Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception

📰 ArXiv cs.AI

Springdrift is a persistent runtime for LLM agents with auditable execution, case-based memory, and normative safety

advanced Published 7 Apr 2026

Action Steps

Implement append-only memory for auditable execution
Integrate case-based reasoning memory layer with hybrid retrieval
Develop deterministic normative calculus for safety gating with auditable axiom trails
Utilize continuous ambient self-perception via structured self-state representation

Who Needs to Know This

AI engineers and researchers working on LLM agents can benefit from Springdrift's auditable and safe runtime, enabling more reliable and trustworthy AI systems

Key Insight

💡 Springdrift provides a reliable and trustworthy runtime for LLM agents, enabling auditable and safe execution

Key Takeaways

Springdrift is a persistent runtime for LLM agents with auditable execution, case-based memory, and normative safety

Full Article

Title: Springdrift: An Auditable Persistent Runtime for LLM Agents with Case-Based Memory, Normative Safety, and Ambient Self-Perception

Abstract:
arXiv:2604.04660v1 Announce Type: new Abstract: We present Springdrift, a persistent runtime for long-lived LLM agents. The system integrates an auditable execution substrate (append-only memory, supervised processes, git-backed recovery), a case-based reasoning memory layer with hybrid retrieval (evaluated against a dense cosine baseline), a deterministic normative calculus for safety gating with auditable axiom trails, and continuous ambient self-perception via a structured self-state represen

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