neural-networks

A research blog post finding that frontier LLMs can natively read and write neural network weights — diagnosing trained policies, editing weights to fix reasoning loops and boost Qwen3.5-0.8B's MATH-500 from 7.9% to 48.1%, and hand-writing small game-playing networks — a capability the authors call Weight-Space Programming.

SAT-ACT is a learning framework for CDCL branching that extracts action preferences from outcome-derived rollouts at the same solver state, trains a state-conditioned network to guide branching, and reduces search costs while improving PAR-2 on SAT Competition benchmarks.

The paper proves a general frequency principle for finite linear approximation classes under gradient descent, showing high-frequency amplitudes vanish uniformly during training and establishing sufficient conditions for low-frequency priority, validated numerically with Legendre and trigonometric bases.

The paper introduces CUTTIE, a hierarchical testing method that clusters a trained network's units into a tree and tests them top-down with conditional independence tests to localize predictive concepts (e.g., regulatory DNA motifs) while controlling the family-wise error rate, demonstrating it on a model predicting chromatin accessibility from sequence.

The paper introduces AutoHebb, a class of self-modifying neural networks using a three-factor Hebbian learning rule gated by an autonomously generated neuromodulatory signal, which achieves zero-shot inference and continual learning while outperforming competing models.

A Reddit post shares the UnEZ paper, a fully unsupervised and directly interpretable neural network similar to an autoencoder but with an interpretable latent space, and asks the community about related architectures.

The developer behind the live evolution sim EDEN evolved its creatures' tiny neural network brains offline for 300 generations, raising win rates on previously unseen rounds (e.g., Ravagers from 7% to 62%, Nomads from 42% to 95%). The key fix was that stream founders previously got random brains each round, discarding evolved wiring; co-evolution across species is the next step.

The tweet shared a complete AI degree curriculum that Andrej Karpathy made freely available on GitHub, consisting of 7 repositories arranged in learning order, covering everything from backpropagation to agents capable of conducting research autonomously, with over 300,000 stars combined.

Show HN: A project that compiles integer neural networks into ZDoom maps so DOOM's voodoo dolls and sector properties perform the arithmetic, verified bit-exact against NumPy. It's extremely slow but fully verifiable, and lets players draw digits for DOOM to classify in real time.

展示了一个16神经元小网络逐步学会分离双螺旋的可视化视频，并介绍其由开源的SenseNova 6.8 Code2Video模型通过代码自动生成，包括叙事节奏。

This paper combines cryptanalytic weight-direction extraction with label-based distillation to reconstruct fully connected neural networks, showing that Leaky ReLU enables neuron recovery where ReLU fails, and demonstrating high prediction agreement on synthetic and CIFAR-10 victim models.

该论文定义了一种与 Fisher 信息矩阵对齐的参数分布，使得在保证低损失的同时获得与 Fisher 信息矩阵零特征值数量成正比的参数对数体积，从而得到一个随 Fisher 信息矩阵期望秩缩放的高概率 PAC 泛化误差界。

The paper shows that sparsity-inducing regularization causes functional modularity to emerge in multitask neural networks, identifying compact task-selective subnetworks ('functional tokens') that can be recombined across pathways without retraining.

A video comparing KS3, the latest hermite spline regression block in the K-Splanifolds series, against MLPs on fitting a 4D Mandelbulb over 9,000 gradient descent steps, with KS3 achieving the lowest loss at nearly identical forward/backward latency.

该论文提出一种跨尺度学习的非线性波形融合方法，用于通感一体化（ISAC）中的图像传输与多目标定位，在测试中将归一化定位误差降低约29%，同时提升图像PSNR。

MIT News专访探讨一篇发表于《Past and Present》的新论文，该论文追溯了AI幻觉的历史起源：早期计算机工程师和心理学家有意在神经网络中注入随机性，以模拟人类思维的创造性与不可预测性，并指出创造力与可预测性之间存在必然的权衡。

This paper studies predictive flow matching under distribution drift, proposing DyProd-FM which refits on dyadic historical prefixes and uses a sleeping Prod master to achieve minimax-optimal dynamic regret under both relative velocity risk variation and target-law variation, without requiring convexity or curvature assumptions.

该论文基于等变度数理论（equivariant degree theory）提出了一种通用的拓扑对称性指数，用于刻画神经网络损失景观中临界轨道的各向同性及其净拓扑贡献，适用于任意网络架构并给出软激活 MLP、Transformer 和等变神经网络的计算示例。

提出SNTO框架，将状态、伴随和设计变量用神经网络联合表示并优化，结合多GPU硬件感知训练，实现可扩展的应力约束拓扑优化，可处理高达八百万配置点的三维大规模问题。

Handwired 是一个可以把普通 JavaScript 代码（如 DOM 渲染的迷你桌面应用）直接转化为神经网络的工具，可对界面交互进行训练和优化。