Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Artificial Intelligence (AI) agent system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4x latency improvement over cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.
The tokenization of real-world assets (RWAs) has emerged as a transformative application of blockchain technology, with market projections estimating trillions of dollars in tokenized assets within the coming decade. However, a fundamental challenge remains unaddressed: physical assets such as precious metals, stored commodities, and warehoused goods incur structural negative carry -- custody, insurance, and audit costs that accumulate over time. While existing tokenization models have successfully established the market for digital gold and treasuries, they typically manage operational costs at the issuer level. The FRS introduces a framework to bring these economics directly on-chain, avoiding mechanisms such as token rebasing that compromise fungibility and composability with decentralized finance (DeFi) protocols. This paper proposes the Fungible Reserve Standard (FRS), a deterministic token design framework that encodes carrying costs transparently into on-chain logic. The FRS introduces an asset-per-token variable q(t) that decreases according to a predefined annualized carrying cost rate, coupled with a supply reconciliation mechanism that preserves holder balances and ERC-20 composability. While mathematically inspired by the daily expense ratio accrual in traditional asset management -- which often embed centralized profit margins -- the FRS design specifically encodes actual operational carrying costs to provide pure institutional-grade accounting clarity without compromising DeFi compatibility. The framework is asset-agnostic and applicable to any real-world asset with positive, predictable holding costs.
We formulate and solve stochastic control problems that model the core yield-generating strategy of the Ethena protocol, a decentralized finance (DeFi) stablecoin that earns yield by combining a long position in staked Ethereum (stETH) with an equal-sized short position in ETH perpetual futures. The combined position is delta-neutral with respect to the ETH spot price, yet earns carry from two sources: staking rewards on the stETH leg, and funding-rate payments received from long perpetual holders when the perpetual trades at a premium to spot. A key feature of our model is that the control -- the rate of simultaneously buying stETH and shorting the perpetual -- exerts two distinct types of price impact. \textit{Permanent} impact shifts the mid-market prices of both legs, compressing the basis and permanently eroding future funding income. \textit{Temporary} impact reflects execution slippage on each leg. We study both an infinite-horizon discounted problem and a finite-horizon problem in which the protocol maximizes total wealth up to a fixed date $T$, subject to a terminal cost for liquidating any remaining position. In both cases the optimal control is obtained explicitly.
Bitcoin's limited programmability and transaction throughput have historically prevented native Bitcoin from participating in decentralized finance (DeFi) applications. Existing solutions depend on honest-majority thresholds, or centralized custodial entities that introduce significant trust requirements. This paper introduces Bitcoin Smart Accounts (BSA), a novel protocol that enables native Bitcoin to access DeFi through trust-minimized infrastructure while maintaining self-custody of funds. BSA achieves this through a combination of emulated Bitcoin covenants using Partially Signed Bitcoin Transactions (PSBTs) and Taproot scripts, a Trusted Execution Environment (TEE)-based arbitration system, and destination chain smart contracts that enable DeFi platforms to accept self-custodial Bitcoin as collateral without necessitating protocol-level modifications. The setup leverages liquidity secured by the Lombard Security Consortium which provides a twofold advantage: for a DeFi protocol, liquidators rely on fungible assets with deep liquidity to quickly exit positions, while for a depositor, the general trust assumptions of honest majority (m-of-n) are reduced to existential honesty (1-of-k). We present the complete protocol design, including the Bitcoin architecture, the TEE-based arbitration mechanism, and the Smart Account Registry for protocol management. We provide a security analysis that demonstrates the correctness, safety, and availability properties under our trust model. Our design enables native Bitcoin to serve as collateral in lending markets and other DeFi protocols without requiring users to relinquish custody of funds.
Marco Dessalvi, Massimo Bartoletti, Alberto Lluch-Lafuente
Decentralized Finance (DeFi) has revolutionized financial markets by enabling complex asset-exchange protocols without trusted intermediaries. Automated Market Makers (AMMs) are a central component of DeFi, providing the core functionality of swapping assets of different types at algorithmically computed exchange rates. Several mainstream AMM implementations are based on the constant-product model, which ensures that swaps preserve the product of the token reserves in the AMM - up to a trading fee used to incentivize liquidity provision. Trading fees substantially complicate the economic properties of AMMs, and for this reason some AMM models abstract them away in order to simplify the analysis. However, trading fees have a non-trivial impact on users' trading strategies, making it crucial to develop refined AMM models that precisely account for their effects. In this work, we extend a foundational model of AMMs by introducing a new parameter, the trading fee ϕ ∈ (0,1], into the swap rate function. Fee amounts increase inversely proportional to ϕ. When ϕ = 1, no fee is applied and the original model is recovered. We analyze the resulting fee-adjusted model from an economic perspective. We show that several key properties of the swap rate function, including output-boundedness and monotonicity, are preserved. At the same time, other properties - most notably additivity - no longer hold. We precisely characterize this deviation by deriving a generalized form of additivity that captures the effect of swaps in the presence of trading fees. In particular, we prove that when ϕ < 1, executing a single large swap yields strictly greater profit than splitting the trade into smaller ones. Finally, we derive a closed-form solution to the arbitrage problem in the presence of trading fees and prove its uniqueness. All results are formalized and machine-checked in the Lean 4 proof assistant.
