arXiv:2606.14574v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly deployed as planners for autonomous agents in household environments. While existing benchmarks evaluate whether LLM-generated plans execute successfully, they overlook a critical type of failure: latent failures. Unlike immediate failures that trigger instant feedback at execution time and enable timely correction, latent failures do not immediately halt plan execution but silently compromise goal achievement. In severe cases, they cause irreversible harm. To address this gap, we introduce SIMMER,
As LLMs become ubiquitous in autonomous agents, identifying and mitigating subtle planning failures is critical for safe and effective deployment.
This research highlights a crucial, often overlooked, vulnerability in LLM-driven autonomous systems, affecting their reliability and trustworthiness.
The introduction of SIMMER provides a new benchmark for evaluating LLM planning capabilities beyond immediate errors, pushing for more robust agentic AI.
- · AI safety researchers
- · Developers of autonomous agents
- · Industries deploying LLMs in critical applications
- · Robust AI model developers
- · LLM developers ignoring latent failures
- · Benchmarks focusing only on immediate plan success
- · Companies deploying brittle agentic AI prematurely
Improved debugging and robustness of LLM-powered autonomous agents.
Increased investor and public confidence in AI agents as their reliability grows.
Accelerated adoption of AI agents in sensitive domains, leading to new market opportunities and ethical considerations.
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Read at arXiv cs.AI