Paper Citation Record · LEDGER

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation

As of 23 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 0 inbound Pith citation observations for arXiv:2605.12857.

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

pith.paper-citation-record.v1
2605.12857 v1

Coverage vector

measured 40 of 40 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-06-30T21:59:20.163621Z

measured 40 of 40 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-23T06:30:58.430688+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

40 of 40 outbound references displayed

verified exact5
verified fuzzy31
unresolved4
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 873fa8aa-0b58-4128-bb20-bdc4db66e094 · outbound

This paper cites Evaluating large language models trained on code, 2021.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Evaluating large language models trained on code, 2021

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.669807Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 78ca7e6d-e68f-4b5f-b387-e56a702f0ba8 · outbound

This paper cites SiliconMind-V1: Multi-agent distillation and debug-reasoning workflows for Verilog code generation, 2026.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation SiliconMind-V1: Multi-agent distillation and debug-reasoning workflows for Verilog code generation, 2026

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.671562Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 6056e784-671a-4e37-822e-1fba75b67114 · outbound

This paper cites Teaching large language models to self-debug.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Teaching large language models to self-debug

Reference 3

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.676252Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation ead768f7-35a2-43f4-8059-3a34b6a4a42f · outbound

This paper cites ChipSeek-R1: Generating human-surpassing RTL with LLM via hierarchical reward-driven reinforcement learning, 2025.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation ChipSeek-R1: Generating human-surpassing RTL with LLM via hierarchical reward-driven reinforcement learning, 2025

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.694877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 4db65b0c-6bea-407d-8e12-cfc7afce1379 · outbound

This paper cites AutoVCoder: A systematic framework for automated Verilog code generation using LLMs, 2024.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation AutoVCoder: A systematic framework for automated Verilog code generation using LLMs, 2024

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.662151Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a9bbd8b6-ff26-44ff-a562-b8a98324acb3 · outbound

This paper cites DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning, 2025.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation DeepSeek-R1: Incentivizing reasoning capability in LLMs via reinforcement learning, 2025

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.665885Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a67f751b-3458-44af-b9ea-89dc98132c81 · outbound

This paper cites OriGen: Enhancing RTL code generation with code- to-code augmentation and self-reflection.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation OriGen: Enhancing RTL code generation with code- to-code augmentation and self-reflection

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.660069Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7878a638-024f-4ea3-b268-fde3fee404e1 · outbound

This paper cites VerilogCoder: Autonomous Verilog coding agents with graph-based planning and abstract syntax tree (AST)-based waveform tracing tool.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation VerilogCoder: Autonomous Verilog coding agents with graph-based planning and abstract syntax tree (AST)-based waveform tracing tool

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.658136Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 224e5729-87dc-4dc1-8e60-1c83efbc8edf · outbound

This paper cites MetaGPT: Meta program- ming for a multi-agent collaborative framework.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation MetaGPT: Meta program- ming for a multi-agent collaborative framework

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.668119Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2b82c794-dd3d-416a-bce6-630d851d4d6a · outbound

This paper cites Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.687382Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 67136462-42a3-4fad-96f1-e5dc0189638e · outbound

This paper cites AIvril: AI-driven RTL generation with verification in-the-loop, 2024.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation AIvril: AI-driven RTL generation with verification in-the-loop, 2024

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.715760Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 87d940bb-c0bb-4103-926c-1f09e68d7e85 · outbound

This paper cites CraftRTL: High-quality synthetic data generation for Verilog code models with correct-by-construction non-textual representations and targeted code repair, 2024.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation CraftRTL: High-quality synthetic data generation for Verilog code models with correct-by-construction non-textual representations and targeted code repair, 2024

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.723322Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1ac0424e-fcea-4546-944a-a38dbbfedfad · outbound

This paper cites an unresolved cited work.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Unresolved cited work

Reference 13

Resolution: unresolved; raw_fallback, observed 2026-07-07T15:13:54.710146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2ab3f3fe-c1d1-44ce-8d7e-57f423d0fe47 · outbound

This paper cites OpenLLM-RTL: Open dataset and benchmark for LLM-aided design RTL generation.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation OpenLLM-RTL: Open dataset and benchmark for LLM-aided design RTL generation

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.712007Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation cc8750bb-d4d4-48b7-bfa5-07415809974f · outbound

This paper cites Multi-agent actor-critic for mixed cooperative-competitive environments.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Multi-agent actor-critic for mixed cooperative-competitive environments

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.706103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a1f99dcd-aa8e-4925-8ad7-21b49e2f9007 · outbound

This paper cites Self-refine: Iterative refinement with self-feedback.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Self-refine: Iterative refinement with self-feedback

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.698495Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 27f36e40-dfd5-4479-83fe-aa2e8a46a213 · outbound

This paper cites CoopetitiveV: Leveraging LLM-powered coopetitive multi-agent prompting for high-quality Verilog generation, 2024.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation CoopetitiveV: Leveraging LLM-powered coopetitive multi-agent prompting for high-quality Verilog generation, 2024

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.700287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7a7ab50e-a321-470c-b6c4-39e6e8b8bd28 · outbound

This paper cites BetterV: Controlled Verilog generation with discriminative guidance.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation BetterV: Controlled Verilog generation with discriminative guidance

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.696700Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 22bbada9-d4e1-4e6d-8938-0f61a7510787 · outbound

This paper cites Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Revisiting VerilogEval: A Year of Improvements in Large-Language Models for Hardware Code Generation

Reference 19

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:05:05.880846Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 70ad44aa-d7cd-4b57-b824-2abbb68a642a · outbound

This paper cites Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Comprehensive Verilog Design Problems: A Next-Generation Benchmark Dataset for Evaluating Large Language Models and Agents on RTL Design and Verification

