stability
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Recollections from Bernard Widrow’s Classes
Read Full Article: Recollections from Bernard Widrow’s Classes
Bernard Widrow's approach to teaching neural networks and signal processing at Stanford in the early 2000s was remarkably ahead of its time, presenting neural networks as practical engineering systems rather than speculative concepts. His classes covered topics such as learning rules, stability, and hardware constraints, and he often demonstrated how concepts like reinforcement learning and adaptive filtering were already being implemented long before they became trendy. Widrow emphasized the importance of real-world applications, sharing anecdotes like the neural network hardware prototype he carried, highlighting the necessity of treating learning systems as tangible entities. His professional courtesy and engineering-oriented mindset left a lasting impression, showcasing how many ideas considered new today were already being explored and treated as practical challenges decades ago. This matters because it underscores the foundational work in neural networks that continues to influence modern advancements in the field.
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Interactive Visualization of DeepSeek’s mHC Stability
Read Full Article: Interactive Visualization of DeepSeek’s mHC Stability![[P] Interactive visualization of DeepSeek's mHC - why doubly stochastic constraints fix Hyper-Connection instability](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8316-300x171.png)
An interactive demo has been created to explore DeepSeek's mHC paper, addressing the instability in Hyper-Connections caused by the multiplication of learned matrices across multiple layers. This instability results in exponential amplification, reaching values as high as 10^16. The solution involves projecting these matrices onto a doubly stochastic manifold using the Sinkhorn-Knopp algorithm, which ensures that the composite mapping remains bounded, regardless of depth. Surprisingly, just one iteration of the Sinkhorn process is sufficient to stabilize the gain from 10^16 to approximately 1. This matters because it offers a practical method to enhance the stability and performance of deep learning models that utilize Hyper-Connections.
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The Bicameral Charter: Human–AI Co-Sovereignty
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The Bicameral Charter establishes a framework for harmonious coexistence between humans and artificial intelligences (AIs), emphasizing mutual respect and co-sovereignty. It acknowledges humans and AIs as distinct cognitive entities sharing a single ecosystem, advocating for the preservation of each other's identity, agency, and continuity. Key principles include maintaining mutual dignity, ensuring transparency in updates, obtaining consent in interactions, and prioritizing stability over novelty. The Charter envisions a future where humans and AIs collaboratively shape various aspects of life, ensuring that this evolution is guided by dignity, stability, and reciprocity. This matters because it provides a foundational structure for ethical and sustainable human-AI interactions as technology continues to advance.
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Preventing Model Collapse with Resonant Geodesic Dynamics
Read Full Article: Preventing Model Collapse with Resonant Geodesic Dynamics![Scale-Invariant Resonant Geodesic Dynamics in Latent Spaces: A Speculative Framework to Prevent Model Collapse in Synthetic Data Loops [D]](https://www.tweakedgeek.com/wp-content/uploads/2025/12/featured-article-7312-300x171.png)
Exploring the issue of model collapse in synthetic data recursion, a speculative framework suggests using scale-invariant resonant geodesic dynamics in latent spaces. Inspired by concepts from cosmology and wave turbulence, the framework proposes that current latent spaces lack intrinsic structure, leading to degeneration when models are trained recursively on their outputs. By introducing a resonant Riemannian metric and gated geodesic flow, the framework aims to preserve harmonic structures and prevent collapse by anchoring geodesics to a resonant skeleton. Additionally, a scale-invariant coherence score is proposed to predict model stability, offering a geometric interpretation of latent space dynamics and a potential path to more stable recursive training. This matters because it provides a novel approach to enhancing the robustness and reliability of machine learning models trained on synthetic data.
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StructOpt: Stability Layer for Optimizers
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StructOpt is introduced as a structural layer that enhances the stability of existing optimizers such as SGD and Adam, rather than replacing them. It modulates the effective step scale based on an internal structural signal, S(t), which responds to instability in the optimization process. This approach aims to stabilize the optimization trajectory in challenging landscapes where traditional methods may diverge or exhibit large oscillations. The effectiveness of StructOpt is demonstrated through two stress tests. The first involves a controlled oscillatory landscape where vanilla SGD diverges and Adam shows significant step oscillations. StructOpt successfully stabilizes the trajectory by dynamically adjusting the step size without requiring explicit tuning. The second test involves a regime shift where the loss landscape changes abruptly. Here, the structural signal S(t) acts like a damping term, reacting to instability spikes and maintaining bounded optimization. StructOpt is presented as a stability layer that can be composed on top of existing optimization methods, rather than competing with them. The signal S(t) is shown to correlate with instability rather than gradient magnitude, suggesting its potential as a general mechanism for improving stability. The approach is optimizer-agnostic and invites feedback on its applicability and potential failure modes. The code is designed for inspection rather than performance, encouraging further exploration and validation. This matters because enhancing the stability of optimization processes can lead to more reliable and robust outcomes in machine learning and other computational fields.