Ethos

As an artist, it's very common for people to scam your works, like not paying for them or blocking you after you make them. Mostly fake artists will steal your arts. My art studio, called Guhit Art Studio, is a victim of those, and having an ethos is such a big help no matter how many times bad actors change their usernames, our review for their accounts will stay there forever! Just recently, I used the slashed feature where I slashed one guy after he stole our arts several times. Now, plenty of people are aware of his scamming tactics.

Ethos come to validate the people's credibility. No other projects have done this before and Ethos easily being a leader in this field. Followed with strong reputable founder and solid community members, Ethos evolving being the most wanted project to get interacted with. Personally, i can avoid interacting with shady or scam project by reviewing their Ethos score

Ethos is basically about who you are and what you stand for. It’s your values, your reputation, and the trust people have in you. People can feel if you’re real or not, especially in Web3.

→ → → [CLICK HERE to go vote](https://snapshot.box/#/s:ethos-labs.eth/proposal/0x7445c479583253682aba38d4147376ad6a73a58f85047e157638a9b7aac53e9d) ← ← ← ## Summary This proposal extends the existing Review-for-Review (R4R) neutralization to detect coordinated review cycles beyond direct reciprocity. Currently, only direct A↔B reciprocal reviews are detected and downweighted. This upgrade adds detection of triangle cycles (A→X→B→A) and rectangle cycles (A→X→Y→B→A), catching more sophisticated coordination patterns while preserving the existing unlock model. As part of this update, the maximum review score contribution is also reduced from 540 to 400, with redistributed weight flowing to vouch-based signals. ## Motivation The current R4R detection only catches the simplest coordination pattern: two users directly reviewing each other. In practice, coordinated groups have adapted by routing reviews through intermediaries: - **Triangles:** Alice reviews Bob, Bob reviews Carol, Carol reviews Alice — each gets a positive review without any direct reciprocation. - **Rectangles:** Similar pattern through two intermediaries, making the coordination even harder to spot. These patterns inflate scores without genuine community endorsement. Our preview data shows meaningful score corrections when cycle detection is enabled, particularly for users with high review counts and low independent engagement. We have noticed many profiles that have achieved scores that are high simply through reviewing in massive groups, and they offer 0 economic security to the scores, so we want to change that. This should also help with the idea of "global" versus "local" reputation — cycles are more likely to appear when someone is only reputable from a singular group of people supporting them (even organically) but less likely when someone has reputation from multiple sources. ## Specification 1. **Cycle detection depth:** Increase `maxCycleLength` from 2 (direct only) to 4 (direct + triangles + rectangles). Reviews that form part of any cycle up to length 4 are classified as reciprocated. 2. **Positive-only filtering:** Only positive reviews are eligible for cycle classification. Negative and neutral reviews are always treated as external. This ensures the system penalizes coordinated *inflation* without discounting legitimate retaliatory or neutral reviews. 3. **Unlock model preserved:** The existing R4R unlock model still applies — the first 10 reciprocated reviews always count, and each external review unlocks one additional reciprocated review. Reviews beyond the unlock threshold receive zero weight. 4. **ELO and sentiment modifiers:** All existing review weighting mechanisms (ELO-based author/subject score ratio, community vote sentiment modifier) continue to apply on top of cycle detection. 5. **Score weight rebalancing:** The maximum review score contribution is reduced from 540 to 400. This reclaimed weight is redistributed to vouch-based signals — the vouched ETH ceiling increases from 280 to 390, and the voucher count ceiling increases from 270 to 300. This rebalancing reflects the reduced signal value of reviews in high-cycle environments and strengthens the economic security of the overall score. ## Collusion Resistance Each extension of cycle detection increases the coordination cost required to inflate scores. The table below shows the optimal evasion strategy at each detection level, using a scenario where a coordinated group wants to deliver 100 undetected positive reviews to a single person: Under the current system, a group of 200 colluders can split into two groups that review each other without any direct reciprocation, fully evading detection. With EIP-14, that same 2-group strategy is caught through triangle and rectangle detection. To evade, groups must route reviews through chains of 5+ participants, requiring 500 people to achieve the same 100 undetected reviews — a 5x increase in coordination cost. The coordination cost scales sharply: not only do you need more people, but each additional participant must maintain a consistent positive review chain across multiple hops, making the scheme increasingly fragile and detectable through other signals. ## Impact Based on preview data across all profiles: - Scores only decrease or stay the same relative to production (no artificial boosts) - Users with no cycle involvement see zero change - The largest corrections apply to users with extensive reciprocated review networks - Non-profiled users (those without an Ethos profile) are unaffected — they retain production behavior, which gives them further advantage (scores like Vitalik, Cobie, and ZachXBT will remain high) - Score ceilings are rebalanced to reflect signal quality: review weight decreases (540→400) while vouch-based signals increase (vouched ETH 280→390, voucher count 270→300), improving resistance to review-only score inflation ## Future Considerations - Extend cycle detection beyond 4, to pentagons and beyond, as we notice more collusion. - Extend this type of detection to vouches ## Rollout This change will be deployed with a 60 day gradual rollout period, but may be updated to a shorter period if the team decides to do so.

Please use this to test and ensure you can vote your opinion on future Ethos changes. Just make sure you vote correctly, anon.

