Paper Citation Record · LEDGER

The Code Barrier: What LLMs Actually Understand?

As of 18 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:2504.10557.

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

pith.paper-citation-record.v1
2504.10557 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 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 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:17:35.254425Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-01T11:35:42.832954Z

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 298e4df5-19b8-447c-96bf-5ce8ac996869 · inbound

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency cites this paper.

Optimizing Token Consumption in LLMs: A Nano Surge Approach for Code Reasoning Efficiency The Code Barrier: What LLMs Actually Understand?

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-16T11:17:35.254425Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7e13953a-f72c-48e9-9a16-6aca096ff70b · inbound

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study cites this paper.

Using Contrastive Learning to Improve Two-Way Reasoning in Large Language Models: The Obfuscation Task as a Case Study The Code Barrier: What LLMs Actually Understand?

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-15T16:28:34.536630Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2acb36da-02b1-47e0-9d23-816c1bde1c8c · inbound

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing cites this paper.

Is "Knowing It's Malicious Enough?" Evaluating LLMs for Fine-Grained Malware Behavior Auditing The Code Barrier: What LLMs Actually Understand?

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-15T15:56:17.568021Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 37975b99-e31f-4119-94fd-81d8389269c5 · inbound

From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software cites this paper.

From Rookie to Expert: Manipulating LLMs for Automated Vulnerability Exploitation in Enterprise Software The Code Barrier: What LLMs Actually Understand?

Reference 22

Resolution: verified exact; arxiv_id, observed 2026-05-16T20:03:22.168293Z

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 96a98f60-b023-4042-9a66-224704d37137 · inbound

(How) Do Large Language Models Understand High-Level Message Sequence Charts? cites this paper.

(How) Do Large Language Models Understand High-Level Message Sequence Charts? The Code Barrier: What LLMs Actually Understand?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:49:23.224745Z

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 8c446b88-e276-4e48-88e8-e68b1ff62c35 · inbound

(How) Do Large Language Models Understand High-Level Message Sequence Charts? cites this paper.

(How) Do Large Language Models Understand High-Level Message Sequence Charts? The Code Barrier: What LLMs Actually Understand?

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-15T05:49:47.836070Z

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 dfa3ebe5-3da0-49b4-b4d2-b84f20dc9adb · inbound

Do Machines Struggle Where Humans Do? LLM and Human Comprehension of Obfuscated Code cites this paper.

Do Machines Struggle Where Humans Do? LLM and Human Comprehension of Obfuscated Code The Code Barrier: What LLMs Actually Understand?

Reference 58

Resolution: verified exact; arxiv_id, observed 2026-07-01T11:35:42.835199Z

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.