arXiv AI
Aug 11
Business Arena: Benchmarking LLM Agents in a Realistic Marketplace
arXiv:2608. 08621v1 Announce Type: new Abstract: Running a business is a challenging form of intelligent work.
AI Playing Business Games: Benchmarking Large Language Models on Managerial Decision-Making in Dynamic Simulations
arXiv:2509. 26331v2 Announce Type: replace Abstract: The rapid advancement of LLMs sparked significant interest in their potential to augment or automate managerial functions.
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arXiv AI
Jun 9
MBABench: Evaluating LLM Agents on End-to-End Spreadsheet Tasks in Finance
arXiv:2605. 22664v2 Announce Type: replace Abstract: LLM agents are increasingly expected to carry out end-to-end workflows, producing complete artifacts from high-level user instructions.
arXiv AI
Jun 18
CEO-Bench: Can Agents Play the Long Game?
arXiv:2606. 18543v1 Announce Type: new Abstract: Language model agents are becoming proficient executors at isolated, short-horizon tasks such as software engineering and customer service.
arXiv AI
Jul 23
FORCE-Bench: A Benchmark, Dataset, and Evaluation Harness for Agentic AI in Enterprise Finance
arXiv:2607. 19409v1 Announce Type: new Abstract: Recent advances in large language models have accelerated deployment of agentic systems in operational finance.
arXiv AI
Jun 16
RetailBench: Benchmarking long horizon reasoning and coherent decision making of LLM agents in realistic retail environments
arXiv:2606. 15862v1 Announce Type: new Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain.
arXiv AI
Jul 10
RetailBench: Evaluating Long-Horizon Autonomous Decision-Making and Strategy Stability of LLM Agents in Realistic Retail Environments
arXiv:2603. 16453v3 Announce Type: replace Abstract: Large language model (LLM) agents have made rapid progress on short-horizon, well-scoped tasks, yet their ability to sustain coherent decisions in dynamic long-horizon environments remains uncertain.
arXiv AI
Jun 2
Business Utility of Large Language Models as Exploratory Data Analysis Agents
arXiv:2606. 00051v1 Announce Type: cross Abstract: Large Language Models (LLMs) are increasingly used in analytical workflows, but their suitability as exploratory data analysis (EDA) agents in business settings remains uncertain.
arXiv AI
Jun 17
Can LLMs Be CEOs? Benchmarking Strategic Resource Reallocation with Multi-Role Agent Simulation
arXiv:2606. 17459v1 Announce Type: new Abstract: Evaluating the decision-making capabilities of large language models (LLMs) is a growing research priority, yet existing benchmarks focus on isolated cognitive tasks such as reasoning, knowledge retrieval, and economic rationality in stylized settings.
arXiv AI
Jun 30
SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows
arXiv:2606. 29955v1 Announce Type: cross Abstract: Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making.
arXiv AI
Jun 30
The CRISTAL Method: Neurosymbolic analysis from AI-synthesized world models
arXiv:2606. 29799v1 Announce Type: new Abstract: This project introduces the CRISTAL Method (Coherent Reliable Intentional Synthesis of Truthful Analysis Logic), a neurosymbolic framework for automating complex analysis workflows, with fundamental investment analysis as a primary use case.
arXiv AI
Jul 8
EvalLoop: A Methodology for Evaluation-Driven Iterative Improvement of Business AI Systems
arXiv:2607. 05638v1 Announce Type: cross Abstract: Teams deploying large language models in business contexts need evaluation systems, yet most treat evaluation as static model selection: run benchmarks, rank models, deploy the winner.
Hugging Face Trending Papers
Jun 29
SpreadsheetBench 2: Evaluating Agents on End-to-End Business Spreadsheet Workflows
Spreadsheets are widely used for business analysis, financial modeling, reporting, and decision-making. However, most existing spreadsheet benchmarks evaluate isolated operations such as single-formula generation or local cell edits, and therefore fail to capture end-to-end workflows in realistic business settings.