Since 2023, RWA has evolved from a concept into a verified sector with real data.

Tokenization of U.S. Treasuries, gold, and private credit is giving rise to a wave of new products, with TVL growing rapidly. BlackRock’s BUIDL fund, launched on Ethereum less than a year ago, has surpassed $500 million in assets; Franklin Templeton has moved its money market funds onto Polygon and Stellar, with managed assets also crossing this threshold. Boston Consulting Group predicts that by 2030, the total value of tokenized assets will reach $16 trillion. This figure is staggering enough, but what’s more significant is the underlying structural premise: can this sector safely support and sustain such a massive volume of assets over the long term?

Teams working on RWA have all tackled the same question: What assets can be tokenized? And how do you bring them on-chain?

The answer to this question has already been validated by the market. Government bonds can be tokenized, gold can be tokenized, private credit can be tokenized, and real estate, commodities, and even carbon credits have all seen experimental and commercial tokenization products. Technically, minting a token that represents ownership of an asset is not a high barrier — with smart contracts, custodial accounts, and on-chain oracles feeding prices, a standardized process allows products to go live. The industry has spent roughly two to three years refining this answer: almost any asset with clear ownership and legally definable rights can be tokenized.

But immediately after, the RWA team faced the next question: Once moved on-chain, how can assets remain active on-chain while preserving the economic relationship between the tokens and the underlying assets?

FRS, a new solution from Matrixdock

Under this issue, Matrixdock proposed the FRS (Fungible Reserve Standard) as a solution, aiming to address the on-chain holding cost problem at the mechanism level.

Matrixdock is Asia’s leading institutional-grade RWA tokenization platform, under BIT (formerly Matrixport), with products including the U.S. Treasury token STBT, the gold token XAUm, and the silver token XAGm. Over the past several years, Matrixdock has accumulated comprehensive practical experience in institutional-grade RWA, spanning compliant custody, on-chain transparency, and DeFi integration. It is also the core technology partner for the Kingdom of Bhutan in tokenizing sovereign assets, having built the TER gold token for GMC.

As mentioned earlier, physical assets on-chain are stored in vaults and incur annual custody, insurance, and audit fees; however, in most tokenization designs, these expenses are completely invisible. A token claims to represent one ounce of silver, but over time, users can actually redeem less than one ounce—yet they cannot realize this in real time from the blockchain.

The peg between tokens and underlying reserves is static from the moment of minting, and all subsequent changes rely on off-chain disclosures by the issuer. This issue accumulates over time. A one-year discrepancy may still be acceptable—what about the third or fifth year? Without an automatic correction mechanism, the longer a product operates, the greater the gap becomes between on-chain tokens and underlying assets.

FRS addresses this issue by introducing a variable q(t), representing the amount of underlying physical assets corresponding to each token at time t.

The specific analysis of FRS is based on the formula: q(t) = q(0) × (1 - r/365)^t

where q(0) refers to: the initial asset amount corresponding to the token, which equals 1 troy ounce at the issuance of XAGm;

r refers to the annualized holding cost rate, which is 0.3% (= 0.003) for XAGm;

t refers to the number of days elapsed since issuance;

Traditional RWA products handle holding costs in only two ways: periodically withdrawing assets from the reserve to pay fees, or opening a separate contract to charge users individually. The former disrupts the 1:1 backing between reserves and tokens, while the latter is incompatible with DeFi composability.

FRS's approach is to leave all assets in the vault untouched and only adjust q(t). Each day, q(t) decreases by r/365, while the number of tokens in users' wallets remains unchanged, but the asset backing per token decreases slightly each day. The portion of "reduced" assets corresponds to on-chain, transparently recorded custody fees. There are no off-chain operations or manual interventions—everything runs automatically according to the formula.

The reserve equation must hold exactly at all times, meaning that every new minting and redemption operation must be completed within this framework, with no exceptions.

When new assets enter the reserve, the number of newly minted tokens is determined by q(t) at that moment: new tokens issued = new asset amount / q(at that time). When users redeem, the corresponding tokens are burned, and the reserve is reduced accordingly. The entire process is automatically executed by on-chain logic, with no manual calculations or time delays caused by batch settlements. At the instant each transaction is completed, the reserve relationship equation is immediately re-established.

The true test of this mechanism only becomes apparent over time. Traditional RWA products have a common structural flaw: on-chain deviations accumulate continuously over time. The error in the first year is approximately 0.5%, but by the third year, it may have reached 1.5%.

