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Research Paper#Deep Learning, State Space Models, Memory Optimization🔬 ResearchAnalyzed: Jan 3, 2026 19:16

Breaking the Memory Wall for SSMs with Phase Gradient Flow

Published:Dec 28, 2025 20:27
1 min read
ArXiv

Analysis

This paper addresses a critical memory bottleneck in the backpropagation of Selective State Space Models (SSMs), which limits their application to large-scale genomic and other long-sequence data. The proposed Phase Gradient Flow (PGF) framework offers a solution by computing exact analytical derivatives directly in the state-space manifold, avoiding the need to store intermediate computational graphs. This results in significant memory savings (O(1) memory complexity) and improved throughput, enabling the analysis of extremely long sequences that were previously infeasible. The stability of PGF, even in stiff ODE regimes, is a key advantage.
Reference

“PGF delivers O(1) memory complexity relative to sequence length, yielding a 94% reduction in peak VRAM and a 23x increase in throughput compared to standard Autograd.”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 25, 2025 20:47

I Solved an 'Impossible' Math Problem with AI

Published:Dec 23, 2025 09:29
1 min read
Siraj Raval

Analysis

This article, presumably by Siraj Raval, claims to have solved an "impossible" math problem using AI. Without further context on the specific problem, the AI model used, and the methodology, it's difficult to assess the validity of the claim. The term "impossible" is often used loosely, and it's crucial to understand what kind of impossibility is being referred to (e.g., computationally infeasible, provably unsolvable within a certain framework). A rigorous explanation of the problem and the AI's solution is needed to determine the significance of this achievement. The article needs to provide more details to be considered credible.
Reference

“I Solved an 'Impossible' Math Problem with AI”

PermalinkSiraj Raval
Research#llm📝 BlogAnalyzed: Jan 3, 2026 06:22

Evolution Strategies

Published:Sep 5, 2019 00:00
1 min read
Lil'Log

Analysis

The article introduces black-box optimization algorithms as alternatives to stochastic gradient descent for optimizing deep learning models. It highlights the scenario where the target function's analytic form is unknown, making gradient-based methods infeasible. The article mentions examples like Simulated Annealing, Hill Climbing, and Nelder-Mead method, providing a basic overview of the topic.
Reference

“Stochastic gradient descent is a universal choice for optimizing deep learning models. However, it is not the only option. With black-box optimization algorithms, you can evaluate a target function $f(x): \mathbb{R}^n \to \mathbb{R}$, even when you don’t know the precise analytic form of $f(x)$ and thus cannot compute gradients or the Hessian matrix.”

PermalinkLil'Log