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Paper Citation Record · LEDGER

NExT: Teaching Large Language Models to Reason about Code Execution

As of 10 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2404.14662.

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

pith.paper-citation-record.v1
2404.14662 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 13 of 13 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-10T06:31:04.303077+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-10T13:13:35.237486Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-30T13:44:41.795923Z

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 dc02356f-a228-4912-a2d6-4b320984578f · inbound

CodeMind: Evaluating Large Language Models for Code Reasoning cites this paper.

CodeMind: Evaluating Large Language Models for Code Reasoning NExT: Teaching Large Language Models to Reason about Code Execution

Reference 44

Resolution
verified exact
arxiv_id, observed 2026-05-24T03:55:59.675272Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-24T03:53:55.964755Z digest=sha256:34377a8f1824a71bf78e147c29716364a457e480725e3f42b705f6d2b671f227

Observation 64a621c6-5cc3-4c49-9183-234bcafde910 · inbound

Training Language Models to Self-Correct via Reinforcement Learning cites this paper.

Training Language Models to Self-Correct via Reinforcement Learning NExT: Teaching Large Language Models to Reason about Code Execution

Reference 19

Resolution
verified exact
arxiv_id, observed 2026-05-17T12:04:10.383925Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-17T12:04:10.210508Z digest=sha256:08af52378c994c9fa58873a34fd5831c935a7f3e38fb7d0d4f43eaa3d3900657

Observation 4573a54c-22b5-431e-9233-99457b0cb8e9 · inbound

CoCoNUT: Structural Code Understanding does not fall out of a tree cites this paper.

CoCoNUT: Structural Code Understanding does not fall out of a tree NExT: Teaching Large Language Models to Reason about Code Execution

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-10T13:13:35.237486Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-10T13:13:35.237486Z digest=sha256:d16b65f1ee23f642067246a214d6c71c55df4b648143018885ef448a3af4f241

Observation 712134d3-4605-45be-9379-c90766fe2188 · inbound

A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Models cites this paper.

A Tool for In-depth Analysis of Code Execution Reasoning of Large Language Models NExT: Teaching Large Language Models to Reason about Code Execution

Reference 25

Resolution
unresolved
no resolver link, observed 2026-08-09T23:21:56.493230Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-09T23:21:56.493230Z digest=sha256:3157b389f11456eed5256be12e6999c5bdf9c1ca923d279a01cc02adc6dec211

Observation 0306d34a-b0c3-4930-b1ed-93fa9187ac2f · inbound

What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces cites this paper.

What I cannot execute, I do not understand: Training and Evaluating LLMs on Program Execution Traces NExT: Teaching Large Language Models to Reason about Code Execution

Reference 19

Resolution
unresolved
no resolver link, observed 2026-08-08T15:14:54.207215Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-08T15:14:54.207215Z digest=sha256:684d1c05919643e23acef284563ad5af76945ce28e0395aa70813cda432ade70

Observation 08ee141c-3679-4f85-a344-e29b03392c90 · inbound

CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning cites this paper.

CodeReasoner: Enhancing the Code Reasoning Ability with Reinforcement Learning NExT: Teaching Large Language Models to Reason about Code Execution

Reference 30

Resolution
unresolved
no resolver link, observed 2026-08-06T14:50:27.959391Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T14:50:27.959391Z digest=sha256:dd8ff77c72e0104a33a580b778d42132fdac171c1fbe70feac207a29a778ad06

Observation 47953471-4c87-4574-9e25-38d35e764e1e · inbound

ReLog: Execution-Aware Logging with Runtime Feedback for LLM-Oriented Debugging cites this paper.

ReLog: Execution-Aware Logging with Runtime Feedback for LLM-Oriented Debugging NExT: Teaching Large Language Models to Reason about Code Execution

Reference 44

Resolution
unresolved
no resolver link, observed 2026-08-04T05:40:56.150418Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-04T05:40:56.150418Z digest=sha256:7463f044bdddd93c8dceec194586ee1628c8304e1bbb439910a9ffe3a98f9f0a

Observation ee0e9870-acc3-4a0e-8efe-12d7ec693183 · inbound

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

Teaching LLMs Program Semantics via Symbolic Execution Traces NExT: Teaching Large Language Models to Reason about Code Execution

Reference 29

Resolution
verified exact
arxiv_id, observed 2026-05-11T20:26:13.447050Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-08T08:56:25.022619Z digest=sha256:068e40a776310079ccd882386a33511286c3bd75375b02d06e34d6ba64a6bb50

Observation 16a3ce91-2b03-439b-8cce-3a6af7ec15a8 · inbound

Agentic MIP Research: Accelerated Constraint Handler Generation cites this paper.

Agentic MIP Research: Accelerated Constraint Handler Generation NExT: Teaching Large Language Models to Reason about Code Execution

Reference 13

Resolution
verified exact
arxiv_id, observed 2026-05-12T02:16:16.302851Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-12T02:12:27.170669Z digest=sha256:221976cea71e04fc9246f472fad391c59dfe06d718c6f7662938024973a78006

Observation e6fd4555-8b16-488e-ad3d-6042b06876ef · inbound

StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning cites this paper.

StepCodeReasoner: Aligning Code Reasoning with Stepwise Execution Traces via Reinforcement Learning NExT: Teaching Large Language Models to Reason about Code Execution

Reference 37

Resolution
metadata mismatch
arxiv_id, observed 2026-05-13T05:32:20.212235Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=arxiv_source observed=2026-05-13T05:27:37.521421Z digest=sha256:6299635888f7a464e20ce9ef8c1983b5ffcf404f5d65546b0f7c7e877068bc6b

Observation 6a653599-ef5d-4558-9d9e-742fe386fba6 · inbound

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair cites this paper.

MemRepair: Hierarchical Memory for Agentic Repository-Level Vulnerability Repair NExT: Teaching Large Language Models to Reason about Code Execution

Reference 31

Resolution
verified exact
arxiv_id, observed 2026-05-19T23:07:51.129563Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-19T23:06:05.988893Z digest=sha256:621b80672f0dce364bc80832e367eee80c56a43ec72f70e102442c9dde168a60

Observation 38b6bc2c-4ef0-4cb9-b488-01ba34986627 · inbound

Code as Agent Harness cites this paper.

Code as Agent Harness NExT: Teaching Large Language Models to Reason about Code Execution

Reference 30

Resolution
verified exact
arxiv_id, observed 2026-05-20T10:58:14.517676Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-05-20T10:54:54.558241Z digest=sha256:6c65d59b036e2ce3750eb0e749fd3b0dc636ca30776cf6900ff544a548d8ce4d

Observation 589b51b6-7ee5-4b76-afec-2f07f04e8e21 · inbound

MicroAgent: Context-Augmented Multi-Agent Framework for Automatic Microservice Decomposition cites this paper.

MicroAgent: Context-Augmented Multi-Agent Framework for Automatic Microservice Decomposition NExT: Teaching Large Language Models to Reason about Code Execution

Reference 43

Resolution
verified exact
arxiv_id, observed 2026-06-30T13:44:41.797641Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

source=pdf_text observed=2026-06-30T05:46:03.623076Z digest=sha256:043f0d0141b4b2fdd8409181c5a4b7a2be64fd25a373f92617fa7b7499eb9e4f