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Paper Citation Record · LEDGER

Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

As of 8 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 13 inbound Pith citation observations for arXiv:2308.01240.

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

pith.paper-citation-record.v1
2308.01240 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 2026-08-08T06:32:00.761636+00:00

measured 13 of 13 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T06:00:16.923044Z

measured 1 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

18
arxiv_reference, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation bde149ff-327d-4cbf-93c6-85e846f42169 · inbound

LLM Evaluators Recognize and Favor Their Own Generations cites this paper.

LLM Evaluators Recognize and Favor Their Own Generations Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 33

Resolution
verified exact
arxiv_id, observed 2026-05-22T18:44:28.906549Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-22T18:44:28.766639Z digest=sha256:c5ab9c5ddaf8cd1c608bd18057f171be6dcc102d9efe73aa537d4b9d7ee2617f

Observation 8f21828f-ec4c-4852-8fca-44cebbb71860 · inbound

Large Language Model-Based Agents for Software Engineering: A Survey cites this paper.

Large Language Model-Based Agents for Software Engineering: A Survey Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 130

Resolution
verified exact
arxiv_id, observed 2026-05-17T12:35:48.348348Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-17T12:35:48.170947Z digest=sha256:7faedecbbc264febbc7de2ab356dbceec78fb8e825b074bd96ce9a932778f283

Observation c30b4f62-4304-40d6-9164-317564d99421 · inbound

"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering cites this paper.

"Should I Give Up Now?" Investigating LLM Pitfalls in Software Engineering Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 23

Resolution
verified exact
arxiv_id, observed 2026-05-23T17:33:15.999793Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-23T17:30:20.359630Z digest=sha256:436115ed70bffe79111c5559953a50e90749cdeccc3717529050af435dfd2e27

Observation 412aafdc-837c-47dd-96bf-e7da983c14d6 · inbound

AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation cites this paper.

AdaDec: A Uncertainty-Guided Lookahead Decoding Framework for LLM-Based Code Generation Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 9

Resolution
verified exact
arxiv_id, observed 2026-05-19T10:22:14.567778Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-19T10:19:07.701197Z digest=sha256:1c1bff10890681ef837aa5390936201f3d718d6b9e893da5efb57b0a34bdbc27

Observation a9a44159-a09f-4e11-8a89-a47582ee1cb6 · 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 Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 16

Resolution
unresolved
no resolver link, observed 2026-08-07T06:00:16.923044Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-07T06:00:16.923044Z digest=sha256:b1fb1bdc64d4c8e9e6ac737fed2d11287e11908fb5d0fbbb6264593834a546d9

Observation 876b9082-be22-4e2a-8cc0-f8e484d4aefa · inbound

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models cites this paper.

Is Quantization a Deal-breaker? Empirical Insights from Large Code Models Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 52

Resolution
unresolved
no resolver link, observed 2026-08-06T17:56:21.347461Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:56:21.347461Z digest=sha256:86e7a640a9847fcf461fd7c65a41856c05e89df849f52f7d8c7664234696139c

Observation 5d82ea6b-c7e8-4fd7-8d2d-d7d5bab565ba · inbound

Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization cites this paper.

Chain-of-Descriptions: Improving Code LLMs for VHDL Code Generation and Summarization Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 23

Resolution
unresolved
no resolver link, observed 2026-08-06T16:52:54.207511Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:52:54.207511Z digest=sha256:76442cc22216e12fe3a4df6b9288d7aeddd9f8b927d71ef96bde9b9c4f3891b2

Observation 14ae6066-fe9e-4458-a1e5-056f900c6ac8 · inbound

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models cites this paper.

Impact of Code Context and Prompting Strategies on Automated Unit Test Generation with Modern General-Purpose Large Language Models Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 22

Resolution
unresolved
no resolver link, observed 2026-08-06T16:20:48.461201Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T16:20:48.461201Z digest=sha256:0264695f3baff21a7768f9bbfa8201922e301355410ae2a3e271e308a5883b8c

Observation 206e38da-14f4-4e7e-9702-a5b07d5bf678 · inbound

IFEvalCode: Controlled Code Generation cites this paper.

IFEvalCode: Controlled Code Generation Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 74

Resolution
unresolved
no resolver link, observed 2026-08-06T11:44:34.123108Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T11:44:34.123108Z digest=sha256:d6ab3f1bfd916fba364d4cd209901a212b474721ed810f084fd646db858bff62

Observation 333a04eb-1ffc-464a-ac94-f3ab82fbb241 · inbound

Project-Level C-to-Rust Translation via Pointer Knowledge Graphs cites this paper.

Project-Level C-to-Rust Translation via Pointer Knowledge Graphs Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 49

Resolution
verified exact
arxiv_id, observed 2026-05-18T08:31:06.714699Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-05-18T08:26:15.321418Z digest=sha256:9046a6fdc4ec9ce26a4fec7309795b73b4acf8f53923b3e29e12ef70b3d18dd5

Observation ebda495a-e8a8-4365-9848-e71392b8f8ac · inbound

LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities cites this paper.

LLM-Based Agentic Systems for Software Engineering: Challenges and Opportunities Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 31

Resolution
unresolved
no resolver link, observed 2026-08-03T10:30:41.902327Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-03T10:30:41.902327Z digest=sha256:3cdf00ceda8c2f924ff837b4848dadb4fb306e1458e715ba3f8a1458a934c30b

Observation 469ef906-f6ed-46e3-93be-abc49c564503 · inbound

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges cites this paper.

Stability vs. Manipulability: Evaluating Robustness Under Post-Decision Interaction in LLM Judges Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 78

Resolution
verified exact
arxiv_id, observed 2026-07-02T08:36:47.681968Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=arxiv_source observed=2026-06-28T05:58:59.870335Z digest=sha256:8851e1d7da38a19c2c56be5fbc025e01b329b0893c5c4f9abfe2dd068fef3286

Observation 7c2c9227-d8f3-42d4-9b7a-e895fdf877f1 · inbound

ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards cites this paper.

ROSUM-MCTS: Monte Carlo Tree Search-Inspired HDL Code Summarization with Structural Rewards Evaluating Instruction-Tuned Large Language Models on Code Comprehension and Generation

Reference 25

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:27:22.700040Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-08T06:32:00.761636+00:00.

source=pdf_text observed=2026-06-27T20:22:27.538227Z digest=sha256:f47022a8a2d7ed0120f696545c2c21d8b3fb88335e42c8d9cc0c9db9a5aaea5b