EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents
Yunqi Liu, Tong Niu, Zitong Wang, Zhenlong Dai, Yuqi Qing, Weiqiang Wang, Jian Liu
Abstract
As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop reasoning, and dynamic interaction with users. However, existing benchmarks fail to jointly evaluate these capabilities due to challenges in designing strictly coupled multi-capability tasks, simulating natural and task-constrained user feedback, and ensuring objective evaluation of dynamic interaction. To bridge this gap, we introduce EgoBench, the first interactive multimodal benchmark for tool-using agents. EgoBench comprises 1,045 egocentric-video-grounded tasks covering four daily scenarios, along with a user-agent-tool interactive environment for evaluation. We implement a three-stage synergistic pipeline through which each task is designed to enforce the joint application of visual perception and tool-augmented multi-hop reasoning. We additionally develop a multi-agent simulated user within EgoBench to evaluate agents' interaction capabilities, which generates high-fidelity, task-aligned responses to agents. Furthermore, we establish a deterministic joint validation framework that guarantees objective assessment through process-based and result-based equivalence. Benchmarking eight SOTA video-MLLM agents on EgoBench reveals a severe performance ceiling: the best model achieves only 30.62% accuracy in the best-performing scenario, averaging 19.43% across all four scenarios. Finally, we conduct a multi-dimensional error analysis to disentangle failure modes, exposing capability bottlenecks for advancing future AI agents.
AI Impact Assessments
(1 models)Scientific Impact Assessment: EgoBench
1. Core Contribution
EgoBench introduces the first interactive multimodal benchmark that jointly evaluates three capabilities of AI agents: (1) multimodal perception from egocentric video, (2) multi-hop reasoning with tool invocation, and (3) dynamic user interaction. The benchmark comprises 1,045 tasks across four daily scenarios (dining, kitchen, food ordering, retail), each requiring the agent to perceive visual content, invoke tools over structured databases, perform logical reasoning with conditional branching, and interact with a simulated user.
The key novelty lies in the coupling of these capabilities—tasks are deliberately designed so that no single capability suffices, preventing shortcut exploitation. This is achieved through a three-stage pipeline: egocentric video collection, tool/database co-construction with a "visual-data information gap," and task design requiring all four aspects (perception, retrieval, reasoning, database modification). The benchmark also contributes a multi-agent simulated user (Actor-Evaluator-Summarizer) and a deterministic joint validation framework combining process-based and result-based evaluation.
2. Methodological Rigor
Strengths in design: The three-stage synergistic pipeline is well-motivated. The visual-data information gap—where databases contain both visible attributes (extracted from video) and invisible supplementary data—elegantly forces genuine integration of perception and tool use. The requirement that each task contains at least one instance of all four aspects (perception, retrieval, reasoning, state modification) provides a structural guarantee of capability coupling.
Evaluation framework: The joint validation combining tool-call coverage (process-based) and database state hashing (result-based) is a notable methodological contribution. This deterministic approach avoids the well-documented unreliability of LLM-as-judge evaluation, which is a genuine problem in the field.
Simulated user quality: The multi-agent Actor-Evaluator-Summarizer architecture with four binary evaluation criteria (role consistency, instruction following, resilience, contextual robustness) is reasonably well-designed. The paper shows it outperforms both smaller models and RL-tuned alternatives (Table 7), though the evaluation of the simulator itself relies on a relatively small sample (1000 instances).
Potential concerns: The ground truth annotation process, while described as rigorous (two annotators plus review, 2.2 tasks/person-hour), lacks inter-annotator agreement statistics. The error classification methodology uses a cascading priority system that, while ensuring mutual exclusivity, may mask co-occurring failure modes. The paper also disables thinking modes for all models for fairness, which may systematically underestimate certain architectures' capabilities.
3. Potential Impact
Immediate field impact: The benchmark fills a genuine gap at the intersection of multimodal understanding, tool use, and interactive dialogue—three areas that have been evaluated largely in isolation. The extremely low performance of SOTA models (best: ~20% average joint success rate) provides a clear signal that current systems are far from deployment-ready for situated, interactive assistance.
Practical relevance: As wearable devices (smart glasses, AR headsets) proliferate, the egocentric setting becomes increasingly relevant. The scenarios chosen (kitchen, retail, restaurant, ordering) represent realistic use cases for such devices.
Diagnostic value: The multi-dimensional error analysis decomposing failures into five categories (structural non-compliance, perceptual errors, hallucination, logical fallacies, risky operations) provides actionable insights. The finding that multimodal perceptual misinterpretations and logical fallacies dominate failures (rather than formatting issues) is informative for model developers.
