EgoErrorVQA: Assess Egocentric Comprehension Capabilities through Procedural Errors for Ego-Agentic AI

• URL: http://arxiv.org/abs/2608.24134v1
• Date: Tue, 25 Aug 2026 06:58:44 GMT
• Title: EgoErrorVQA: Assess Egocentric Comprehension Capabilities through Procedural Errors for Ego-Agentic AI
• Abstract summary: EgoErrorVQA task is proposed for egocentric procedural comprehension with explicit procedural errors modeling.<n>We develop a user-friendly evaluator agent based on the Agent2Agent protocol.<n>We also introduce Ego-ADR, an Adaptive Decoupled Reasoning framework.
• Score: 24.612087629174656
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The majority of our everyday activities are procedural and consist of sequences of interdependent steps. However, existing benchmarks for Visual Agents and Visual Language Models (VLMs) overlook the evaluation of their procedural comprehension ability from an egocentric visual perspective, particularly for detecting procedural errors, a critical capability for everyday assistance. To bridge this gap, the EgoErrorVQA task is firstly proposed for egocentric procedural comprehension with explicit procedural errors modeling. Besides, we develop a user-friendly evaluator agent based on the Agent2Agent (A2A) protocol, enabling rigorous and standardized evaluation of visual agents through VQA-based interaction. A range of models are evaluated using both open-ended and multiple-choice questions, revealing persistent weaknesses in handling procedural errors and error types. Moreover, we introduce Ego-ADR, an Adaptive Decoupled Reasoning framework that decouples complex procedural reasoning to enhance models' understanding of procedural errors. It achieves consistent performance gains over the selected baselines and attains state-of-the-art results on several metrics under comparable settings. Code: https://github.com/z1oong/EgoErrorVQA

Related papers

• MMPCBench: Benchmarking Multimodal Large Language Models on Proactive Critique of Flawed Inputs [16.970480774847857]
  We define Proactive Critique as the model's autonomous ability to identify, analyze and fix faulty user inputs without extra prompts.<n>We propose MMPCBench, a comprehensive framework for evaluating MLLMs' proactive critique competence.<n>It features a fine-grained taxonomy of 4 primary error types spanning 12 subcategories, ranging from cross-modal contradictions to missing visual premises.
  arXiv  Detail & Related papers  (2026-08-29T14:19:10Z)
• From Profiling to Synthesis: Benchmarking Implicit Behavioral Alignment in Personalized LLM Agents [56.187590188698955]
  Large Language Models have enabled increasingly capable autonomous agents, yet personalization remains critical for making such agents practically useful.<n>Recent benchmarks have begun evaluating personalization in agents, but they largely rely on static preference snapshots, fixed interaction logs, or question answering over predefined user profiles.<n>We introduce IBA-Bench, a benchmark for implicit behavioral alignment constructed from longitudinal interaction histories.<n>We propose IBA-Agent, an agent framework that reconciles conflicting priorities through broad retrieval and trajectory-level alignment.
  arXiv  Detail & Related papers  (2026-08-03T12:52:04Z)
• EgoProceVQA: A Novel Egocentric Procedural Understanding Task with Self-Skill-Exploration Agent [40.15272756472325]
  Egocentric Procedural Understanding VQA task (EgoProceVQA)<n>EgoProceGen, a data generation platform that efficiently constructs QA data tailored to different question types.<n>EgoProceAgent, a self-skill-exploration agentic framework.
  arXiv  Detail & Related papers  (2026-07-15T12:55:35Z)
• How to Correctly Make Mistakes: A Framework for Constructing and Benchmarking Mistake Aware Egocentric Procedural Videos [18.88275830423089]
  We present PIE-V (Psychologically Inspired Error injection for Videos), a framework for constructing and benchmarking egocentric procedural videos.<n>For benchmarking, we introduce a unified taxonomy and a human rubric with nine metrics that cover step-level and procedure-level quality.
  arXiv  Detail & Related papers  (2026-04-16T15:14:29Z)
• AgentProcessBench: Diagnosing Step-Level Process Quality in Tool-Using Agents [50.481033105867205]
  We introduce AgentProcessBench, the first benchmark dedicated to evaluating step-level effectiveness in realistic, tool-augmented trajectories.<n>The benchmark comprises 1,000 diverse trajectories and 8,509 human-labeled step annotations with 89.1% inter-annotator agreement.<n>It features a ternary labeling scheme to capture exploration and an error propagation rule to reduce labeling ambiguity.
  arXiv  Detail & Related papers  (2026-03-15T16:13:58Z)
• Metacognitive Self-Correction for Multi-Agent System via Prototype-Guided Next-Execution Reconstruction [58.51530390018909]
  Large Language Model based multi-agent systems excel at collaborative problem solving but remain brittle to cascading errors.<n>We present MASC, a metacognitive framework that endows MAS with real-time, unsupervised, step-level error detection and self-correction.
  arXiv  Detail & Related papers  (2025-10-16T05:35:37Z)
• Interactive Agents to Overcome Ambiguity in Software Engineering [61.40183840499932]
  AI agents are increasingly being deployed to automate tasks, often based on ambiguous and underspecified user instructions.<n>Making unwarranted assumptions and failing to ask clarifying questions can lead to suboptimal outcomes.<n>We study the ability of LLM agents to handle ambiguous instructions in interactive code generation settings by evaluating proprietary and open-weight models on their performance.
  arXiv  Detail & Related papers  (2025-02-18T17:12:26Z)
• Transparent and Coherent Procedural Mistake Detection [30.540514590818265]
  Procedural mistake detection (PMD) is a challenging problem of classifying whether a human user has successfully executed a task (specified by a procedural text)<n>We extend PMD to require generating visual self-dialog rationales to inform decisions.<n>Given the impressive, mature image understanding capabilities observed in recent vision-and-language models (VLMs), we curate a suitable benchmark dataset for PMD based on individual frames.
  arXiv  Detail & Related papers  (2024-12-16T16:13:55Z)
• Evaluating Generative Language Models in Information Extraction as Subjective Question Correction [49.729908337372436]
  We propose a new evaluation method, SQC-Score. Inspired by the principles in subjective question correction, we propose a new evaluation method, SQC-Score. Results on three information extraction tasks show that SQC-Score is more preferred by human annotators than the baseline metrics.
  arXiv  Detail & Related papers  (2024-04-04T15:36:53Z)
• PREGO: online mistake detection in PRocedural EGOcentric videos [49.72812518471056]
  We propose PREGO, the first online one-class classification model for mistake detection in egocentric videos. PREGO is based on an online action recognition component to model the current action, and a symbolic reasoning module to predict the next actions. We evaluate PREGO on two procedural egocentric video datasets, Assembly101 and Epic-tent, which we adapt for online benchmarking of procedural mistake detection.
  arXiv  Detail & Related papers  (2024-04-02T13:27:28Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.