HippoCamp: Benchmarking Contextual Agents on Personal Computers
arXiv:2604.01221v1 Announce Type: new Abstract: We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal file management. Unlike existing agent benchmarks that focus on tasks like web interaction, tool use, or software automation in generic settings, HippoCamp evaluates agents in user-centric environments to model individual user profiles and search massive personal files for context-aware reasoning. Our benchmark instantiates device-scale file systems over real-world profiles spanning diverse modalities, comprising 42.4 GB of data across over 2K real-world files. Building upon the raw files, we construct 581 QA pairs to assess agents' capabilities in search, evidence perception, and multi-step reasoning. To facilitate fine-grained analysis, we provide 46.1K densely annotated structured trajectories for step-wise failure diagnosis. We evaluate a wide range of state-of-the-art multimodal large language models (MLLMs) and agentic methods on HippoCamp
arXiv:2604.01221v1 Announce Type: new Abstract: We present HippoCamp, a new benchmark designed to evaluate agents' capabilities on multimodal file management. Unlike existing agent benchmarks that focus on tasks like web interaction, tool use, or software automation in generic settings, HippoCamp evaluates agents in user-centric environments to model individual user profiles and search massive personal files for context-aware reasoning. Our benchmark instantiates device-scale file systems over real-world profiles spanning diverse modalities, comprising 42.4 GB of data across over 2K real-world files. Building upon the raw files, we construct 581 QA pairs to assess agents' capabilities in search, evidence perception, and multi-step reasoning. To facilitate fine-grained analysis, we provide 46.1K densely annotated structured trajectories for step-wise failure diagnosis. We evaluate a wide range of state-of-the-art multimodal large language models (MLLMs) and agentic methods on HippoCamp. Our comprehensive experiments reveal a significant performance gap: even the most advanced commercial models achieve only 48.3% accuracy in user profiling, struggling particularly with long-horizon retrieval and cross-modal reasoning within dense personal file systems. Furthermore, our step-wise failure diagnosis identifies multimodal perception and evidence grounding as the primary bottlenecks. Ultimately, HippoCamp exposes the critical limitations of current agents in realistic, user-centric environments and provides a robust foundation for developing next-generation personal AI assistants.
Executive Summary
This article presents HippoCamp, a benchmark designed to evaluate agents' capabilities on multimodal file management in user-centric environments. The benchmark utilizes real-world profiles and 42.4 GB of data across 2K files to assess agents' search, evidence perception, and multi-step reasoning abilities. The authors evaluate state-of-the-art multimodal large language models and agentic methods, revealing a significant performance gap, particularly with long-horizon retrieval and cross-modal reasoning. The study highlights the limitations of current agents in realistic environments and provides a foundation for developing next-generation personal AI assistants.
Key Points
- ▸ HippoCamp is a new benchmark designed to evaluate agents' capabilities on multimodal file management
- ▸ The benchmark utilizes real-world profiles and 42.4 GB of data across 2K files
- ▸ State-of-the-art multimodal large language models and agentic methods show a significant performance gap
Merits
Strength in Design
The authors' creation of a realistic, user-centric environment for evaluating agents' capabilities is a significant strength of the study.
Robust Foundation
The provision of 46.1K densely annotated structured trajectories for step-wise failure diagnosis offers a robust foundation for developing next-generation personal AI assistants.
Demerits
Limited Generalizability
The study's focus on multimodal file management may limit its generalizability to other domains and tasks.
Expert Commentary
HippoCamp is a significant contribution to the field of AI research, as it provides a much-needed benchmark for evaluating agents' capabilities in user-centric environments. The study's findings highlight the limitations of current agents and identify areas for improvement, particularly in multimodal perception and evidence grounding. The provision of densely annotated structured trajectories for step-wise failure diagnosis offers a valuable resource for developers seeking to create more effective personal AI assistants. However, the study's focus on multimodal file management may limit its generalizability to other domains and tasks.
Recommendations
- ✓ Future research should aim to develop more effective multimodal perception and evidence grounding techniques for personal AI assistants.
- ✓ Researchers should consider developing benchmarks that evaluate agents' capabilities in a broader range of tasks and domains.
Sources
Original: arXiv - cs.AI
