Designing an optimal reputation & weighting system for PeerBench

[mistrz-g]

Hi all — I’d like to kick off a focused discussion on the reputation and weighting mechanisms in PeerBench. The paper proposes a practical prototype with three leaderboards (Data Contributors, Reviewers, Models) and a lightweight reputation + slashing economy; I’ll briefly summarize the original design, call out potential risks, and propose concrete questions so we can iterate on a safer, more robust design together.

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1. Brief Summary of the Original Design (from the paper)

Three leaderboards

• Data contributor leaderboard: ranks contributors by cumulative test quality + verification bonuses.
  ContributorScore(c) = Σ quality(T_i^(c)) + bonuses
• Reviewer leaderboard: ranks reviewers by correlation with consensus quality.
  ReviewerScore(r) = Pearson({q(i)_r}, {q(i)})
• Model leaderboard: model score is a reputation-weighted average of per-test scores.
  ModelScore(m) = (Σ_i w(T_i) * s_i(m)) / (Σ_i w(T_i))

Weight calculation for each test combines measured test quality (peer reviews) and contributor reputation:
w(T) = max{0, 0.7 * quality(T) + 0.3 * min(2, ρ_c / 100)}

Temporal fairness / scheduling options:

• Immediate scoring on request (fast, but incomparable across time and vulnerable to contamination).
• Synchronized cohort evaluation (fairer cohort comparisons; slower).
• Proposed approach: hybrid — a portion of data for immediate scoring, some for synchronized windows.

Workflow highlights

• Contributors submit tests and a scoring function, commit a hash, reviewers rate tests (≥3 reviews).
• Tests live in a reservoir of size k; low-weight or oldest tests are retired and published.
• Reputations are updated and collateral may be slashed for misconduct.

This is intentionally short, for detailed version and rationale please check the original paper: link

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2. Potential Risks & Failure Modes in the Proposed Scheme

1. Reputation capture & Sybil/scale attacks
A single actor can create many identities to inflate reviewer scores or contributor reputation. Reputation may also centralize influence over time.

2. Collusion & targeted cherry-picking
Contributors and reviewers could coordinate to craft tests favoring certain models or to boost each other’s scores.

3. Incentive misalignment
Contributors may chase easy, high-consensus tests; reviewers may herd toward consensus instead of accurate judgments.

4. Weighting function brittleness
The fixed 0.7/0.3 split may be brittle; small behavior changes could disproportionately influence overall model scores.

5. Temporal fairness & comparability
Immediate scoring can lead to cohort drift, while synchronized cohorts reduce system responsiveness.

6. Reviewer bias & calibration drift
Pearson correlation may reward agreement with consensus and penalize valuable dissent.

7. Economic centralization & slashing risks
Collateral requirements and slashing could disproportionately affect smaller actors.

8. Sealed sandboxes
The paper only briefly mentions the concept without implementation or architectural details.

9. Data leakage via endpoints
Running tests against live model endpoints may leak prompts or content into training data.

10. UX & participation friction
Complex staking, verification, and review processes may reduce participation.

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3. Key Questions for Community Input

To keep this discussion focused, here are the core questions we should answer first:

A. Reputation

1. Should contributor/reviewer reputation use additive updates, Bayesian scoring, or capped/decaying influence?
2. What minimal anti-Sybil measures should PeerBench adopt (verification, staking, web-of-trust, etc.)?

B. Weighting

3. Is the current 0.7 * quality + 0.3 * reputation weighting appropriate, or should we consider alternatives like capped influence, nonlinear scaling, or robust aggregation?

C. Reviewer Scoring

4. Is Pearson correlation the right metric for reviewer quality, or should we consider rank correlations, calibration tasks, or gold-item checks?

D. Fairness & Scheduling

5. How should we balance immediate scoring vs synchronized cohorts to preserve comparability without hurting liveness?

E. Collusion & Abuse Resistance

6. What practical and lightweight mechanisms should detect collusion without over-penalizing honest coordination (e.g., shared labs, teams)?

F. Sealed Sandboxes

7. What should the “sealed sandbox” actually be in practice?

• Should PeerBench use VMs, containers, TEEs (e.g., SGX/SEV), or offline execution?
• What level of network isolation is required to prevent data exfiltration or test leakage?
• Should the sandbox support reproducibility through deterministic execution?

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Your thoughts, critiques, and alternatives are very welcome. Even short comments or parameter suggestions are useful. Let’s collaborate on a reputation and weighting design that is robust, fair, and transparent for everyone using PeerBench.