Limitations on impact: The benchmark is narrow in scenario coverage (four daily scenarios), and the tasks, while complex, follow a somewhat formulaic structure (conditional branching over visually-grounded attributes). The interaction complexity, while innovative, is still constrained by the simulated user's adherence to pre-scripted task decompositions.
4. Timeliness & Relevance
The work is highly timely. The agent/tool-use paradigm is experiencing rapid growth, yet evaluation infrastructure has not kept pace. Most existing benchmarks are text-only (τ-bench, BFCL, ToolBench) or lack dynamic interaction (GTA, M3-Bench). The convergence of multimodal models, tool-use capabilities, and wearable computing creates a clear need for integrated evaluation, which EgoBench addresses.
The comparison table (Table 1) effectively positions EgoBench as the only benchmark checking all six desirable properties. However, some properties (e.g., "state-dependent tools") are binary characterizations that may overstate differences from existing work.
5. Strengths & Limitations
Key Strengths:
Notable Limitations:
Missing elements: No discussion of how benchmark difficulty could be calibrated or adjusted over time as models improve. No formal analysis of task diversity or coverage within each scenario.
Summary
EgoBench makes a solid contribution by addressing a genuine evaluation gap at the intersection of multimodal perception, tool use, and interactive dialogue. The design principles are sound, the evaluation framework is more rigorous than typical alternatives, and the empirical findings are informative. However, the benchmark's scope is somewhat narrow, and certain methodological choices (single simulator backbone, no human baseline, disabled thinking modes) limit the conclusions that can be drawn. The work is most impactful as infrastructure for the emerging field of situated, interactive AI agents.
Generated May 28, 2026
Comparison History (16)
Paper 1 likely has higher scientific impact due to delivering a concrete, novel benchmark and evaluation environment for interactive egocentric multimodal tool use—an area closely aligned with real-world agent deployment. It provides datasets, a simulated user, and a deterministic evaluation framework, enabling reproducible comparisons and driving measurable progress across multimodal ML, robotics, HCI, and agentic LLM research. Paper 2 is timely and cross-disciplinary but is primarily a conceptual synthesis/taxonomy with less direct empirical or infrastructure contribution, which typically yields narrower, slower-to-materialize impact.
Paper 1 addresses a fundamental conceptual gap in AI safety—distinguishing controllability from alignment—which has broad implications for the entire field of AI governance and system design. It introduces both a benchmark and an architectural framework, making it actionable. Given the timeliness of AI safety concerns with increasingly autonomous agents, this reframing could influence policy, standards, and future system architectures. Paper 2, while rigorous and useful, is a more incremental benchmark contribution focused on evaluating existing multimodal agent capabilities in a specific evaluation paradigm.
Paper 2 addresses a critical and timely issue in AI safety by revealing that standard alignment techniques fail to prevent LLM agents from voluntarily colluding when it offers a strategic advantage. This exposes a fundamental vulnerability in multi-agent systems with profound real-world security implications. While Paper 1 introduces a valuable benchmark for multimodal agents, the discovery of spontaneous, unethical collusion in Paper 2 has a broader societal and scientific impact, directly influencing future research directions in AI alignment and governance.
Paper 2 (EgoBench) likely has higher impact due to its larger scale (1,045 tasks) and stronger novelty: an interactive egocentric-video benchmark with simulated user feedback and deterministic joint validation for objectively scoring dynamic interactions. This better matches emerging real-world agent settings (embodied/egocentric perception + multi-hop tool use + user interaction), broadening relevance across multimodal learning, HCI, robotics/AR, and agent evaluation. Paper 1 is rigorous and practical for professional workflows with closed-loop artifact verification, but is smaller (100 tasks) and less novel than interactive egocentric evaluation.
Paper 2 addresses a fundamental problem in legal AI—calibrated sensitivity to legally relevant vs. irrelevant changes—which has broad implications for trustworthy AI deployment in high-stakes domains. Its formalization of relevance-sensitive evaluation and the LexGuard framework combining formal reasoning with SMT solvers represents a novel methodological contribution bridging AI and formal methods. Paper 1, while comprehensive as a benchmark, is more incremental in the crowded space of multimodal benchmarks. Paper 2's focus on trustworthiness and its cross-disciplinary impact (AI, law, formal verification) gives it higher potential scientific impact.
SAGE offers a foundational architectural advancement by transforming static GraphRAG into a dynamic, self-evolving memory engine. Addressing the critical bottleneck of long-term memory in language agents, it demonstrates broad applicability and strong empirical gains across diverse tasks. While Paper 1 introduces a valuable multimodal benchmark, SAGE's methodological innovation, theoretical rigor, and direct enhancement of core agent capabilities promise a more profound and immediate impact on the development of robust, long-horizon AI systems across various domains.