Vote at [To be filled] #EIP-3: Sequential Review Eligibility This EIP proposes an update to the review impact calculation by introducing a sequential, daily eligibility mechanism for reviews. The core principle is that an author's reviews become active and contribute to scoring one at a time, on a per-calendar-day basis, following the sequence of their creation. This measure aims to prevent an excessive number of reviews from a single author from disproportionately and immediately influencing a population's reputation score, promoting a more metered and organic impact of one's public opinions. If an author writes many reviews within a short period of time, their impact on any given subject (and the overall system) is spread out, making the reputation system more resilient to bursts of activity and encouraging sustained, thoughtful engagement. We do not discourage authors from leaving 100s of reviews at once; it may even be an efficient use of their time to frontload their reviews. Reviews will continue to show on the profile page of the review subject. However, the score impact of those reviews will cascade to avoid surges and manipulation. ##Motivation There are now individuals who are selling Ethos reviews in order to boost the recipient's score quickly. That is not forbidden, but it does introduce perverse incentives. This change makes such manipulation more costly by limiting the ability of the seller to spam score-influencing reviews. This enforces the notion of economic security; we cannot eliminate the ability to buy one's reputation, but if it has a high cost, then it still acts as a valuable signal. Furthermore: having purchased reviews from a known seller will be permanently recorded on the blockchain, even should those reviews be later archived. There are communities which are now "gaming" the Ethos scoring system by leaving a massive number of reviews within days of joining. While we encourage people to review their friends and communities, this should not allow a single community to immediately attain reputable status. ##Specification The EIP-3 eligibility algorithm introduces the following rules for reviews when calculating their impact on a subject's score: 1. Chronological Ordering: For each review author, all their reviews are collected and sorted chronologically by their creation timestamp (createdAt). 2. Eligibility Date Calculation: 1. For each review by the same author: 1. If there is a not NextAvailableReviewDay , or the NextAvailableReviewDay is in the past, the NextAvailableReviewDay is set to today. 2. The review becomes active on the NextAvailableReviewDay . 3. The NextAvailableReviewDay increases by one. 3. Active Review for Scoring: A review is considered "active" and included in the score impact calculation for a subject once the review passes the associated AvailableReviewDay ## Rationale * Rate Limiting: This mechanism naturally rate-limits the impact of an individual author's reviews, ensuring a more gradual influence. * Determinism: The algorithm is deterministic, making score calculations predictable. * Synergy with EIP-1: This proposal works in conjunction with EIP-1's review quality measures. While EIP-1 focuses on the quality and sentiment of individual reviews, EIP-3 focuses on the timing and velocity of their impact. ## Considerations Although we will conduct a vote for implementing this EIP, the discussion will also determine if we apply any of the following considerations, or new suggestions not yet considered. * This algorithm could be modified to introduce exponential or multiplicative backoff. For example, the NextAvailableReviewDay could multiply by 1.5x for each review, instead of increasing linearly. It could also follow a bonding curve, increasing at a predetermined non-linear rate. * We could modify the number of reviews per day locally; 2 reviews per day, or globally; 1 review per Ethos user per day. * We could allow authors to archive existing reviews, allowing later reviews to apply earlier, essentially reducing the queue. ## Example Scenarios * Scenario 1: Author A writes 50 reviews for various subjects on Day 1. * Outcome: Their first review (chronologically) becomes active Day 1, the second Day 2, ..., the 50th on Day 50. * Scenario 2: Author B writes Review 1 (R1) on Day 1. They then write Reviews 2, 3, and 4 (R2, R3, R4) on Day 15. * Outcome: * R1 is eligible on Day 1. * R2 (created Day 15): is eligible on Day 15. * R3 (created Day 15): is eligible on Day 16. * R4 (created Day 15): is eligible on Day 17. ## Backwards Compatibility All applicable weighting from previous EIPs and review impact algorithms will continue to apply: 1. From all "active" reviews a specific author has written for a particular subject, only the latest active review is considered for score impact calculation. 2. The weighting of this latest active review (based on author score, subject score, ELO, and vote sentiment modifiers as established in EIP-1) remains consistent with previous versions. ## Security Considerations This change is designed to improve the robustness of the reputation system against manipulation by high-velocity review submissions. By metering the impact, it makes it harder for any single author to cause rapid, unmerited swings in reputation scores. No new direct smart contract vulnerabilities are introduced, as this is an off-chain calculation logic update.

Ethos is the best platforms I have experienced in Web3 as it It focuses on reputation and credibility for crypto. What stands out is how it highlights real humans while actively fighting sybil behavior. It makes it much easier to separate trustworthy projects from scammed ones. With a fast-growing community and a clear mission to bring the internet alive again, Ethos is building trust where it truly matters.

Ethos started with a great idea that reputation in crypto must matter to distinguish between real people and bots. I believe this will matter even much more as we go further

Ethos feels like something different in web3 where identity and reputation actually matter. Instead of just wallets and transactions it tries to bring a human layer into crypto. Reviews vouches and profiles make it easier to understand who you are dealing with. Still early and not perfect but the idea makes sense. If they keep improving and stay transparent this could become something really useful for the whole space.

Original tweet saved by @mrdejie: "EIP-3.1 was approved and is now LIVE on Ethos 🚨🚨 Going forward, if someone you review positively OR have vouched for is slashed successfully, you WILL lose credibility score of your own This includes ongoing slashes for @JamesWynnReal & @blast ↓ https://t.co/W3u8TjLAs3 https://t.co/P1qNZ1yn5S" Authored at: 2025-06-04T04:36:36.000Z Author user id: 1738066467558395904 Link to tweet: https://x.com/ethos_network/status/1930121484959138136