Overview
AgentCity retrieves relevant papers based on user-specified keywords, migrates their implementations into LibCity under unified interfaces, and evaluates the migrated models with automated hyperparameter tuning.
Key Features
Literature Retrieval
Automatically retrieves and analyzes relevant research papers based on your specified keywords and research topics.
Model Migration
Seamlessly migrates model implementations into LibCity with unified interfaces and consistent coding standards.
Hyperparameter Tuning
Automated hyperparameter optimization ensures models achieve their best performance on target datasets.
Architecture
Global Coordinator
AgentCity is organized around a Global Coordinator, which executes three stages in sequence. Each stage is managed by a Stage Leader Agent that is responsible for planning the workflow, delegating tasks, and monitoring progress.
Concrete operations are carried out by specialized Subagents, each designed for a narrowly defined function (e.g., paper analysis, code adaptation, or evaluation).
Literature Retrieval
Search and analyze academic papers, extract relevant methodologies and implementations.
Model Migration
Adapt and integrate model implementations into LibCity's unified framework with proper interfaces.
Hyperparameter Tuning
Optimize model parameters and evaluate performance across multiple benchmark datasets.
Robustness & Refinement
To ensure robustness under heterogeneous inputs, each stage supports a bounded refinement loop. When a stage encounters unsatisfactory outcomes—such as low-quality search results, migration errors, or unstable evaluation performance—the Stage Leader analyzes the feedback and selectively re-invokes relevant Subagents to refine the result.
These refinement loops are explicitly bounded by a maximum number of iterations, ensuring predictable execution time and preventing uncontrolled retries.
Intermediate results from each stage are summarized into structured notes and passed to downstream stages by the Global Coordinator. This design allows later stages to reuse upstream decisions and constraints without reprocessing the same information, while keeping stage responsibilities clearly separated.