HippoCamp: Benchmarking Contextual Agents on Personal Computers
Summary: 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 2,000 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.
Key Features of HippoCamp
- Evaluation of agents in user-centric environments.
- Utilization of 42.4 GB of diverse real-world files.
- Construction of 581 QA pairs for evaluating agent capabilities.
- Provision of 46.1K annotated structured trajectories for failure analysis.
- Assessment of various state-of-the-art multimodal large language models.
Significance of the Findings
The findings from the HippoCamp benchmark are significant for the development of personal AI systems. The limitations identified in current agents highlight the need for improved models that can effectively handle the complexities of user-centric data management. The report emphasizes:
- The gap in accuracy, with top models achieving only 48.3% in user profiling.
- The challenges faced in long-horizon retrieval tasks.
- The difficulties associated with cross-modal reasoning in personal file systems.
- The importance of addressing multimodal perception and evidence grounding issues.
Future Directions
Based on the insights gained from HippoCamp, researchers and developers are encouraged to focus on:
- Enhancing multimodal perception capabilities of agents.
- Improving evidence grounding techniques to support better decision-making.
- Developing more effective strategies for long-horizon retrieval in personal file systems.
- Creating personalized AI assistants that can adapt to individual user needs and contexts.
In conclusion, HippoCamp serves as a foundational tool for advancing the field of personal AI, pushing the boundaries of what is possible in user-centric file management and contextual reasoning.