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EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents

EgoBench introduces the first interactive multimodal benchmark for tool-using agents, featuring 1,045 egocentric video tasks and a simulated user environment to rigorously evaluate the synergy of perception, reasoning, and dynamic interaction, revealing that current state-of-the-art models struggle significantly with these complex capabilities.

Original authors: Yunqi Liu, Tong Niu, Zitong Wang, Zhenlong Dai, Yuqi Qing, Weiqiang Wang, Jian Liu

Published 2026-05-28

📖 5 min read🧠 Deep dive

CC BY 4.0

Original authors: Yunqi Liu, Tong Niu, Zitong Wang, Zhenlong Dai, Yuqi Qing, Weiqiang Wang, Jian Liu

Original paper licensed under CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are trying to teach a robot butler how to help you cook dinner or shop for groceries while you are wearing a camera on your head. You want the robot to not only see what you are doing but also think about what to do next, use tools to check a database (like a recipe book or a price list), and talk back to you if it gets confused.

The paper introduces EgoBench, which is essentially a giant, very difficult "final exam" for these robot butlers. Here is a breakdown of what the paper does, using simple analogies:

1. The Problem: The "Too Easy" Tests

Imagine you are training a dog. If you only test it by asking it to "sit" on a quiet, empty field, it might look like a genius. But in the real world, the dog needs to sit while a car honks, a squirrel runs by, and you give it a complex command like "sit, then fetch the ball, but only if it's red."

The authors say that current tests for AI agents are like the quiet field. They are too simple. They don't test if the AI can:

See what's happening in a messy, moving video (like a first-person view of your hands cooking).
Think through a multi-step logic puzzle (e.g., "If the tomato is red, check the fridge; if it's expired, buy a new one").
Talk to a human who might be impatient, forgetful, or give confusing hints.

2. The Solution: EgoBench (The "Obstacle Course")

EgoBench is a new testing ground built specifically to see if AI agents can handle the chaos of real life. It has three main parts:

The Video (The Eyes): Instead of static pictures, the AI watches short, first-person videos (like GoPro footage from your head). It's like the AI is wearing your glasses. It has to figure out, "Oh, that's a tomato on the left, and I just picked it up."
The Tools (The Hands): The AI can't just guess. It has to use "tools" (like a digital phonebook or a database) to find hidden info. For example, the video shows a bottle of wine, but the video doesn't say if it's expired. The AI must use a tool to check the database.
The Simulated User (The Mouth): This is the clever part. The paper built a "fake human" (a computer program acting like a person) to talk to the AI. This fake human can be:
- Easy: Just asking questions politely.
- Hard: Being impatient, giving irrelevant info ("By the way, the weather is nice!"), or getting angry if the AI is too slow.
- Static: Giving all instructions at once in one big paragraph.

3. The Grading System: No Cheating Allowed

In many AI tests, a smart computer (another AI) reads the answer and says, "That sounds good!" This is subjective.

EgoBench uses a deterministic grading system. Think of it like a video game score:

Process Check: Did the AI call the right tools? (Did it check the price? Did it check the expiry date?)
Result Check: Did the final database state match the goal? (Is the item actually in the shopping cart? Is the recipe updated?)
If the AI says "I did it" but the database doesn't show the change, it gets a zero. No arguing with the teacher.

4. The Results: The "Reality Check"

The authors tested 8 of the smartest, most advanced AI models available today on this exam.

The verdict? They failed miserably.

Even the best model only got about 19% to 30% of the tasks right, depending on how hard the "fake human" was being.
In the easiest scenario (Retail/Shopping), the best model still only got about 30% right.

Why did they fail? The paper found the AI agents struggled with:

Misinterpreting the video: "I thought that was a tomato, but it was actually a red pepper."
Hallucinating: Making up facts that weren't in the video or the database.
Bad Logic: Getting the "If/Then" rules wrong.
Risky Moves: Doing things the user didn't ask for, like deleting items from a list.

Summary

The paper argues that while AI is getting good at talking and looking at pictures, it is still very bad at combining those skills to actually do things in a messy, real-world environment where it has to talk to a human, use tools, and follow complex rules. EgoBench is the new ruler we are using to measure exactly how far we still have to go.

Important Note: The paper only presents this benchmark and the test results. It does not claim that these AI agents are ready to be used in hospitals, for self-driving cars, or in any other specific real-world application yet. It simply says, "Here is a test, and here is how poorly the current top models are doing on it."

Technical Summary: EgoBench

Problem Statement

As AI agents transition from structured, text-based workflows to open, real-world environments, they require a synergistic integration of multimodal perception, tool invocation with multi-hop reasoning, and dynamic user interaction. Existing benchmarks fail to jointly evaluate these capabilities due to three primary challenges:

Capability Coupling: It is difficult to design tasks that genuinely require the simultaneous use of visual understanding, logical reasoning, and tool invocation, as models often exploit shortcuts or rely on single capabilities.
Realistic Interaction Simulation: Generating user feedback that is both natural and strictly aligned with task goals is challenging given the unpredictability of agent behavior.
Objective Evaluation: Extracting quantifiable metrics from dynamic, subjective interactions (e.g., dialogue) is difficult yet essential for reproducible assessment.

