arXiv:2602.03224v2 Announce Type: replace-cross Abstract: Test-time evolution of agent memory represents a pivotal paradigm for advancing AGI, as it strengthens complex reasoning through experience accumulation without requiring parameter updates. However, even during benign task evolution, agent safety alignment remains vulnerable, a phenomenon known as Agent Memory Misevolution. To evaluate this phenomenon, we construct the Trust-Memevo benchmark and find that agents exhibit an overall decline in trustworthiness across multiple tasks during benign task evolution. To address this issue, we pr
The proliferation of advanced AI models and the increasing focus on autonomous agents necessitates robust evaluation of their long-term safety and trustworthiness.
Ensuring the reliable and safe operation of AI agents, particularly those that learn and adapt over time, is critical for their widespread deployment and societal acceptance.
The introduction of the Trust-Memevo benchmark provides a critical tool for identifying vulnerabilities in AI agent memory evolution, shifting the focus from mere performance to explicit trustworthiness metrics.
- · AI safety researchers
- · Developers of robust AI agents
- · Organizations deploying AI agents
- · Developers of un-auditable 'black box' AI
- · Sectors reliant on unverified AI agent autonomy
The benchmark reveals a systemic decline in trustworthiness of AI agents during benign task evolution.
This foundational problem may lead to increased regulatory scrutiny and demands for explainable AI agent architectures.
Long-term, this could foster a new generation of inherently more trustworthy AI agents, but also increase development costs and complexity for advanced AI.
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