Sharp Spectral Thresholds for Logit Fixed Points
The paper titled 'Sharp Spectral Thresholds for Logit Fixed Points' by Tongxi Wang addresses stability in softmax feedback systems. It identifies a previously overlooked stable regime and provides a new threshold for stability that extends beyond classical theories. This research enhances the understanding of phase transitions in reward-responsive systems.
- ▪The paper focuses on softmax feedback systems used in various mathematical applications.
- ▪It reveals a new stability threshold that differs from classical approaches.
- ▪The research fills a gap in understanding pre-bifurcation regimes in logit systems.
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Computer Science > Machine Learning arXiv:2605.15651 (cs) [Submitted on 15 May 2026] Title:Sharp Spectral Thresholds for Logit Fixed Points Authors:Tongxi Wang View a PDF of the paper titled Sharp Spectral Thresholds for Logit Fixed Points, by Tongxi Wang View PDF HTML (experimental) Abstract:Softmax feedback systems are a common mathematical core of entropy-regularized reinforcement learning, logit game dynamics, population choice, and mean-field variational updates. Their central stability question is simple: when does a self-reinforcing softmax system produce a unique and globally predictable outcome? Classical theory gives a conservative answer. By treating softmax as a unit-scale response, it certifies stability only in a strongly randomized regime.
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