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ARXIV:2604.04211 · BLOCKCHAIN AI TOOLS · SUBMITTED 07 APR · 20:11 UTC · FRESHNESS UNKNOWN
ARXIV:2604.04211BLOCKCHAIN AI TOOLSSUBMITTED 07 APR · 20:11 UTCFRESHNESS UNKNOWNXiaohang Yu · William Knottenbelt · arXiv
LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing.
Opportunity summary
Pain LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing.
Evidence 0 refs | 0 sources | 0% coverage
Blocker Evidence unverified
LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process.
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a…
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross-chain transaction tracing. A public repository is…
Blockchain AI Tools moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
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LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing.
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Paper Pack
10.48550/arXiv.2604.04211LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing.
Abstract
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process. To instantiate this paradigm, we introduce LOCARD, the first agentic framework for blockchain forensics. LOCARD operationalizes this perspective through a Tri-Core Cognitive Architecture that decouples strategic planning, operational execution, and evaluative validation. Unlike generic LLM-based agents, it incorporates a Structured Belief State mechanism to enforce forensic rigor and guide exploration under explicit state constraints. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross-chain transaction tracing. We introduce Thor25, a benchmark dataset comprising over 151k real-world cross-chain forensic records, and evaluate LOCARD on the Group-Transfer Tracing task for dismantling Sybil clusters. Validated against representative laundering sub-flows from the Bybit hack, LOCARD achieves high-fidelity tracing results, providing empirical evidence that modeling blockchain forensics as an autonomous agentic task is both viable and effective. These results establish a concrete foundation for future agentic approaches to large-scale blockchain forensic analysis. Code and dataset are publicly available at https://github.com/xhyumiracle/locard and https://github.com/xhyumiracle/thorchain-crosschain-data.
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Proof status
unverified0 refs; 0 sources; 0% coverage.
What was readable
Derived fallback: Estimated from adjacent evidence; not verified from source.
Viability
Time to MVP
Commercial
Export
Preparing verified analysis
Dimensions overall score 8.0
PROBLEM
LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process.
METHOD
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a seque...
RESULT
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross-chain transaction tracing. A public repository is linked, so build verification can inspect imple...
WHY NOW
Blockchain AI Tools moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Blockchain forensics inherently involves dynamic and iterative investigations, while many existing approaches primarily model it through static inference pipelines. We propose a paradigm shift towards Agentic Blockchain Forensics (ABF), modeling forensic investigation as a sequential decision-making process.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 8.0/10 on the public viability pass. To demonstrate the efficacy of the ABF paradigm, we apply LOCARD to the inherently complex domain of cross-chain transaction tracing. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Blockchain AI Tools moved forward this cycle; last verified April 2026. Public score 8.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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Concepts
Methods
Materials
Markets
Competitors
LOCARD empowers blockchain forensic analysts with an agentic framework for high-fidelity cross-chain investigation and tracing.
Segment
Blockchain AI Tools
Adoption evidence
Public code linked for build inspection
Commercial read
8.0/10 public viability
Direct
Adjacent
Substitute
Unknown
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CITED BY
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Extension
Commercially relevant
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Build Passport
Build passport pending - Proof Lab budget No verified cost estimate / $7.00 cap
status
missing
reason
passport_row_missing
proof status
unverified
cost/budget
No verified cost estimate
confidence low
next verification path
Build brief missing until Build Passport data exists.
Source missing: Build Passport payload.
Experiment plan missing until prototype path is available.
No prototype path attached.
Validation checklist missing until required assets, cost, and regulatory flags are verified.
No checklist artifact is attached to the Build Passport payload.
Derived signals show verified:false until source-backed receipts exist.
Evidence coverage
OpportunityKernel evidence_receipt
0 refs / 0 sources / 0% coverage
unknown
Verify missing sources before using this as buyer proof. verified:false
Build readiness
BuildPassport EvidenceState
passport absent
unknown
Run Proof Lab or inspect typed missing state. verified:false
Artifact maturity
GitHub and Hugging Face maturity payloads
No public artifact surface observed
unknown
Open source artifacts or mark the gap as missing. verified:false
Technical feasibility
partial
Current read
Runnable path is not fully verified.
Evidence
No Build Passport payload attached.
Gaps
Next test
Run minimal reproduction from the Build Passport prototype path.
Market urgency
missing
Current read
Buyer urgency is not verified from source.
Evidence
0 references, 0 sources, 0% evidence coverage.
Gaps
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Collect buyer interview, deployment evidence, or cited demand signal.
Buyer clarity
missing
Current read
No budget owner is verified for this paper.
Evidence
Build tab has no CRM, procurement, or operator source.
Gaps
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Map target operator, economic buyer, and procurement trigger.
Defensibility
missing
Current read
Defensibility signals are missing.
Evidence
No defensibility receipt attached.
Gaps
Next test
Refresh defensibility bars with source receipts.
Integration burden
missing
Current read
No public implementation surface observed.
Evidence
No GitHub or Hugging Face payload attached.
Gaps
Next test
Write integration checklist from prototype path and target workflow.
Capital intensity
missing
Current read
No observed cost estimate is verified.
Evidence
Cost passport has no observed_usd value.
Gaps
Next test
Run cost passport or mark the cost field not applicable.
Regulatory load
missing
Current read
No regulatory classification is attached.
Evidence
Build Passport ledger does not include regulatory flags.
Gaps
Next test
Classify regulatory flags before commercialization planning.
No named scientific founder assigned.
Paper authors are not treated as operators without consent.
People
No named person assigned.
Gaps
Next verification path
Prototype owner missing.
Build Passport does not name an implementer.
People
No named person assigned.
Gaps
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Operator workflow not sourced.
No buyer or workflow interview attached.
People
No named person assigned.
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No GTM owner verified.
No CRM or outreach source attached.
People
No named person assigned.
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Regulatory need unclassified.
No clinical or regulatory source attached.
People
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Gaps
Next verification path
ARTIFACTS
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DEFENSIBILITY
Defensibility and confidence evidence pending.
WATCHTOWER
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FORESIGHT
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OPPORTUNITYKERNEL CHANGES SINCE LAST VIEW
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RELATED PAPER UPDATES
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TIMELINE
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BUZZ
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