Research foundations
Connect the mining-intelligence roadmap to a specific research source without treating a Bitcoin model as a validated Zcash strategy.
A research reference and validation agenda; no paper-derived mining strategy is deployed.
Referenced paper
Hasan Selçuk Kılıç and Nail Akar. Capital Costs Protect the Network: Mining Gap Dynamics in Proof-of-Work Blockchains Under a Stochastic Mempool. arXiv:2610.22451v1, submitted 15 October 2026.
The paper studies delayed mining while transaction fees accumulate. Its model combines a two-tier fee market and stochastic backlog with Bitcoin’s difficulty-adjustment setting. Under its assumptions, capital costs constrain equilibrium hashpower and limit the mining gap. This is a conditional theoretical finding, not a claim that any particular rental is profitable.
Oreloom research questions
Oreloom’s proposed research program uses the paper as a starting point for questions, not as a ready-made decision engine.
- How should owned-equipment capital costs and rented-hashpower commitments be represented separately?
- How sensitive are modeled outcomes to fees, subsidy, difficulty, and delivery interruptions?
- Can observed idle time be attributed to an economic mining gap, rather than downtime or missing telemetry?
- Which model outputs remain useful once uncertainty and operational constraints are included?
Validation before Zcash use
The model’s Bitcoin setting and homogeneous operating-cost baseline need re-examination before a Zcash application. Oreloom has not validated the paper’s conclusions for Zcash.
A Zcash adaptation would need network-specific consensus and reward rules, observed transaction-fee behavior, pool arrangements, equipment units, and rental constraints.
Research-to-production gates
- Document the paper version, equations used, assumptions, units, and deviations.
- Construct reproducible datasets with timestamps and source-quality checks.
- Validate the Zcash adaptation against historical records and separate holdout periods.
- Publish sensitivity results, errors, known limitations, and conditions where the model should abstain.
- Keep any future execution workflow subject to explicit budgets, authorization, monitoring, and receipts.