# Stake-Assured Human Feedback

Stake-Assured Human Feedback is Reppo's mechanism for turning economic conviction into usable training signal.

It extends vote-escrowed token design beyond governance and applies it directly to publishing, curation, and market discovery.

Stake-Assured Human Feedback does not treat locked capital as a substitute for domain knowledge.

It is a way to make curation economically accountable inside a market with explicit task design, access rules, and quality standards.

#### How it works

1. **Lock REPPO → receive veREPPO**
   * Voters lock **$REPPO** for a chosen duration.
   * In return, they receive **veREPPO**, which represents voting power.
   * The larger and longer the lock, the more voting power they receive.
2. **Allocate voting power across datanets**
   * veREPPO holders vote during each epoch.
   * They can support or oppose markets and submissions based on expected usefulness.
   * Voting power decays linearly during the epoch, so earlier conviction carries more weight.
3. **Generate structured preference data**
   * Every vote, ranking decision, and market adjustment produces curation signal.
   * That signal becomes usable data for training, evals, and benchmarking workflows.
4. **Reward useful participation**
   * Rewards can come from network emissions and datanet-level incentive programs.
   * Exact economics depend on how the datanet is configured.
5. **Adjust votes, not the lock**
   * Voters can reallocate across epochs.
   * The underlying REPPO stays locked for the selected term.

#### Where expertise enters

Expertise enters at the datanet level, not from stake alone.

Each datanet owner chooses the task, access rules, incentive design, and acceptance standards.

That means a datanet can be open, curated, or restricted to a narrower expert set.

In specialized markets, stake backs judgment among eligible participants.

It does not create expertise where none exists.

#### Key properties

* **Stake-backed curation:** influence comes from locked capital, not passive attention.
* **Expertise-aware by design:** datanet owners decide who can participate and what quality bar applies.
* **Preference strength:** votes express not just direction, but economically weighted conviction.
* **Early signal discovery:** linear decay rewards acting early, not just following momentum.
* **Flexible markets:** datanets share one core mechanism but can set different rules and incentives.
* **Anti-farming pressure:** locking and epoch-based participation make short-term extraction harder to sustain.

#### What the mechanism does not claim

It does not claim that open token markets are the right fit for every workflow.

It does not claim that capital concentration automatically reveals truth.

It claims that, in markets with good task design and credible participation rules, stake-backed competition can surface useful signal faster than flat-rate annotation alone.


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://reppo-labs-xyz.gitbook.io/reppo-labs/protocol-mechanics/stake-assured-human-feedback.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.