Research area · Decentralised systems
Protocol claims are only as credible as the assumptions underneath them
My decentralised-systems papers propose mechanisms and formal models for physical-infrastructure networks, transaction ordering and cross-rollup execution. The useful question for a protocol team is what formal and empirical work would make a mechanism's claims believable.
The situation
We need to assess the assumptions in an infrastructure incentive mechanism. What formal and empirical work would make the claims more credible?
What you leave with
Protocol modelling and research collaboration: written assumptions, a model of the participants, an experiment or proof, and stated limitations.
For: Protocol R&D leader; academic researcher
Choosing a route
The two narrower routes below share a method but answer different questions.
| If the claim is about… | The core uncertainty is… | The evidence that helps most |
|---|---|---|
| Rewards that pay independent operators to provide physical infrastructure | Whether rational, and some irrational, participants produce the intended service at the intended cost | An agent model, sensitivity analysis over its parameters, and a reproducible simulation |
| Transaction ordering, extraction or cross-rollup execution | Whether fairness or atomicity holds when someone controls ordering, timing or a sequencer | Formalised assumptions, a search for counterexamples, and adversarial evaluation |
If your mechanism has both — say, an infrastructure network whose rewards are settled through a shared sequencer — start with whichever assumption you are least willing to defend.
What credible protocol evidence looks like
Most protocol documents state what a mechanism is designed to do. Fewer state the conditions under which it does so. A research engagement in this area produces, at minimum:
- Assumptions — participant types, information each sees, costs, latency, trust in oracles or sequencers.
- A model — those assumptions encoded so they can be varied, not just described.
- An experiment or proof — simulation across parameter ranges, or a formal argument, aimed at the claim.
- Limitations — what the model leaves out and how that could change the conclusion.
Analysis plans are fixed before results where possible (see pre-specified evaluation plans).
Decentralised deepfake detection
Decentralized Deepfake Detection Blockchain Network using Dynamic Algorithm Management (preprint, 2023) proposes a network in which several detection algorithms are deployed side by side, evaluated, and added or retired as generation techniques change, with a transparent record of that process. The open questions it leaves are evaluation questions: how detectors are scored without a fixed benchmark that attackers can learn, and what incentives keep evaluators honest. Those are the same assumption-model-experiment questions as the protocol work above, applied to media integrity.
Related work
Centralized Intermediation in a Decentralized Web3 Economy examines how value accrues to intermediaries even on decentralised infrastructure — a reminder that a protocol’s decentralisation does not guarantee decentralised economic outcomes. Epistral Network, co-authored with S Upadhyay, proposes a decentralised framework for media curation.
Useful if
- A protocol team has a mechanism whose claims rest on how participants behave and wants those assumptions made explicit and tested.
- An academic group wants a collaborator on mechanism modelling, simulation or formal specification.
- A sponsor needs an independent reading of a protocol design before funding further work.
Not the right fit if
- You need a token launch, smart-contract audit or security sign-off. Those are different services with different evidence standards.
- You want a whitepaper written to support a fundraise. Research here may conclude the mechanism does not behave as intended.
- You need a protocol built or operated (see the link below).
Limits and unfavourable results
- All of my papers in this area are arXiv preprints from 2023–2024. None has been through peer review, and no code artefact is listed for them.
- The papers propose frameworks and mechanisms. They are starting points for evaluation, not evidence that any deployed network behaves as intended.
- Market and adoption figures move quickly in this field; I do not make claims about any specific live network's economics.
Evidence behind this page
- Generalised DePIN Protocol: A Framework for Decentralized Physical Infrastructure Networks
2023 · Preprint (not peer reviewed) · cited by 9 (Google Scholar, October 2026)
- FairFlow Protocol: Equitable Maximal Extractable Value (MEV) mitigation in Ethereum
2023 · Preprint (not peer reviewed) · cited by 3 (Google Scholar, October 2026)
- Towards Universal Atomic Composability: A Formal Model for Multi-Rollup Environments on Ethereum
2023 · Preprint (not peer reviewed) · cited by 2 (Google Scholar, October 2026)
- Centralized Intermediation in a Decentralized Web3 Economy: Value Accrual and Extraction
2023 · Preprint (not peer reviewed) · cited by 2 (Google Scholar, October 2026)
- Decentralized Deepfake Detection Blockchain Network using Dynamic Algorithm management
2023 · Preprint (not peer reviewed) · cited by 7 (Google Scholar, October 2026)
- Epistral Network: Revolutionizing Media Curation and Consumption through Decentralization
2024 · Preprint (not peer reviewed)
Narrower routes from here
Research topic
DePIN Mechanism and Incentive Evaluation
Test a DePIN incentive mechanism with stated assumptions, an agent model, sensitivity analysis and a reproducible simulation.
Research topic
Composability, MEV and Protocol Mechanisms
Formalise the assumptions behind fairness, MEV and cross-rollup atomicity claims, then search for counterexamples.
Questions
What makes a protocol incentive claim credible?
Four things written down: the assumptions about participants and the environment, a model that encodes them, an experiment or proof that tests the claim within the model, and the limits of what that shows about the real system.
Are these papers peer reviewed?
No. The DePIN, FairFlow, atomic composability, intermediation, deepfake-detection and Epistral papers are arXiv preprints. Their publication pages show status and citation counts.
What is the Decentralized Deepfake Detection paper about?
It proposes a blockchain-based network in which multiple deepfake detection algorithms can be deployed, evaluated and updated over time, with a transparent record of how detection is managed. It is a 2023 preprint and a design proposal rather than a benchmark result.
Do you work on DePIN and MEV separately?
They share a method — explicit assumptions, an adversary or agent model, then simulation or formal analysis — but answer different questions. DePIN work is about whether incentives produce the intended physical service; composability and MEV work is about ordering, atomicity and extraction under adversarial execution.
See also
Method
Pre-Specified Evaluation Plans
Versioned research question, primary outcomes, exclusion and analysis rules, and an amendment log, fixed before results.
Method
Research Reproducibility Packages
Environment manifest, inputs with provenance, per-table scripts, raw per-run outputs and a known-limits statement, with worked examples.
Collaboration
Industry-Academic AI Research Collaboration
Map a shared research question, split contributions and settle data and publication dependencies before the work starts.
Papers & Artefacts
Every paper with its publication status, co-authors, code and data.
Bring a protocol research question
Describe the mechanism, the claim it is meant to support and the assumptions you are least sure of. A public design document or a short summary is enough to start.
Last reviewed 2026-10-07.