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

TIMEGATE introduces a resource-aware policy layer that manages continual ML adaptation by dynamically budgeting time and evaluation resources, achieving significant compute and energy savings without…

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…

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 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 a formal Risk-Cost Model (RCM) to provide an economically grounded and mathematically rigorous framework for adaptive authentication in high-stakes financial systems.

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.

This paper establishes the identifiability of latent regimes and regime-dependent causal structures in complex non-stationary time series modeled by Markov Switching Models, even with instantaneous ef…

The paper introduces ARCA, a novel credit assignment method that measures token salience directly from the adapter's residual hidden state, addressing the degeneracy of standard intrinsic signals when…

The paper develops and validates a novel Deep Reinforcement Learning (DRL) framework to enhance pair trading in volatile cryptocurrency markets, demonstrating statistically significant outperformance…

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 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 proposes a cost-aware, adaptive maintenance framework using Reinforcement Learning (RL) and self-supervised learning to mitigate performance degradation (concept drift) in Android malware de…

The paper proposes S2L-PO, a framework that uses smaller, naturally diverse models as structured explorers to enhance the policy-level diversity and performance of larger language models during traini…

This paper introduces CFMME, a comprehensive Chinese financial multimodal benchmark, and evaluates current Large Vision-Language Models (LVLMs), finding that while state-of-the-art models perform mode…

Taiji is a novel LLM-as-Enhancer framework that optimizes recommender systems by addressing the challenges of generating high-quality reasoning data and balancing semantic and ID-based rewards.