: unresolved; no resolver link, observed 2026-07-11T21:45:42.517559Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.