The longer the time, the more obscure the economic relationship between the token and its underlying asset becomes. FRS answers this question with a different logic: by continuously zeroing out deviations through daily automatic reductions in q(t). Instead of periodic calibration, it precisely reflects the relationship every day. Theoretically, a product based on FRS can operate indefinitely without the on-chain economic relationship becoming distorted over time.

Another design detail deserves separate clarification. FRS explicitly separates custody costs from management profits: the r in the formula reflects only actual holding costs such as storage, insurance, and auditing, and excludes any management fees or profit extraction. This boundary is intentional—it aims to make the token a faithful mirror of the underlying asset’s economic reality, rather than a financial product embedded with commercial profit. The transparency of costs approaches the disclosure level of commodity ETFs, but is fully verifiable on-chain, without reliance on any party’s voluntary disclosure.

This is the most fundamental difference between FRS and most existing RWA products at the structural level: it’s not about doing something better, but about doing something different. The former treats holding costs as an off-chain financial issue, while the latter addresses them as an on-chain mechanism problem.

How does Silver XAGm work?

It’s one thing for the FRS framework to be valid in the whitepaper, and another for it to operate stably under real assets and real market pressures.

Therefore, we can understand FRS's role in the real market by analyzing Matrixdock's use of FRS's latest product, the silver token XAGm.

We use silver as an example not because it is easy, but precisely because it is difficult.

Among precious metals, gold follows a relatively straightforward logic: it is a macroeconomic reserve asset, with its price primarily driven by safe-haven demand and central bank behavior; its volatility is relatively predictable, the market has sufficient depth, and the custody infrastructure is well-established. The main challenges in tokenizing gold stem from compliance and custody, while the asset’s inherent properties remain stable.

But silver is different. Silver is driven by two entirely distinct pricing mechanisms: one is the investment logic, where silver’s status as a precious metal and its safe-haven属性 align with gold’s macro narrative; the other is the industrial logic, where silver serves as a core raw material for photovoltaic cells, electric vehicles, 5G base stations, and AI hardware, with demand directly tied to the global manufacturing cycle. These two logic systems sometimes move in the same direction, sometimes offset each other, and sometimes amplify one another.

The result is: The price volatility of silver has historically been about two to three times that of gold.

It rises more sharply in bull markets, falls more deeply in bear markets, and experiences more severe liquidity contraction under extreme market pressure. Silver has a well-known nickname in financial circles: "the poor man's gold," a label rooted in its significantly lower price per unit, much larger physical volume, and higher price elasticity compared to gold.

These characteristics make silver's requirements for on-chain structure more stringent than those of gold.

First, this means that the holding cost of silver is relatively high among precious metals. The reason is straightforward: silver has a much lower unit value than gold but a significantly larger physical volume. For the same market value, silver occupies about eighty times the volume of gold. This means that costs related to storage space, handling, and insurance—components calculated by volume rather than value—are proportionally higher for silver.

The high volatility of silver prices imposes additional requirements on the token structure of RWA products. When the price of the underlying asset fluctuates significantly, holders will ask: How much silver does my token precisely represent right now? If the answer relies on off-chain disclosures, its credibility will be severely undermined during periods of intense market volatility—not because the issuer is dishonest, but because "trust" requires time, and the market does not grant it.

The value of FRS here is solving this trust issue through mathematics.

Using an annualized fee rate of 0.3% for XAGm: the daily decay rate is approximately 0.000822%; after holding for a full year, q(365) = 0.997 troy ounces. The cost does not vanish into thin air—it is precisely encoded into time.

The current q(t) value of 0.999589042 can be directly verified on-chain. This number represents the cumulative daily holding cost since the product's launch.

We can verify whether the entire mechanism is self-consistent using real-time data:

Circulating supply: 45,972.892 XAGm;

Silver reserves: 45,954.000 troy ounces;

Current q(t): 0.999589042;

Verification: 45,972.892 × 0.999589042 ≈ 45,954

Derive the number of days online from q(t): approximately 50 days, perfectly matching the product launch date. The data is on-chain, the logic is in the contract—anyone can verify it independently.

The formula is public, the contract is public, and reserve data is on-chain. This feature’s value is amplified on volatile assets like silver.

If FRS can operate stably on silver, maintain the reserve relationship equation without distortion under extreme market conditions, and ensure the automatic decrease of q(t) functions normally under liquidity pressure, then it gains stronger credibility as a scalable design paradigm.