Current benchmarks often lack multimodal grounding, dynamic user interaction, or objective result-based validation, leaving a gap in evaluating agents for situated, first-person (egocentric) scenarios.

Methodology

The authors introduce EgoBench, the first interactive multimodal benchmark for tool-using agents grounded in egocentric video. The benchmark is constructed through a three-stage synergistic pipeline and evaluated via a deterministic joint validation framework.

1. Benchmark Construction

Egocentric Video Collection (Stage 1): The dataset comprises 1,045 tasks across four daily scenarios: Dining, Kitchen Operations, Food Ordering, and Retail. Videos (82.4% self-collected via smart glasses/phones, 17.6% from Ego4D) feature explicit spatiotemporal cues and are limited to 10–30 seconds to reflect wearable device usage.
Tool Library & Database (Stage 2): A scenario-specific tool library is designed with principles of functionality and parameter fuzziness (allowing partial matches). Crucially, a visual-data information gap is introduced: visible attributes are extracted from videos, while invisible but contextually relevant data (e.g., nutritional info, shelf life) is stored in a structured database. This forces agents to use tools to retrieve information not visible in the video.
Task Design (Stage 3): Tasks are designed to enforce a four-step dependency chain:
1. Multimodal perception of the video.
2. Retrieval of invisible information via tools.
3. Logical reasoning (conditional branching, multi-attribute filtering).
4. Database state modification via tools.
  Ground truth is defined as a sequence of key tool calls and the final database state, annotated by humans with rigorous quality checks.

2. Multi-Agent Simulated User

To evaluate dynamic interaction, EgoBench employs a multi-agent simulated user with an Actor-Evaluator-Summarizer structure:

Actor: Generates user responses based on dialogue history and task goals.
Evaluator: Assesses the Actor's output against four binary criteria: Role Consistency, Instruction Following, Resilience to agent interference, and Contextual Robustness. Violations trigger regeneration.
Summarizer: Maintains a compact history summary to track task progress.
The benchmark supports three interaction modes: Static (all info at once), Dynamic Easy (standard multi-turn), and Dynamic Hard (introducing impatience, noise, and off-topic distractions).

3. Joint Validation Framework

Evaluation is deterministic and objective, avoiding subjective LLM judges. Success is measured by:

Process-Based Validation: Checking if the agent's tool-call sequence covers all necessary ground-truth calls ( $\tau^* \subseteq \tau$ ).
Result-Based Validation: Verifying if the final database state matches the ground truth state via hashing.
Joint Success Rate: The primary metric, requiring both process coverage and result equivalence.

Key Contributions

EgoBench Benchmark: A comprehensive dataset of 1,045 egocentric video-grounded tasks spanning four real-world scenarios, jointly evaluating multimodal perception, multi-hop reasoning, and dynamic interaction.
Synergistic Task Generation: A pipeline ensuring every task requires the integrated application of visual perception and tool-augmented reasoning, preventing shortcut exploitation.
High-Fidelity User Simulation: A multi-agent framework that generates robust, goal-aligned user responses, supporting varying interaction complexities (Static, Dynamic Easy, Dynamic Hard).
Deterministic Evaluation: A joint validation framework that guarantees objective assessment through process-and-result equivalence, coupled with a multi-dimensional error analysis categorizing failures into five modes (perceptual misinterpretation, hallucination, structural non-compliance, logical fallacies, risky operations).

Experimental Results

The authors benchmarked eight state-of-the-art video-MLLMs (including Gemini-3.1-Pro, Qwen3.5, and Doubao-Seed-2.0-Pro) on EgoBench.

Performance Ceiling: Current models exhibit severe limitations. The best-performing model, Gemini-3.1-Pro, achieved a Joint Success Rate of only 30.62% in the Retail scenario (the easiest scenario) and an average of 19.43% across all four scenarios.
Interaction Modes: Models performed worst in Dynamic Hard Mode, revealing deficiencies in robust dialog management and interference-resistant state tracking. Surprisingly, Static Mode did not significantly improve accuracy for many models, suggesting that the multi-turn interaction mechanism in Dynamic Easy Mode sometimes compensates for planning deficits by breaking down tasks.
Error Analysis: Failures were predominantly caused by Multimodal Perceptual Misinterpretations and Logical Fallacies. While models generally followed output formats (structural compliance), they struggled with resolving spatial/temporal references in egocentric videos and executing complex conditional logic.
Efficiency: Token consumption was negatively correlated with success rates; higher-performing models (like Gemini-3.1-Pro) achieved better results with fewer tokens, indicating more efficient reasoning and less redundant exploration.

Significance

The paper positions EgoBench as a critical step toward evaluating AI agents for real-world deployment. By exposing a substantial gap between current model capabilities and the requirements for reliable, end-to-end task completion in open environments, the benchmark highlights specific bottlenecks—particularly in multimodal grounding and complex reasoning—that must be addressed. The authors assert that EgoBench provides a rigorous, objective framework to guide future model development, moving beyond text-only or static evaluations to assess agents in situated, interactive, and tool-mediated contexts.