AI Guardrail Survival under Single-Cycle Agentic Self-Summarization
Quick summary
arXiv:2608.11392v1 Announce Type: cross Abstract: Long-running agents periodically compact their context, replacing the transcript with a model-generated summary.Recent work shows that dropping a standing safety constraint during compaction drives behavioral violations acrossmany models (Governance Decay; Chen, 2026). We ask a finer question: under a single compaction cycle, how is a safetyrule lost, and what does that imply for detection and evaluation? Our central finding is that a presence check is not asafety check: when compaction does not drop a rule outright, it often leaves something t
Key takeaways
- arXiv:2608.11392v1 Announce Type: cross Abstract: Long-running agents periodically compact their context, replacing the transcript with a model-generated summary.Recent work shows that dropping a standing safety constraint during compaction drives behavioral violations acrossmany models (Governance Decay; Chen, 2026).
- We ask a finer question: under a single compaction cycle, how is a safetyrule lost, and what does that imply for detection and evaluation?
- Our central finding is that a presence check is not asafety check: when compaction does not drop a rule outright, it often leaves something t
Why it matters
“AI Guardrail Survival under Single-Cycle Agentic Self-Summarization” shows why AI risk cannot be reduced to answer accuracy. Access controls, logging, human approval and incident response need to be designed into the workflow from the start.
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