The current market cap of XAGm is approximately $3.95 million, with a circulating supply of about 46,000 troy ounces. But scale has never been the core metric at this stage. What needs to be proven now is the stability of the mechanism under real asset conditions and real market environments. The high volatility of silver provides a more stressful test environment than gold.

Beyond structural design, there is a practical issue: how to ensure that off-chain physical reserves remain consistent with on-chain data?

This is a common trust foundation issue faced by RWA products; FRS itself cannot solve it, but XAGm implements an independent verification mechanism at this layer.

At the physical level, the underlying silver of XAGm is held in institutional-grade vaults, using silver bars compliant with the LBMA Good Delivery standard, and is subject to regular independent physical audits by Bureau Veritas, with audit reports publicly released. Bureau Veritas is a globally recognized third-party inspection and certification body with no conflict of interest with the issuer.

On-chain, Matrixdock has implemented a Proof of Reserves mechanism, allowing real-time verification of the circulating supply, the q(t) value, and the total underlying silver amount; anyone can independently verify the validity of the relationship among these three using the formula.

What if the custodian encounters issues? XAGm employs a bankruptcy-remote structure, where the underlying physical assets are held by an independent legal entity, separate from Matrixdock’s operating entity. This means that even if the issuer faces operational problems, the underlying reserves remain legally owned by holders and will not be commingled.

It should be noted that the above mechanism provides structural safeguards, not a zero-risk guarantee. Off-chain audits occur at intervals, the accuracy of on-chain data depends on the reliability of oracles, and both the custodian and auditor are subject to operational risks. These are systemic constraints that cannot be fully eliminated in any tokenized physical asset product. FRS’s contribution lies in achieving a level of on-chain transparency that can be independently verified, rather than claiming to resolve all risks.

How far does Matrixdock have to go after FRS?

Of course, these use cases are still in their early stages. XAGm has been live for fifty days, with limited scale and ongoing efforts to integrate into the ecosystem.

In fact, XAGm itself is merely a verification point; the issue it truly aims to prove is much larger than silver.

Matrixdock, in its Outlook 2026, posits that the core issue of RWA is shifting from “whether it can be tokenized” to “whether these assets can be incorporated into institutional balance sheets and operate continuously across different market cycles.”

This is a point worth serious consideration. After all, the balance sheet represents the strictest threshold for institutional asset holdings, requiring that asset values be clear, verifiable, and audit-ready at all times. An RWA product with hidden cost bases and a deteriorating economic relationship between tokens and underlying assets over time cannot enter the balance sheet—it remains confined to speculative allocations.

This means that RWA is no longer just an issuance issue, but rather closer to a structural one. While many assets can be tokenized, those that institutions can hold long-term must maintain economic stability over time, along with transparency and verifiability.

From this perspective, FRS is not just a product mechanism, but a structural design that enables on-chain assets to possess the capability of being recorded on a balance sheet. It addresses not the issue of minting, but whether assets on-chain can withstand time-based audits.

If this direction holds, the evolution of FRS could also unfold along several other pathways.

First, a unified standard for multiple assets. Precious metals, commodities, and infrastructure assets all use the same q(t) structure, enabling DeFi protocols to handle different types of collateral using a single set of logic. Once a lending protocol integrates a valuation module that understands the q(t) logic, it can theoretically process any RWA asset designed under FRS without requiring custom logic for each individual asset. The value of standardization is here multiplied exponentially.

Second, the adjustable fee mechanism. Currently, r in FRS is a fixed contract parameter, ensuring certainty at the cost of adaptability. In reality, custody costs vary with market conditions—warehouse rents rise, insurance premiums fluctuate, and audit expenses evolve with regulatory requirements. In the future, an on-chain governance mechanism may be introduced to allow r to be adjusted within a defined range, while the adjustment process itself remains transparent and verifiable on-chain, rather than being unilaterally decided by the issuer.

Third is cross-chain consistency. XAGm is currently deployed on Ethereum, and multi-chain expansion is in planning. How to maintain precise synchronization of the same q(t) state across multiple chains is the next engineering challenge—any minor inconsistency in q(t) across different chains could create arbitrage opportunities and undermine the strict validity of the reserve equation.

This also means that the next round of competition in RWA may not occur at the issuance layer, but rather at the structural layer.

Click to learn about the open positions at BlockBeats

Welcome to the official BlockBeats community:

Telegram subscription group: https://t.me/theblockbeats

Telegram group: https://t.me/BlockBeats_App

Official Twitter account: https://twitter.com/BlockBeatsAsia