BankerToolBench: Evaluating AI Agents in End-to-End Investment Banking Workflows

Existing AI benchmarks lack the fidelity to assess economically meaningful progress on professional workflows. To evaluate frontier AI agents in a high-value, labor-intensive profession, we introduce BankerToolBench (BTB): an open-source benchmark of end-to-end analytical workflows routinely performed by junior investment bankers. To develop an ecologically valid benchmark grounded in representative work environments, we collaborated with 502 investment bankers from leading firms. BTB requires agents to execute senior banker requests by navigating data rooms, using industry tools (market data platform, SEC filings database), and generating multi-file deliverables--including Excel financial models, PowerPoint pitch decks, and PDF/Word reports. Completing a BTB task takes bankers up to 21 hours, underscoring the economic stakes of successfully delegating this work to AI. BTB enables automated evaluation of any LLM or agent, scoring deliverables against 100+ rubric criteria defined by veteran investment bankers to capture stakeholder utility. Testing 9 frontier models, we find that even the best-performing model (GPT-5.4) fails nearly half of the rubric criteria and bankers rate 0% of its outputs as client-ready. Our failure analysis reveals key obstacles (such as breakdowns in cross-artifact consistency) and improvement directions for agentic AI in high-stakes professional workflows.

ObjectLab: Automated Diagnosis of Mislabeled Images in Object Detection Data

Despite powering sensitive systems like autonomous vehicles, object detection remains fairly brittle in part due to annotation errors that plague most real-world training datasets. We propose ObjectLab, a straightforward algorithm to detect diverse errors in object detection labels, including: overlooked bounding boxes, badly located boxes, and incorrect class label assignments. ObjectLab utilizes any trained object detection model to score the label quality of each image, such that mislabeled images can be automatically prioritized for label review/correction. Properly handling erroneous data enables training a better version of the same object detection model, without any change in existing modeling code. Across different object detection datasets (including COCO) and different models (including Detectron-X101 and Faster-RCNN), ObjectLab consistently detects annotation errors with much better precision/recall compared to other label quality scores.

Utilizing supervised models to infer consensus labels and their quality from data with multiple annotators

Real-world data for classification is often labeled by multiple annotators. For analyzing such data, we introduce CROWDLAB, a straightforward approach to estimate: (1) A consensus label for each example that aggregates the individual annotations (more accurately than aggregation via majority-vote or other algorithms used in crowdsourcing); (2) A confidence score for how likely each consensus label is correct (via well-calibrated estimates that account for the number of annotations for each example and their agreement, prediction-confidence from a trained classifier, and trustworthiness of each annotator vs. the classifier); (3) A rating for each annotator quantifying the overall correctness of their labels. While many algorithms have been proposed to estimate related quantities in crowdsourcing, these often rely on sophisticated generative models with iterative inference schemes, whereas CROWDLAB is based on simple weighted ensembling. Many algorithms also rely solely on annotator statistics, ignoring the features of the examples from which the annotations derive. CROWDLAB in contrast utilizes any classifier model trained on these features, which can generalize between examples with similar features. In evaluations on real-world multi-annotator image data, our proposed method provides superior estimates for (1)-(3) than many alternative algorithms.