Prove the value. Protect the knowledge.
Our mission is to help researchers demonstrate the value of their work and receive payment before revealing the knowledge behind it. This is our approach to Arrow’s information paradox.
LONG-TERM MISSIONNOT A CLAIM OF A SOLVED PROBLEM
A buyer needs to see an idea to assess its value. Disclosure can give it away.
Arrow’s information paradox, also known as the disclosure paradox, describes a problem in selling knowledge. A buyer needs enough information to judge its value. But revealing that information can give the buyer the knowledge before the seller is paid.
For researchers, that tension is concrete. A useful dataset, method, or hypothesis may be valuable precisely because it has not been widely revealed. Collaboration and compensation can require exposure before terms are settled.
Membra’s research thesis is to explore whether protected evaluation, evidence about agreed computation, and conditional settlement can address this tension for carefully defined tasks.
Read about Arrow’s information paradoxEvaluate the work. Protect the contribution.
This proposed workflow shows how a researcher and funder could agree on an evaluation, inspect execution evidence, and settle payment while limiting what is disclosed.
[ method + dataset withheld ]Private contribution boundaryCheck the specified artifact against an agreed test.
Agreed disclosure boundaryAgree on the question. Protect the contribution.
A researcher supplies private methods or data to a proposed protected execution route. The researcher and funder agree on an evaluation rule and the information that may be released.
Progress requires more than a private model.
Compensate useful work.
Define a task, a payment rule, and measurable acceptance conditions before a contribution is evaluated. Avoid treating an AI score as a reliable measure of scientific value.
Limit what must be disclosed.
Specify protected inputs and permitted outputs. Confidential execution cannot undo knowledge already disclosed, and outputs themselves may leak information.
Check the claim in scope.
Verify stated execution properties and evidence coverage. Hardware trust roots, software correctness, data provenance, and scientific validity remain distinct questions.
The research ahead. The questions still open.
We do not claim a general solution to Arrow’s paradox. The goal is to make progress for defined workflows, with explicit assumptions and measurable guarantees.
- How can a useful evaluation be defined without leaking the contribution?
- What evidence covers the full execution path, rather than only a gateway?
- How are honest payment, controlled release, and disputes handled together?
- How do repeated queries and model outputs affect confidentiality?
- How can execution evidence be combined with reproducibility and independent scientific review?
Decentralized, autonomous agents are part of this direction. Membra’s protocol and app are in development; protected research execution, conditional payments, and autonomous scientific agents are not deployed.
See how V1 leads into the research direction