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 introduces BlueFin, a challenging benchmark for evaluating LLM agents on complex financial spreadsheet tasks, finding that even frontier models perform poorly, scoring less than 50% on avera…

FundaPod is a multi-persona agent platform designed for fundamental investment research, enabling AI agents with distinct viewpoints to independently gather evidence and surface disagreements for huma…

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…

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…

This paper investigates the practical barriers preventing the trustworthy deployment of AI-driven Cyber Threat Intelligence (CTI) in the highly regulated financial sector, identifying four key socio-t…

The paper introduces AGENTCL, a rigorous evaluation framework that uses controlled task streams to accurately measure an agent's ability to accumulate and reuse knowledge across multiple tasks, thereb…

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

The paper extends the User Experience Research (UXR) Points of View (PoV) framework into an AI-augmented methodology specifically designed for guiding the development and governance of high-stakes, hu…

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…

This systematic review analyzes the bidirectional integration of AI and DLT, finding that while research is growing, most studies neglect cross-layer co-design and fail to demonstrate production-scale…

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 the Data-centric Reasoning Compiler (DCRC), a novel data-driven framework that enhances financial QA systems by compiling user queries and retrieved documents into verifiable, exe…

The paper proposes MERIT, a dual-level, multi-horizon memory retrieval framework that significantly improves the performance of interactive text-to-SQL agents by providing both global and local memory…

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 introduces the Lean-Agent Protocol, a formal verification platform that uses Lean 4 theorem proving to ensure agentic AI actions in finance are mathematically compliant with complex regulati…

The paper evaluates multi-agent LLM oracle systems for prediction market resolution, finding that independent aggregation with confidence-weighted voting significantly outperforms single-model baselin…

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.