The paper characterizes the incentive conditions necessary for decentralized micropayment systems to offer credit limits that exceed full collateralization while remaining incentive compatible.

The paper investigates speculative Oracle Extractable Value (OEV) on Layer-2 blockchains, demonstrating that predictable latency differences in cross-chain oracle updates allow for profitable cross-ch…

The paper introduces KTD-Fin, a novel benchmark that evaluates LLM trading agents by masking historical market data and decomposing returns, finding that LLM agents' profits are largely due to passive…

The paper investigates whether tokenizing real-world assets actually improves liquidity, finding that liquidity is highly heterogeneous across asset types and is not reliably predicted by the outstand…

The paper analyzes the potential market impact of a large, unknown Bitcoin holder (the Satoshi overhang) and concludes that the mechanical downside risk is bounded, suggesting the terminal states are…

The paper introduces ForesightFlow, an Information Leakage Score (ILS) framework, to quantify pre-event information leakage in prediction markets, and proposes a necessary extension to analyze empiric…

The paper proposes ReCAP, a novel continual learning framework for portfolio management, which adaptively combines policies from a library based on detected market regimes to achieve superior long-ter…

The paper introduces an explainable risk scoring framework that evaluates tokenized real-world assets (RWAs) based on liquidity, concentration, and market quality, demonstrating that total value locke…

This paper maps the emerging insurability frontier of AI risk by coding 55 AI threat classes against 26 insurance products, identifying four tiers of coverage: affirmative, silent, excluded, and outsi…

This paper proposes a unified, lifecycle-centric framework and a detailed taxonomy to survey and analyze novel, finance-specific attack surfaces and vulnerabilities in AI systems used within the finan…

The paper analyzes and documents various double-dip reward abuse attacks that exploit flaws in how cashback and reward engines handle transaction refunds, proposing formal invariants and defensive alg…

The paper analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments are heterogeneous, lack clear autonomous execution, and exhibit poor risk…

The paper empirically analyzes the nascent DeFi investment agent market, finding that while token valuations are high, current deployments lack robust autonomous execution and exhibit poor risk-adjust…

The paper analyzes transaction fee mechanisms in modern blockchains that use parallel execution and contingency, proving an inherent trade-off between minimizing risks for users and maximizing revenue…

The paper introduces PortBench, a comprehensive benchmark that evaluates LLMs for portfolio management by assessing both correlation awareness and performance across a full, multi-stage decision pipel…

This paper shows that standard optimal control in Markov Decision Processes (MDPs) with an absorbing catastrophic state naturally generates behavioral signatures mimicking prospect theory, even withou…

This paper analyzes the bid-ask spread and welfare in the Glosten-Milgrom model when the market maker observes a noisy, privacy-protected trade direction signal, deriving a specific 'privacy subsidy'…

The paper introduces MEV non-interference, a formal security notion, to ensure that composing new smart contracts in DeFi does not increase the maximal extractable value, thereby providing a formal fo…

The paper introduces FinBoardBench, a novel evaluation suite using financial board games to demonstrate that current LLMs, despite strong static reasoning, fail at complex, dynamic wealth management a…

The paper introduces ForeSci, a novel benchmark that evaluates LLM agents' ability to make forward-looking research judgments using only historical evidence, finding that explicit evidence organizatio…