Control-Plane Placement Shapes Forgetting: An Architectural Study of Agent Memory Across Thirteen System Configurations
arXiv:2606.15903v1 Announce Type: new Abstract: Where an LLM sits in an agent memory pipeline -- between the recall plane that retrieves stored facts (extensively benchmarked) and the control plane that mutates them via supersede, release, purge (largely untested) -- shapes which forgetting failure modes the system recovers. Comparing thirteen system configurations on a 385-case adversarial surface, we observe three placement regimes with partly complementary coverage: deterministic primitives suffice for lexical/temporal categories but fail canonicalization (5% on identifier-obfuscation, 0% o
The rapid advancement of AI agents and LLMs necessitates a deeper understanding of their memory architectures as they become more autonomous and complex.
Understanding how control-plane placement affects agent memory and forgetting is crucial for developing robust, reliable, and safe AI agents capable of long-term operation.
This research provides architectural insights into how AI agent memory systems can be designed to mitigate forgetting, moving beyond simple recall to active mutation and management of information.
- · AI Agent Developers
- · Autonomous System Architects
- · AI Safety Researchers
- · Enterprise AI Adopters
- · Inefficient AI Agent Architectures
- · Systems Prone to Catastrophic Forgetting
- · Developers Relying Solely on Recall-Plane Optimization
Improved stability and performance of AI agents in complex, long-running tasks.
Accelerated development of more sophisticated and trustworthy autonomous AI systems across various industries.
Enhanced AI agent capabilities could lead to new forms of automation and human-AI collaboration, impacting white-collar work and specialized expert tasks.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.CL