SeekBrain: AI Framework to Accelerate Neuroscience Discovery
SeekBrain, a novel artificial intelligence framework, has been launched to enhance discoveries in neuroscience by automating the examination of intricate multimodal datasets. This autonomous multi-agent system employs domain-specific hierarchical planning alongside cross-modal data analysis to create hypotheses and analytical workflows as needed. It actively builds a collection of analysis recipes derived from code-paper pairs, merging expert knowledge with planning and execution capabilities. In comprehensive assessments using the expert-annotated BrainArena benchmark, SeekBrain significantly surpassed leading agent baselines in multiple analysis tasks. Furthermore, it has been implemented in real-world research environments, showcasing its practical effectiveness. This innovation tackles the increasing difficulties related to diverse data and fragmented processes in contemporary neuroscience. The related paper can be found on arXiv with the identifier 2607.29347.
Key facts
- SeekBrain is an autonomous multi-agent framework for neuroscience discovery.
- It uses domain-grounded hierarchical planning and cross-modal data analysis.
- The framework dynamically constructs analysis recipes from code-paper pairs.
- SeekBrain outperforms state-of-the-art agent baselines on the BrainArena benchmark.
- It has been deployed in real-world research settings.
- The paper is available on arXiv with ID 2607.29347.
- The system addresses challenges of heterogeneous data and fragmented workflows.
- It generates hypotheses and analytical pipelines on demand.
Entities
Institutions
- arXiv