Echo-wxy/DG-VDT reference implementation

Evidence record

Author repository for the DG-VDT reward-engine reference implementation. Public material includes the trace-graph schema, RGCN encoder, canonical reference attack signatures, dual-stage reward computation and deterministic unit tests. The repository explicitly excludes the EVM graph-extraction pipeline, full GRPO training/evaluation pipeline, author-constructed datasets, pretrained encoder/policy weights and benchmark reproducibility scripts. As checked after journal publication, the README still says the manuscript is under review and that the missing artifacts will be released upon acceptance, leaving the promised release timing unresolved.

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Claim-level citations (5)

• contextDG-VDT uses multi-graph reinforcement learning for Ethereum vulnerability detection and attacker traceability: The paper reports approximately 6 GPU-hours on one RTX 3090 for RGCN pre-training, about 48 GPU-hours on 4xA100-80GB for DG-VDT-7B GRPO training, and about 180 GPU-hours on 8xA100-80GB for DG-VDT-32B. These are one-time training costs and are distinct from the reported per-trace inference latency.

  Code Information and 'Hardware used in the paper'

• contextDG-VDT uses multi-graph reinforcement learning for Ethereum vulnerability detection and attacker traceability: DG-VDT represents each EVM execution trace as three complementary graphs - fund flow, contract creation and contract calls - and trains a policy with a dual-stage graph reward that transitions from embedding similarity to strict subgraph-isomorphism matching.

  README, Description section; repository root and dgvdt/ reference implementation

• supportsDG-VDT uses multi-graph reinforcement learning for Ethereum vulnerability detection and attacker traceability: The Applied Sciences article is publicly published with a September 30, 2026 publication date, while the checked DG-VDT repository still describes the manuscript as under review and says the unreleased datasets, full training/evaluation pipeline and pretrained weights will be released upon acceptance. The public record therefore shows a release-status lag or stale repository wording, not completion of the promised reproducibility release.

  README opening paragraph; Dataset Information; Usage Instructions item 3

• supportsDG-VDT uses multi-graph reinforcement learning for Ethereum vulnerability detection and attacker traceability: The authors publicly expose the reward engine, graph schema, RGCN encoder, canonical reference graphs and unit tests, but the author-constructed datasets, full GRPO training/evaluation pipeline and pretrained model weights are not yet publicly deposited; the paper and repository say they are available to editors/reviewers and are planned for later release.

  README, opening description and Dataset Information sections; repository contents including dgvdt/ and dgvdt_tests.py

• supportsDG-VDT uses multi-graph reinforcement learning for Ethereum vulnerability detection and attacker traceability: SolidiFI-Bench, SmartBugs Curated and SmartBugs Wild are third-party corpora independent of the DG-VDT authors, but their use does not constitute independent reproduction of DG-VDT: the reported runs and metrics are still produced by the authors, while the public repository does not yet include the complete experimental pipeline or pretrained weights.

  Dataset Information; Code Information; Usage Instructions item 3 listing experimental artifacts not yet publicly released