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Quantizing Time-Series Models As Dynamical Systems: Traje...
[Submitted on 11 Jun 2026 (v1), last revised 12 Jun 2026 (this v · 2026-06-15 · via cs.LG updates on arXiv.org

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Abstract:We introduce the Trajectory-based Quantization Sensitivity Score (TQS), a metric that reframes post-training quantization (PTQ) through the lens of dynamical-systems stability. By modeling the network's rollout as a discrete-time dynamical system, TQS characterizes how quantization-induced errors propagate and amplify over the rollout horizon. Unlike conventional PTQ methods, where sensitivity analysis is often coupled to the quantization procedure, TQS enables a priori sensitivity estimation decoupled from quantizer selection and bit-width assignment. This separation allows for quantization budget planning even for black-box or compiled networks with fused operators. Building on this, we present TQS-PTQ, a flexible mixed-precision framework that requires no calibration data or costly second-order approximations. Our experiments show that a dynamical-systems perspective provides a robust, high-performing pathway for low-precision deployment in resource-constrained settings.

Submission history

From: Mariya Pavlova [view email]
[v1] Thu, 11 Jun 2026 12:53:03 UTC (445 KB)
[v2] Fri, 12 Jun 2026 13:19:29 UTC (445 KB)