Pith. sign in

Paper Citation Record · LEDGER

LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2310.04535.

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

pith.paper-citation-record.v1
2310.04535 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-07T06:34:17.273281+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-06T22:55:09.356710Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T18:37:10.265891Z

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 db8a545c-3605-4b79-95b9-71430392503b · inbound

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models cites this paper.

SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation

Reference 73

Resolution
unresolved
no resolver link, observed 2026-08-06T22:55:09.356710Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T22:55:09.356710Z digest=sha256:f0232a2b0450ee870cf647d22d9c5633c16e0da3e1e43ab6a8d41786a40faf0e

Observation 532e0e8f-0575-4d12-aef8-10331e0c2d13 · inbound

AnalogTester: A Large Language Model-Based Framework for Automatic Testbench Generation in Analog Circuit Design cites this paper.

AnalogTester: A Large Language Model-Based Framework for Automatic Testbench Generation in Analog Circuit Design LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation

Reference 9

Resolution
unresolved
no resolver link, observed 2026-08-06T17:48:52.389401Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T17:48:52.389401Z digest=sha256:6c3e7ae6d791d5d30dd06945295f1d638b9720c9fc27858c8fce875537d5a4d9

Observation 2d72072e-4d8f-4835-8b7e-93e9bc60ead6 · inbound

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation cites this paper.

VerilogDB: The Largest, Highest-Quality Dataset with a Preprocessing Framework for LLM-based RTL Generation LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation

Reference 55

Resolution
unresolved
no resolver link, observed 2026-08-06T18:54:08.228759Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-06T18:54:08.228759Z digest=sha256:6b869672a5b0fedc96e7243f8c0dd62f8d1d9a92287be7936828e34758a7039d

Observation 0c1e3549-c97d-4a1f-b0e7-bd488ccd5590 · inbound

Wit-HW: Bug Localization in Hardware Design Code via Witness Test Case Generation cites this paper.

Wit-HW: Bug Localization in Hardware Design Code via Witness Test Case Generation LLM4DV: Using Large Language Models for Hardware Test Stimuli Generation

Reference 36

Resolution
verified exact
local_arxiv, observed 2026-08-05T18:37:10.273127Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.

source=arxiv_source observed=2026-08-05T18:37:10.191025Z digest=sha256:f4ac261f22a1853df93182907b973ad1e3be4857de0ccb87e5b4913ffbcdc8dd