Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links
Paper Citation Record · LEDGER
Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 5 inbound Pith citation observations for arXiv:2306.02546.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-09T06:31:02.800959+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-08-07T11:05:26.045802Z
A source-named dated measurement, never combined with another source.
Source: arxiv_reference, observed 2026-05-12T09:46:26.105817Z
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 82602a99-f234-4ffd-bb38-4b728196a263 · inbound
Beyond C/C++: Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering cites this paper.
Beyond C/C++: Probabilistic and LLM Methods for Next-Generation Software Reverse Engineering Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Reference 28
- Resolution
- unresolved
- no resolver link, observed 2026-08-07T11:05:26.045802Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 47ba4d92-26c2-453b-bed1-45b2887bc6b3 · inbound
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques cites this paper.
Large Language Models in Cybersecurity: Applications, Vulnerabilities, and Defense Techniques Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Reference 98
- Resolution
- unresolved
- no resolver link, observed 2026-08-06T16:24:29.948540Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 83d0145e-81fc-4fcd-993f-6ca32412087c · inbound
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference cites this paper.
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Reference 21
- Resolution
- verified exact
- arxiv_id, observed 2026-05-10T14:45:49.356048Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.
Observation 4282ebf1-0bac-4656-9b71-633f10f9aadc · inbound
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference cites this paper.
CoDe-R: Refining Decompiler Output with LLMs via Rationale Guidance and Adaptive Inference Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Reference 21
- Resolution
- unresolved
- no resolver link, observed 2026-07-12T21:07:20.347554Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 6aa753dd-088e-49a9-93be-aa2e4caf664b · inbound
REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version) cites this paper.
REBENCH: A Procedural, Fair-by-Construction Benchmark for LLMs on Stripped-Binary Types and Names (Extended Version) Symbol Preference Aware Generative Models for Recovering Variable Names from Stripped Binary
Reference 42
- Resolution
- verified exact
- arxiv_id, observed 2026-05-12T09:46:26.108448Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.