Currently, there are two key shortcomings restricting the vitality of the DeFi fixed income ecosystem: First, asset scenario adaptation is 'passively lagging'; most projects only adjust rules for tAssets and RWA after user needs are clarified, unable to anticipate changes in demand—when users plan to shift from 'short-term liquidity allocation' to 'quarterly lock-up', they must wait for the demand to occur and then manually operate, missing the optimal yield window; Second, the co-creation value is 'fragmented and dispersed'; user contributions to similar projects mostly consist of scattered suggestions, making it difficult to aggregate into comprehensive solutions, and the value of a single contribution is limited, failing to generate a '1+1>2' amplification effect, leading to weak user co-creation motivation.

TreehouseFi breaks the passivity of response with 'asset forward adaptation' and activates fragment contributions through 'co-creation value fusion', building a more foresighted and collective intelligence-releasing DeFi fixed income ecosystem through two major innovative mechanisms.

1. Asset Forward Adaptation: Allow assets to align with users' 'future needs' in advance

TreehouseFi abandons the traditional logic of 'adjusting after demand occurs', designing a three-layer mechanism around 'foresight', to predict demand based on user historical behavior, and optimize asset rules in advance to seize allocation opportunities.

1. Demand Prediction Model

The project builds a 'user demand prediction model', mining demand trends through historical data, replacing 'real-time waiting behavior':

• Core input dimensions for the model: asset holding period (the last 3 allocations exceed 60 days, predicting 'long-term allocation tendency'), yield preference (continuous selection of high-yield RWA, predicting 'increased risk preference'), scenario-related behaviors (new local lending after cross-border allocation, predicting 'cross-scenario combination demand');

• Users do not need to actively provide information; the intelligent contract analyzes historical on-chain data to generate 'demand prediction labels' within 24 hours and marks the confidence level of the prediction (e.g., 'long-term allocation tendency - confidence 90%');

• The prediction logic (e.g., the mapping relationship between cycles and tendencies) is open and transparent; users can view their own prediction labels and the basis for generation, and manually correct any deviations to ensure prediction accuracy.

2. Foresight Parameter Pre-set

Based on demand prediction labels, the project designs a 'foresight parameter preset mechanism' to configure asset rules for users in advance, which take effect directly when demand occurs:

• If the prediction indicates a 'long-term allocation tendency', the system pre-sets the 'long-term lock-up yield tier' of RWA into the user's account 7 days in advance—when users later choose to lock up, they can enjoy the tiered yield without reapplying, and gain an additional 0.2% annualized bonus compared to temporary adjustments;

• If a 'cross-scenario combination demand' is predicted, the system pre-sets 'cross-scenario asset mutual recognition permissions' in the user's account in advance—when users transfer tUSDC from the cross-border scenario to the local lending scenario, they do not need to lift the original pledge, directly reusing pledged credit, saving operational time;

• The preset parameters are only in a 'waiting for activation state'; if users do not trigger the corresponding demand, they do not take effect and incur no additional costs, preventing resource waste.

3. Prediction Verification Optimization

The project establishes a 'prediction-verification-optimization' closed loop to continuously improve prediction accuracy through actual demand feedback:

• After demand occurs, compare the matching degree between 'prediction labels' and 'actual needs' (e.g., if the prediction is 'long-term allocation' and the user actually chooses to lock up, it is deemed 'matching successful'); if the matching rate is below 80%, the model optimization is triggered;

• Regularly collect user feedback on predictions (e.g., 'whether demand was anticipated in advance' and 'whether preset parameters met expectations'), and adjust the weight of model input dimensions based on matching data (e.g., increase the weight ratio of 'scenario-related behaviors');

• A 'prediction optimization report' is published each quarter, disclosing the matching rate, user feedback handling, and the next adjustment direction, ensuring the mechanism continuously aligns with user needs.

