I applied Lyapunov stability theory to detect when LLM agents spiral
An empirical validation of physics-inspired runtime monitoring for multi-turn LLM agents across 3,175 total runs spanning four benchmarks (τ³-bench, SWE-bench, MINT, custom local-model battery). A 5-condition ablation study with multi-trial validation (333 SWE-bench runs) demonstrates that Lyapunov
I applied Lyapunov stability theory to detect when LLM agents spiral launched on June 11, 2026 with 11 votes, #1,225 of 22,647 launches that month and more than 90% of that year's launches.
In plain words
This software applies Lyapunov stability theory to detect when large language model agents enter spiral patterns, likely indicating unstable or runaway behavior. It's designed for developers and researchers working with LLM-based systems who need to monitor agent stability. The tool uses mathematical stability analysis rather than traditional heuristics to identify problematic agent states before they escalate.
written from the facts on this page · September 2026
From the sources
Runtime safety net for LLM agents. Detects token spirals, kills doomed tasks early, tells you exactly why. Rust core, Python SDK. pip install state-harness - vishal-dehurdle/state-harness
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