Reference 20

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:05:05.892499Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e0b129de-fab4-4f64-ad85-5abcb2fdaa43 · outbound

This paper cites ChatDev: Communicative agents for software development.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation ChatDev: Communicative agents for software development

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.702146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation a58c9ba2-2e29-4258-ad24-3f8a9ddde6f0 · outbound

This paper cites an unresolved cited work.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Unresolved cited work

Reference 22

Resolution: unresolved; raw_fallback, observed 2026-07-07T15:13:54.703999Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0faf9549-2d11-4f4b-a5c5-f614b1cb608e · outbound

This paper cites HybridFlow: A Flexible and Efficient RLHF Framework.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation HybridFlow: A Flexible and Efficient RLHF Framework

Reference 23

Resolution: verified exact; local_arxiv, observed 2026-06-30T22:05:05.895627Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 321318e3-c1d9-412b-af0c-4a4f04622deb · outbound

This paper cites Re- flexion: Language agents with verbal reinforcement learning.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Re- flexion: Language agents with verbal reinforcement learning

Reference 24

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.708142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 186df4be-ef74-4912-bf03-1b1a2c939fbf · outbound

This paper cites Pyverilog: A python-based hardware design processing toolkit for Verilog HDL.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Pyverilog: A python-based hardware design processing toolkit for Verilog HDL

Reference 25

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.721220Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 47cd8806-e13f-4d75-8406-b47645344b32 · outbound

This paper cites Qwen3.5: More intelligence, less compute, 2026.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Qwen3.5: More intelligence, less compute, 2026

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.693070Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 3ad289c5-1bf3-4a1c-95ab-7cd0734eee25 · outbound

This paper cites AutoChip: Automating HDL generation using LLM feedback, 2023.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation AutoChip: Automating HDL generation using LLM feedback, 2023

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.683065Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 793a5788-57ba-4d87-a35d-14a71dbfc0c8 · outbound

This paper cites VeriReason: Reinforcement learning with testbench feedback for reasoning- enhanced Verilog generation, 2025.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation VeriReason: Reinforcement learning with testbench feedback for reasoning- enhanced Verilog generation, 2025

Reference 28

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.664103Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 5d9bb77f-e568-4f51-85b1-d512c51922ba · outbound

This paper cites Chain-of-thought prompting elicits reasoning in large language models, 2022.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Chain-of-thought prompting elicits reasoning in large language models, 2022

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.717572Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7b561313-6a43-48be-8e64-31b1e9005f1c · outbound

This paper cites Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Jimenez, Alexander Wettig, Kilian Lieret, Shunyu Yao, Karthik Narasimhan, and Ofir Press

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.719344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bcb82b6b-ac09-4048-a326-cbc8d4cd9f8b · outbound

This paper cites Tree of thoughts: Deliberate problem solving with large language models.Ad- vances in neural information processing systems, 36:11809–11822.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Tree of thoughts: Deliberate problem solving with large language models.Ad- vances in neural information processing systems, 36:11809–11822

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.679231Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 2fa33caa-4ee8-4b19-a114-b1236b423a98 · outbound

This paper cites ChipBench: A next-step benchmark for evaluating LLM performance in AI-aided chip design.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation ChipBench: A next-step benchmark for evaluating LLM performance in AI-aided chip design

Reference 32

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:05:05.901570Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation bc78bf76-53c0-45cf-9e3d-97f33c7f32a8 · outbound

This paper cites RTLSeek: Boosting the LLM-based RTL generation with multi-stage diversity-oriented reinforcement learning, 2026.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation RTLSeek: Boosting the LLM-based RTL generation with multi-stage diversity-oriented reinforcement learning, 2026

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.681046Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 7f0a7a4c-5c41-4f0a-853e-1fbc3826cb43 · outbound

This paper cites Stronger-MAS: Multi-agent reinforcement learning for collaborative LLMs.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Stronger-MAS: Multi-agent reinforcement learning for collaborative LLMs

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.685159Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0083ed8a-43f1-4313-857f-071543c48383 · outbound

This paper cites MAGE: A multi-agent engine for automated RTL code generation, 2024.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation MAGE: A multi-agent engine for automated RTL code generation, 2024

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.689300Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 600fcdbe-ddcf-4417-9240-99693253c93e · outbound

This paper cites LlamaFactory: Unified efficient fine-tuning of 100+ language models.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation LlamaFactory: Unified efficient fine-tuning of 100+ language models

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.691296Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 0b1be98a-c55e-4f7b-8a96-ca66def090cf · outbound

This paper cites Qimeng-codev-r1: Reasoning-enhanced verilog generation.arXiv preprint arXiv:2505.24183.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Qimeng-codev-r1: Reasoning-enhanced verilog generation.arXiv preprint arXiv:2505.24183

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-06-30T22:05:05.884020Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 24db57d0-0940-490d-9a3a-3b4ae2cba04f · outbound

This paper cites an unresolved cited work.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-07-07T15:13:54.674613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation d5c134f5-84f3-40bd-aa3a-de8355942e77 · outbound

This paper cites an unresolved cited work.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation Unresolved cited work

Reference 39

Resolution: unresolved; raw_fallback, observed 2026-07-07T15:13:54.713839Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 569163ec-fa62-43a7-9e27-ef4a6b926e81 · outbound

This paper cites a", 0) & 0x3FF b = inputs.get(.

ChipMATE: Multi-Agent Training via Reinforcement Learning for Enhanced RTL Generation a", 0) & 0x3FF b = inputs.get(

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-07-07T15:13:54.673097Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Pith citing papers

No inbound Pith citation observations are available.