Do vulnerabilities in Decentralized Finance (DeFi) destabilize traditional short-term funding markets? While the prevailing ``Contagion Hypothesis'' posits that stablecoin reserve liquidations may transmit distress to traditional markets through fire-sale pressure, we document a short-horizon ``Flight-to-Quality'' pattern in the opposite direction. In the wake of major DeFi exploits, spreads on 3-month AA-rated commercial paper (CP) tend to narrow rather than widen. We interpret this pattern as consistent with a ``liquidity-recycling'' channel: capital leaving DeFi may be re-intermediated into traditional cash-management markets, with regulatory segmentation under SEC Rule 2a-7 making prime-eligible paper a plausible marginal destination. Because we do not directly observe daily fund-level routing into prime money market funds, this mechanism is inferred from pricing patterns and monthly holdings evidence rather than directly identified. The result is specific to exploit-driven operational shocks, this U.S. CP spread, and short event windows.
Non-fungible tokens (NFTs) on Ethereum currently follow a binary mobility paradigm: ERC-721 enables unrestricted transfers, whereas SBTs (ERC-5192) prohibit transfers entirely. We identify a design gap in which no standard mechanism supports bounded transferability, where ownership mobility is allowed but limited to a finite number of programmable transfers. We study counted NFT transfers and introduce ERC-7634 as a minimal realization compatible with ERC-721. The design augments each token with a transfer counter and configurable cap L, allowing ownership to evolve under a finite transfer budget. ERC-7634 defines a minimal extension interface with three lightweight functions (transferCountOf, setTransferLimit, and transferLimitOf), two events, and native-transfer hooks, requiring fewer than 60 additional lines of Solidity while preserving full backward compatibility with existing NFT infrastructure. We analyze behavioral and economic consequences of counted transfers. Our results reveal (i) a mobility premium induced by remaining transfer capacity, (ii) a protocol-level costing signal that can deter wash trading in cap-aware markets through irreversible budget consumption, (iii) bounded recursive collateralization enabled by limited ownership turnover, and (iv) associated security and gas-cost implications, including wrapper-bypass trade-offs. Evaluation on calibrated simulations shows that moderate limits (e.g., L = 10) affect fewer than 15% of tokens under representative transfer distributions, while repeated manipulation becomes unprofitable after a few cycles in a cap-aware pricing model; the additional gas overhead remains below 11% per transfer. We further position ERC-7634 within the NFT mobility design space, derive practical cap-selection guidelines, and discuss post-cap ownership outcomes including soulbound conversion, auto-burn, and provenance freeze.
The objectives of this paper are to provide a methodology for applying the DeTEcT framework to modelling token economies, to formalise the configuration of the simulation environment, and to introduce an event analysis framework. A token economy is an economic system that has a unique mechanism for controlling its monetary supply, and a medium, in the form of a token or currency, for the valuation of goods and services, the settlement of transactions, and the storage of value. We show the key decisions that must be made when modelling an economy with the DeTEcT framework and showcase some numerical methods that can be used in conjunction with the framework to perform economic simulations. We also propose a framework for analysing and measuring the impacts of events on an economy, while also developing a procedure to measure the significance of these impacts. Throughout the paper, we use Bitcoin as a case study to demonstrate how to apply the frameworks and tools we proposed here. We show how a model of Bitcoin token economy can be set up, and how to measure the impacts of Bitcoin's endogenous policies (i.e., BIPs) on the wealth distribution of its economic participants.
Security token offerings (STOs) turn part of securities issuance into a technology-design choice. We measure the deployment and execution costs of five production tokenization standards on Ethereum and Solana and develop a stylized model in which an issuer chooses the architecture that minimizes the on-chain cost of issuing and servicing a security. The model yields a simple scaling restriction: holding the number of investor accounts, onboarding actions, and transfers fixed, total on-chain cost does not vary with the number of tokenized units. The benchmarks support two results. First, costs differ sharply across architectures. On Ethereum, the most complianceintensive standard raises per-transfer cost to 196,844 gas, 286% above an unrestricted token baseline, and requires 36.8 million gas to deploy, roughly seven to fourteen times the deployment cost of lighter Ethereum designs. On Solana, the shared-program model shifts deployment from issuer-specific smart-contract deployment to account rent, and per-transfer fees are several orders of magnitude below Ethereum's. Second, tokenized issuance scales differently from conventional issuance. Deployment is a fixed cost per security, while recurring on-chain costs are driven by investor accounts, compliance and onboarding actions, and secondary-market transfers. These results show that token standards do more than record ownership: they allocate compliance, transfer, and issuer-control functions across code, intermediaries, and platforms, thereby shaping both the cost and governance structure of tokenized securities.
