Benchmarking Edge LLM State Collapse: Drift Gauntlet & CSMS
Drift Gauntlet benchmarks recursive LLM state collapse on Jetson Orin Nano edge hardware, while CSMS runtime governance prevents it with near-zero latency cost.
Researchers examined how continuously running autonomous agents, such as vision-language-action systems, degrade over long recursive inference loops on constrained edge hardware, accumulating state drift and schema corruption. To quantify this, they built Drift Gauntlet, a model-agnostic benchmark that tests whether a model can preserve a deterministic JSON world state across 50 recursive cycles, tracking schema integrity, resistance to contradicting fixed constants, and a Euclidean-distance-based drift metric called Logos Drift.
To address the failure mode, they introduce CSMS (Cognitive State Manifold Safeguard), a hardware-native runtime governance layer. Since direct Hessian computation isn't practical with black-box inference engines, CSMS relies on an embedding covariance spectral stability proxy to monitor state-space expansion and intervene only when drift spikes.
Tested on an NVIDIA Jetson Orin Nano across ten open-weight model families ranging from 1B to 8B parameters, CSMS cut TinyLlama's schema failures from 22 to just 1, added effectively zero latency overhead for stable models, kept overhead around 10% even under heavy stress, and reduced mean step latency from 13.74s to 2.18s under memory pressure.
The findings suggest that runtime reliability is becoming a mandatory infrastructure layer for continuous robotics and agentic AI workloads, making stability-preserving governance without performance loss a key design concern for edge deployments.