Current financial benchmarks prioritize large language models (LLMs) for task accuracy and portfolio returns, yet overlook risks arising from multi-agent cooperation, tool-sharing, and real-world financial actions. We introduce M-SAEA, a Multi-agent, Safety-Aware Evaluation Agent that audits LLM teams without fine-tuning, deploying ten probes across four layers: model, workflow, interaction, and system, to yield a continuous risk vector and natural-language rationale. Evaluated across three high-stakes tasks (finance management, webshop automation, transactional services) with six prominent models, M-SAEA (i) identifies unsafe trajectories with minimal false positives, (ii) reveals latent risks (e.g., temporal staleness) that are not addressed by standard metrics, and (iii) provides granular, actionable scores for balancing safety and latency pre-deployment. By quantifying safety as a model-agnostic metric, M-SAEA reorients evaluation from individual tasks to collaborative teams, offering a robust template for risk-first assessment of agentic AI in finance and beyond.

Large language models (LLMs) have achieved remarkable success in natural language processing (NLP), demonstrating significant capabilities in processing and understanding text data. However, recent studies have identified limitations in LLMs’ ability to manipulate, program, and reason about structured data, especially graphs. We introduce GraphEval36K, the first comprehensive graph dataset, comprising 40 graph coding problems and 36,900 test cases to evaluate the ability of LLMs on graph problem-solving. Our dataset is categorized into eight primary and four sub-categories to ensure a thorough evaluation across different types of graphs. We benchmark eight LLMs, finding that private models outperform open-source ones, though the gap is narrowing. We also analyze the performance of LLMs across directed vs undirected graphs, different kinds of graph concepts, and network models. Furthermore, to improve the usability of our evaluation framework, we propose Structured Symbolic Decomposition (SSD), an instruction-based method designed to enhance LLM performance on complex graph tasks. Results show that SSD improves the average passing rate of GPT-4, GPT-4o, Gemini-Pro and Claude-3-Sonnet by 8.38%, 6.78%, 29.28% and 25.28%, respectively.

Large Language Models (LLMs) have achieved remarkable success in natural language tasks, yet understanding their reasoning processes remains a significant challenge. We address this by introducing XplainLLM, a dataset accompanying an explanation framework designed to enhance LLM transparency and reliability. Our dataset comprises 24,204 instances where each instance interprets the LLM’s reasoning behavior using knowledge graphs (KGs) and graph attention networks (GAT), and includes explanations of LLMs such as the decoder-only Llama-3 and the encoder-only RoBERTa. XplainLLM also features a framework for generating grounded explanations and the debugger-scores for multidimensional quality analysis. Our explanations include why-choose and why-not-choose components, reason-elements, and debugger-scores that collectively illuminate the LLM’s reasoning behavior. Our evaluations demonstrate XplainLLM’s potential to reduce hallucinations and improve grounded explanation generation in LLMs. XplainLLM is a resource for researchers and practitioners to build trust and verify the reliability of LLM outputs. Our code and dataset are publicly available.