Direct Preference Optimization (DPO) has emerged as a popular alternative to Reinforcement Learning from Human Feedback (RLHF) for aligning Large Language Models (LLMs) with human preferences, eliminating the need for explicit reward modeling. However, DPO and most of its variants treat all tokens in a response uniformly during preference optimization, assigning equal optimization pressure to every token regardless of its actual contribution to the preference judgment. This uniform treatment is suboptimal because only a small subset of tokens, such as factual claims, safety-critical terms, or key instruction-following phrases, typically determines the preference label, while the remaining tokens act as noise that dilutes the optimization signal. We propose GIT-DPO, a novel token-level preference optimization framework that combines two complementary innovations: a gradient-based token attribution mechanism that dynamically computes fine-grained importance weights by measuring the sensitivity of the DPO loss to perturbations in each token's log-probability, and a contrastive dual-pair loss that simultaneously pulls the policy toward preferred responses, pushes it away from dispreferred responses, and regularizes low-importance tokens toward the reference model. Extensive experiments on Al-pacaEval 2.0, Arena-Hard, and MT-Bench across three backbone LLMs (Llama-3-8B-Instruct, Mistral-7B-Instruct, Qwen2.5-7B-Instruct) demonstrate that GIT-DPO consistently outperforms nine strong baselines including DPO, SimPO, TDPO, ORPO, KTO, ConfPO, Selective-DPO, and AlignDistil. On Llama-3-8B-Instruct, GIT-DPO achieves a length-controlled win rate of 22.84 percent on AlpacaEval 2.0, outperforming the strongest baseline by 2.74 percentage points, while converging 1.5 times faster than standard DPO with only 5 to 8 percent additional training time per step.
Token projects face a financing deadlock: a professional security audit is the single strongest pre-launch signal of contract safety, but audits cost tens of thousands of dollars and must be paid for before a token exists to sell, while contributors who could fund the audit have no way to verify what they are funding. Existing launchpads resolve this deadlock by inserting trusted parties (the platform, a review council, the founder's promises), which relocates the trust problem rather than removing it. We formalize the verifiable launch: a deposit-and-deployment protocol in which an escrow contract accepts exactly four kinds of input (money, clock time, cryptographic proofs, and per-user choices affecting only that user) and in which no actor ever attests, approves, or decides anything on behalf of another actor. A founder commits on-chain to a fixed-schema claims manifest describing the token's properties, together with a zkVM receipt proving that a complete implementation satisfying those claims existed before any money moved, without revealing the code. Contributions sit in a non-custodial escrow; on success, an audit fee derived deterministically from the proven code size is escrowed for a pre-named auditor; the audit report is bound on-chain either by the auditor's own typed-data signature or by a permissionless web proof of the report on the auditor's domain; machine-evaluated launch conditions over the report's finding counts gate deployment; a second receipt binds the post-remediation code to the same manifest; and the escrow itself deploys the token by CREATE2 and doubles as the distribution ledger. Every failure path degrades automatically to pull-based refunds. We give the mechanism as a formal state machine with exact verification predicates, analyze its security against adversarial founders, contributors, auditors, platforms, and proof-layer attacks, treat the incentives game-theoretically (including why paying the auditor independent of findings dominates outcome-contingent payment), and cost the hard engineering: compiling Solidity inside a zkVM, for which we define a tiered deployment path that upgrades from attested to fully proven verification by verifier substitution alone. We are precise about what the mechanism cannot do: it proves process, never quality; it removes discretionary trust, never market or legal risk; and a clean audit of an honest manifest does not make a good project. Section 8 records the seven design revisions the analysis forced on earlier iterations of the protocol.
This chapter analyses the historical and economic logic of small-denomination coin production and circulation from antiquity to the early modern period. The persistent problem of small- change shortages is primarily attributed to the disproportionately high production costs of low-value coins relative to their face value. The ‘standard formula’, as proposed by Cipolla, Sargent, and Velde, emerges as a historical solution: governments should issue small change as token coins, ensure their legal tender status and acceptance for tax payments, and maintain credible—though not necessarily actual—convertibility into full-bodied coins in order to control supply and prevent inflation. This chapter contributes new insights by integrating numismatic evidence with modern economic modelling to reassess long-standing theories about the persistent shortage of small change. The chapter examines supply-side constraints, including minting and melting points, and explores alternative methods to the standard formula, such as debasement, coin division, and advanced minting technologies. Historical case studies from ancient Greece, Rome, the Middle Ages, and early modern Europe illustrate the evolution of these practices. While ancient Greeks and Romans developed early partial versions of this formula, they did not fully solve the problems of quantity control, convertibility, and counterfeiting. Notably, small-change shortages were most acute during the late Middle Ages and early modern period. The use of trade tokens issued by local authorities and private merchants, particularly in England, played a crucial role in mitigating these shortages, leading to the adoption of standardized minting practices and the standard formula by the early nineteenth century. The analysis underscores the complex interplay between production costs, monetary policy, and the availability of small change, highlighting the innovative responses to an enduring monetary problem.