Paper Citation Record · LEDGER

The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

As of 7 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 8 inbound Pith citation observations for arXiv:2403.07257.

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

pith.paper-citation-record.v1
2403.07257 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 8 of 8 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 8 of 8 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T10:34:04.859147Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-11T20:31:14.166413Z

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 b9cb44e7-9ce6-4b06-ac99-68df1c7d398b · inbound

QiMeng: Fully Automated Hardware and Software Design for Processor Chip cites this paper.

QiMeng: Fully Automated Hardware and Software Design for Processor Chip The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 120

Resolution: unresolved; no resolver link, observed 2026-08-07T10:34:04.859147Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e546350a-2b6e-4be3-8a00-7285f4309725 · inbound

CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs cites this paper.

CROP: Circuit Retrieval and Optimization with Parameter Guidance using LLMs The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-06T20:41:59.561876Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b6a75014-a137-4de7-943b-a74f75678c88 · inbound

RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs cites this paper.

RealBench: Benchmarking Verilog Generation Models with Real-World IP Designs The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-06T15:19:53.116262Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 844bcb60-1ed6-4c4c-83d8-e874fe94597c · inbound

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs cites this paper.

MMCircuitEval: A Comprehensive Multimodal Circuit-Focused Benchmark for Evaluating LLMs The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-06T15:51:16.969771Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 5989ffb7-2a52-4f37-b701-bb64fd666b53 · inbound

A Multi-Agent Generative AI Framework for IC Module-Level Verification Automation cites this paper.

A Multi-Agent Generative AI Framework for IC Module-Level Verification Automation The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-06T12:36:38.657858Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 32782312-4ab5-4a82-96d7-0490272870a1 · inbound

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design cites this paper.

FedChip: Federated LLM for Artificial Intelligence Accelerator Chip Design The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-06T14:49:56.564912Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 703eba2e-6f26-46d0-8460-44f29f834cd9 · inbound

AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval cites this paper.

AnalogRetriever: Learning Cross-Modal Representations for Analog Circuit Retrieval The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 10

Resolution: verified exact; arxiv_id, observed 2026-05-11T20:31:14.169475Z

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.

Observation f6cb9033-0bce-4624-b975-60a8c7fe1a79 · inbound

Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation cites this paper.

Towards Reliable AI-Assisted Analog Design: Template-Constrained LLM Agents for SAR ADC Generation The Dawn of AI-Native EDA: Opportunities and Challenges of Large Circuit Models

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T05:12:19.994178Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.