SearchEyes
What it is
SearchEyes is a framework for developing multimodal deep search agents that can perform multi-hop reasoning. It uses a unified typed knowledge graph to simultaneously handle data synthesis, environment simulation, and reward signal generation.
The problem it solves
Addresses the structural disconnect in training pipelines where data synthesis, search environments, and reward signals are developed independently, leading to poor long-horizon reasoning.
What you could use it for
- Multi-hop visual search queries requiring reasoning across multiple images and knowledge entities
- Simulating complex search environments for training AI agents
- Generating high-quality multi-hop datasets with complex structural constraints
Read the source ↗Back to the library →
Summary written by an AI model from the source; check the source before you rely on it.