arXiv:2607.01988v1 Announce Type: new Abstract: Long-running adaptive intelligent agents face a structural tension between knowledge consolidation and information integrity. Memory consolidation is conventionally treated as an agent-changing operation: a model is fine-tuned, a prompt rewritten, a policy distilled, or a reflection appended to the context that governs future behaviour. In regulated autonomic deployment this is a liability because the agent operates under commitments and audit contracts that bind to a specific, cryptographically certified identity. We propose to treat consolidati
The increasing deployment of autonomous intelligent agents in critical sectors necessitates robust solutions for verifiable memory consolidation without compromising their operational integrity and audit trails.
This research addresses a fundamental tension in agent design, enabling advanced AI systems to adapt and learn while maintaining compliance and accountability, which is crucial for regulated industries and national security applications.
The proposed method allows for continuous agent learning and improvement without 'identity drift', making AI agents more reliable and auditable for practical, long-term deployment in sensitive environments.
- · AI agents developers
- · Regulated industries
- · Audit and compliance platforms
- · Defence and security sectors
- · AI systems prone to unverified drift
- · Organizations relying on opaque AI learning
- · Black-box AI audit methodologies
This enables faster and more secure deployment of self-improving AI agents in highly regulated industries by addressing critical liability concerns.
It will accelerate the development of 'always-on' AI systems that can continuously learn and adapt in real-world scenarios without requiring frequent recertification.
The concept of cryptographically certified agent identity could become a standard requirement for all autonomous systems operating in public or critical infrastructures, fostering trust but also potentially centralizing control over AI identities.
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Read at arXiv cs.AI