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…

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 proposes a market-feedback adaptive retrieval system for frozen LLMs in financial RAG, significantly improving event-impact prediction by learning which evidence sources to prioritize.

The paper investigates forecasting sparse and bursty vulnerability sightings, concluding that traditional time-series models like SARIMAX are inadequate, and count-based methods like Poisson regressio…

The paper proposes a novel nine-dimension risk assessment framework for institutional DeFi adoption, significantly enhancing existing methodologies by incorporating novel dimensions like composability…

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…

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 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 the pricing of outcomes in prediction markets is significantly influenced by the financial friction of delayed settlement, quantifying this effect using an annualized settlement…

The paper proposes the Interaction-Native Knowledge Harness (InKH), an architecture that absorbs complex context into financial LLM agents, significantly improving performance, reducing latency, and e…

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 analyzes Ethereum builder transactions to show that builder centralization is an emergent property of the Proposer-Builder Separation (PBS) architecture, driven by specific order flow and ME…

LiquiLM is a novel framework that combines Large Language Models (LLMs) with a Dynamic Co-Attention Network (DCN) to effectively bridge the semantic gap between complex smart contract code and high-le…

The paper demonstrates that large language models (LLMs) exhibit measurable, controllable biases toward specific assets like Bitcoin, identifying an internal feature that can causally shift portfolio…

SCAFDS introduces a novel, seven-stage graph attention system that models fraud propagation using co-occurrence edge features and generates forensically traceable SAR narratives, significantly improvi…

This paper analyzes the Solana Rug Pull ecosystem by creating a large-scale, manually verified dataset of fraudulent tokens, identifying three key behavioral patterns, and characterizing the resulting…

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…

PACT is a Pareto-aware active learning controller that significantly reduces the false-positive investigation burden in low-prevalence security alert streams without sacrificing recall.

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…