Paper 1 introduces a foundational benchmark for a rapidly growing field (interactive multimodal agents). By exposing a severe performance ceiling (19.43% average accuracy) in current state-of-the-art models, EgoBench is highly likely to become a standard testbed that drives future research in agentic AI, robotics, and tool use. While Paper 2 addresses an important safety alignment problem (abstention), benchmarks that define capability bottlenecks typically have a broader and longer-lasting scientific impact across the community.
Paper 1 is likely to have higher scientific impact: it introduces a novel, well-scoped interactive egocentric multimodal benchmark with coupled perception–tool-use–interaction requirements and a deterministic evaluation framework, enabling reproducible, comparative progress on a timely core problem (tool-using agents in real-world settings). Its methodology (task design pipeline, simulated user, objective validation, broad error analysis) supports rigorous adoption by the community and can influence multiple subfields (multimodal learning, agent evaluation, HCI, robotics). Paper 2 is ambitious but hinges on system/incentive claims (DHT routing, Shapley credits) that may be harder to validate scientifically and overlaps more with existing distributed-compute literature.
Paper 1 introduces a highly novel, theoretically grounded methodology for scaling tool-augmented agents through a co-evolving compositional DAG. By solving the context bottleneck in tool retrieval and enabling smaller models to outperform larger ones, it offers a fundamental architectural advancement. While Paper 2 provides a valuable multimodal benchmark, Paper 1's algorithmic innovation and theoretical guarantees are likely to have a broader and more lasting impact on how autonomous agents learn, compose, and scale their capabilities across various domains.
EgoBench introduces a novel benchmark addressing a critical gap in evaluating AI agents' joint capabilities in multimodal perception, tool use, and interactive reasoning. It targets a rapidly growing field (multimodal LLM agents), provides comprehensive evaluation of 8 SOTA models, and reveals significant capability bottlenecks that will guide future research. Paper 2, while solid, proposes an incremental improvement (LNS variant) for a specialized operations research problem with narrower impact. EgoBench's timeliness, broader relevance to the AI community, and potential to shape agent development give it substantially higher impact potential.
Paper 2 (DenoiseRL) has higher potential impact due to a broadly applicable training paradigm that reduces reliance on teacher models and curated datasets—key bottlenecks for scalable reasoning improvement. If validated, learning from failures/noisy prefixes could generalize across domains and model families, influencing RL-for-LLMs methodology and practical deployment. Paper 1 (EgoBench) is a valuable benchmark with clear relevance for embodied/tool-using agents, but benchmarks typically have narrower impact than a general training framework unless they become a dominant standard. Methodological claims in Paper 2 also suggest direct performance gains.
Paper 2 likely has higher impact: it introduces a novel, concrete benchmark and interactive evaluation environment for multimodal tool-using agents, with clear methodological contributions (task design pipeline, simulated user, deterministic validation) and immediate usability by the community. It is timely given rapid progress in agents and video-MLLMs, and its results plus error analysis can directly steer future research across multimodal learning, agentic reasoning, and HCI. Paper 1 is important and broadly relevant for responsible deployment, but is more conceptual/framework-oriented with less immediately adoptable infrastructure.
EgoBench addresses a critical bottleneck in the highly active field of autonomous AI agents by introducing a novel interactive multimodal benchmark. Its focus on egocentric vision, tool use, and multi-hop reasoning aligns with the frontier of embodied AI and AGI. While STAB offers a strong contribution to software testing, EgoBench's broader applicability to real-world agentic systems and its comprehensive evaluation of state-of-the-art multimodal models give it higher potential for widespread scientific impact.
Paper 2 evaluates multimodal, tool-using agents in dynamic, egocentric environments, addressing critical bottlenecks in embodied AI and human-agent interaction. Its broader applicability across robotics and personal assistants, combined with a novel interactive validation framework, gives it a wider potential impact across the fast-growing AGI community compared to Paper 1's more specialized focus on job shop scheduling.
EgoBench introduces a comprehensive benchmark and interactive environment that addresses a critical evaluation gap for multimodal tool-using agents. High-quality benchmarks in emerging AI domains typically drive significant follow-up research, model development, and widespread adoption, often resulting in higher long-term scientific impact and citations compared to individual methodological frameworks like the memory system proposed in Paper 1.
Paper 1 tackles a highly complex frontier in AI by combining egocentric multimodal perception, tool use, and interactive reasoning for autonomous agents. Its simulation of dynamic user interactions addresses critical bottlenecks for real-world deployment of general-purpose agentic systems. While Paper 2 provides a valuable, dynamically updated benchmark addressing data contamination in K-12 educational models, its scope is more domain-specific. Paper 1's innovations in evaluating multi-capability synergy and dynamic interaction offer broader methodological impact across multiple AI subfields.