Continual Graph Memory Agent for AI Research Replication

Replicating modern AI research is often a multi-hour, error-prone process that hinges on locating implementation-critical details dispersed across text, code, and execution logs. Recent methods on AI research replication expose a recurring bottleneck: agents repeatedly rediscover the same reusable implementation patterns across papers. To address this challenge, we present the Congram — a continual graph memory agent for paper replication that accumulates reusable "recipes" from papers and generated code, allowing continual experience collection and reuse for future code generation. Evaluated on papers from PaperBench, Congram demonstrates significant improvements in token usage (on average, 7763 tokens less for prompt and 1724 tokens less for completion) for code generation compared to the zero-shot setting while preserving comparable replication quality scores.