Building MnemeBrain — belief memory for AI agents
Most AI memory systems store:
• notes • vector embeddings • retrieved context
But they do not maintain beliefs.
When new evidence appears, the system usually overwrites the previous memory.
Real agents should instead track:
- conflicting evidence
- belief revision
- uncertainty
- temporal change
Memory ≠ belief maintenance.
MnemeBrain explores a belief layer for AI agents.
Instead of storing isolated facts, it maintains belief states backed by evidence.
Core concepts:
- Evidence graphs
- Belief nodes
- Belnap four-valued logic
- Contradiction detection
- Confidence + temporal decay
- Belief revision
This allows agents to reason about conflicting information over time.
Agent / LLM
│
▼
MnemeBrain
│
┌─────────────────────┐
│ Belief Graph │
│ Evidence Tracking │
│ Truth States │
│ Revision Engine │
│ Temporal Decay │
└─────────────────────┘
Think of it as a belief system for long-lived agents.
Belief memory architecture for AI agents.
A benchmark for belief dynamics in AI memory systems.
Includes task scenarios such as:
- contradiction detection
- belief revision
- evidence lifecycle
- temporal decay
- extraction from noisy conversations
https://github.com/mnemebrain/mnemebrain-benchmark
Blog posts about agent memory architecture:
Current exploration:
• belief systems for AI agents • contradiction handling in memory • long-lived agent architectures • evaluation of agent memory systems
Previously worked on:
• smart contracts • zero-knowledge systems • Web3 infrastructure
X / Twitter https://x.com/Iamdev_ai