ReliAgent Bench
A benchmark for evaluating the reliability of long-term memory systems in AI agents, focusing on scenarios such as stale-memory resolution and state-conflict handling. The benchmark was constructed by an independent developer.
Timeline (last 90 days)
The author built the memory system TypedMem and the evaluation benchmark ReliAgent Bench.
Among all failure cases, stale-memory resolution accounted for 37%.
After introducing an explicit resolver to handle information supersession relationships, stale-memory failures dropped to zero and overall accuracy jumped from 33% to 78%.
The views and data in this article come from a single developer's post on Reddit, and ReliAgent Bench has not yet undergone extensive third-party verification.
All Facts (4)
The author built the memory system TypedMem and the evaluation benchmark ReliAgent Bench.
50%UnverifiedAmong all failure cases, stale-memory resolution accounted for 37%.
50%UnverifiedAfter introducing an explicit resolver to handle information supersession relationships, stale-memory failures dropped to zero and overall accuracy jumped from 33% to 78%.
50%UnverifiedThe views and data in this article come from a single developer's post on Reddit, and ReliAgent Bench has not yet undergone extensive third-party verification.
50%