2. Co-creation Value Fusion: Allow scattered contributions to 'aggregate into momentum'

TreehouseFi breaks the limitations of 'isolated single contributions', designing a 'value fusion' system that aggregates users' scattered co-creation contributions into complete solutions, and generates additional gains post-aggregation, releasing the value of collective intelligence.

1. Contribution Fragment Aggregation Module

The project develops a 'co-creation contribution aggregation module' to transform users' scattered suggestions (e.g., 'tUSDC redemption rule optimization points' and 'RWA risk buffer suggestions') into aggregable 'contribution fragments':

• Fragments are categorized by 'co-creation themes': for example, under the theme of 'asset forward adaptation', fragments include 'supplementary dimensions for prediction models' and 'suggestions for preset parameter adjustments', with users' scattered suggestions automatically categorized into the corresponding theme;

• The module supports 'fragment stitching': when the number of fragments under a certain theme ≥5 and covers 'problem description - solution - implementation details', it automatically stitches into a 'complete co-creation solution', with the stitching process aided by AI to fill in logical gaps and ensure feasibility;

• Both fragments and complete solutions are stored on-chain for verification, allowing contributors to see their own fragment's position and role in the solution, clarifying contribution value.

2. Fusion Gain Distribution

For the complete solutions formed from aggregation, the project designs a 'fusion gain distribution mechanism', allowing participating users to share additional value:

• After the solution is implemented, in addition to basic co-creation rewards, an additional 5% of the 'fusion gain' (the increase in scenario revenue post-implementation) is extracted and distributed based on fragment contribution—core fragments (e.g., solution proposers) account for 40%, and auxiliary fragments (e.g., detail supplementers) account for 60%;

• Gain distribution occurs in two phases: 50% is distributed in the first month after the solution is implemented, and the remaining 50% is distributed after 3 months upon confirming that effect standards are met (e.g., if scenario participation rate increases by ≥15%), ensuring gains are linked to the actual value of the solution;

• The distribution details are updated in real-time on-chain, allowing users to view the gain calculation process and distribution progress, ensuring fairness and transparency.

3. Cross-scenario Fusion Reuse

To maximize the value of aggregated solutions, the project supports 'cross-scenario fusion reuse', allowing the aggregated solution from scenario A to be adaptable to scenario B:

• The module automatically identifies common needs in different scenarios (e.g., both 'cross-border RWA' and 'green RWA' require a 'risk buffer mechanism'), adjusts existing aggregated solutions for 'scenario adaptation', and generates 'reusable versions';

• After the reusable solution is implemented, original solution contributors can still receive 10% of the fusion gain, encouraging users to submit universally applicable scattered suggestions;

• Public reuse records, allowing users to view how their fragment solutions are reused in different scenarios, enhancing the long-term sense of value in co-creation.

3. Ecological Collaboration and Future Direction

TreehouseFi creates a positive cycle through 'asset forward adaptation' and 'co-creation value fusion': foresight adaptation meets user needs in advance, enhancing user retention; users actively submit scattered contributions to obtain fusion gains, and the aggregated solutions feed back into optimizing the foresight adaptation mechanism (e.g., supplementing prediction model dimensions); cross-scenario reuse further amplifies co-creation value, attracting more users to participate and promoting sustainable ecosystem growth.

In the future, TreehouseFi will focus on:

1. Foresight Scenario Expansion: Add 'county-level micro prediction allocation' and 'supply chain RWA foresight rules' scenarios, refining the industry dimensions of the prediction model (e.g., 'operating cycle prediction' for micro users);

2. Fusion Tool Upgrade: Develop a 'fragment intelligent recommendation' function that recommends supplementary fragment themes based on users' historical contribution directions, accelerating the formation of complete solutions;

3. Transparency Enhancement: Publicly disclose the data sources for asset predictions, the aggregation logic of co-creation fragments, and the calculation basis for fusion gains, enhancing user trust.

TreehouseFi addresses the lagging asset adaptation with 'foresight' and activates the collective value of scattered contributions with 'fusion', providing a new ecological paradigm of 'predicting demand + aggregating wisdom' for the DeFi fixed income sector.