arXiv:2605.25869v1 Announce Type: new Abstract: Long-term memory is essential for persistent LLM agents, yet prevailing architectures store historical interactions as unstructured, flat text. This unconstrained storage induces provenance-role collapse, a critical failure mode where agents suffer from source-monitoring errors. To resolve this cognitive vulnerability at the architectural level, we propose MemIR, a typed Memory Intermediate Representation that operationalizes source monitoring as a structural constraint. MemIR writes long-term memory into grounded atoms that separate raw evidence
The rapid advancement and deployment of LLM agents are exposing critical architectural limitations, leading to research focused on foundational improvements like robust memory systems.
Improving long-term memory and mitigating 'provenance-role collapse' is crucial for the reliability, trustworthiness, and advanced capabilities of autonomous AI agents.
This research introduces a structured approach to agent memory that could prevent fundamental reasoning errors, enhancing agent performance and applicability.
- · AI agent developers
- · Enterprises deploying AI agents
- · AI research institutions
- · Developers of unstructured memory systems
- · Companies with less sophisticated AI agent architectures
More reliable and capable long-term AI agents become possible, performing complex, multi-step tasks with fewer errors.
Increased trust in AI agents could accelerate their adoption across sensitive domains like finance, healthcare, and critical infrastructure.
As agents become more autonomous and reliable, they may accelerate the collapse of white-collar workflows, leading to significant economic restructuring.
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Read at arXiv cs.CL