arXiv:2605.30771v1 Announce Type: new Abstract: AI agents that persist across sessions need memory they can retrieve, audit, update, and erase. Existing memory systems often collapse source evidence, extracted facts, retrieved context, and answer policy into one opaque prompt path, making failures difficult to diagnose: a wrong answer may come from missing evidence, unsupported extraction, stale state, retrieval loss, or answer-model behavior. We present Eywa, a provenance-grounded memory architecture built around evidence before belief. Eywa stores immutable source evidence before deriving ca
The rapid advancement and deployment of AI agents across various domains demand more robust and auditable memory systems to ensure reliability and trust.
This development addresses a critical vulnerability in autonomous AI systems, moving towards more transparent and debuggable AI, which is essential for mass adoption and regulatory acceptance.
AI agents will transition from opaque, 'black box' memory systems to transparent, 'white box' architectures that enable verifiable decision-making and easier failure diagnosis.
- · AI agents developers
- · Enterprises deploying AI agents
- · AI ethics and auditing firms
- · Software developers
- · AI systems with opaque memory architectures
- · Companies reliant on primitive AI memory solutions
Improved reliability and auditability of AI agents will accelerate their integration into sensitive and mission-critical applications.
New regulatory frameworks may emerge to mandate provenance-grounded memory for AI systems deployed in specific sectors.
The enhanced transparency could foster public trust in AI, potentially accelerating societal adoption and investment in autonomous systems.
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Read at arXiv cs.CL