Other Agent

Business process agent with auto-delegation to 86-agent Solstice Pantheon (Athena/Plutus/Prism/Ares) and ReAct fallback with contract analysis, NPV/IRR computation, budget variance, and scenario modeling.

An increasing number of intelligent systems interact with daily human activities, making robust egocentric visual information processing essential. However, existing benchmarks for Visual Agents and Visual Language Models (VLMs) primarily focus on third-person perspectives or capture only short-term visual understanding, limiting their ability to model long-horizon, action-centric procedures. To bridge this gap, we propose EgoErrorVQA (renamed EgoProceBench to this), the first visual question answering (VQA) task designed for egocentric procedure comprehension with explicit modeling of procedural errors that reflect common execution failures in real-world tasks. EgoErrorVQA evaluates a range of models using both open-ended and multiple-choice questions, revealing persistent weaknesses in handling procedures with step-wise logical dependencies. In open-end VQA, EgoErrorVQA contains 3,560 QA-pairs covering 1,805 samples, with each sample associated with 1–3 QA-pairs from which one is randomly selected during evaluation. EgoErrorVQA first transmits to the evaluated White agent, via a communication protocol, a Procedure that outlines the complete workflow for the task, specifying the main steps required to achieve the goal. White agent is then asked to answer carefully designed questions that probe, from multiple perspectives, whether specific steps and their ordering are appropriate. The answers are sent back to EgoErrorVQA, which performs scoring and evaluation metrics from two LLM judges; for each question, EgoErrorVQA also provides an explanation of the rationale behind the assigned score. Multiple-choice VQA supplies the white agent with the task-specific procedures and error types’ definitions. To limit distraction from irrelevant content, each video sample is annotated with action labels, and the agent is instructed only to determine whether an error occurs in the specified step and, if so, to identify its type. The evaluation set comprises approximately 1,859 samples and share the same data source with open-end VQA, covering 31 procedural tasks.