arXiv AI

Seq103: A Unified Neuroevolution Framework for Compact Sequence Architecture Discovery

arXiv:2606. 07664v1 Announce Type: cross Abstract: Neuroevolution is a representative neural architecture search paradigm that evolves both network topology and weights through evolutionary algorithms.

arXiv Machine Learning

Jul 17

NeuronSoup: Evolving Asynchronous, Shared-Neuron Temporal Graphs without Backpropagation

arXiv:2607. 15217v1 Announce Type: cross Abstract: We present NeuronSoup, a neural computation architecture that replaces synchronous layer-by-layer processing with asynchronous, delay-mediated signal propagation through a pool of shared neurons.

By Subodh Kalia

arXiv Machine Learning

Aug 11

Advancing Intelligent Sequence Modeling: Evolution, Trade-offs, and Applications of State-Space Architectures from S4 to Mamba

arXiv:2503. 18970v4 Announce Type: replace Abstract: Structured State Space Models (SSMs) have become a prominent class of sequence models, developed against two long-standing difficulties: the sequential computation and gradient propagation limits of Recurrent Neural Networks (RNNs), and the quadratic time and memory cost of self-attention in Transformers.

By Shriyank Somvanshi, Md Monzurul Islam, Mahmuda Sultana Mimi, Sazzad Bin Bashar Polock, Gaurab Chhetri, Anandi Dutta, Amir Rafe, Subasish Das

llms

arXiv AI

Jul 10

Agentic Neural Architecture Search

arXiv:2607. 07984v1 Announce Type: new Abstract: Neural architecture search (NAS) methods have grown increasingly efficient, yet they remain bounded by manually engineered search spaces that require substantial domain expertise and must be rebuilt for every new task.

By Seokhoon Jeong, Mijung Kim, Taehwan Kim

llms agents computer-vision benchmarks

arXiv AI

Jun 10

LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks

arXiv:2606. 10294v1 Announce Type: cross Abstract: Deploying neural networks on unconventional hardware demands architectures that co-optimize task accuracy and platform-specific constraints such as energy cost, physical non-idealities, and numerical precision.

By Tyler King, Timothee Leleu

llms

arXiv Machine Learning

Jun 25

Frequency Domain Reservoir Computing

arXiv:2606. 24969v1 Announce Type: new Abstract: While the quadratic sequence-length bottleneck of transformers has fueled a resurgence in recurrent models, effectively capturing complex dynamics requires architectures that balance efficient training with highly expressive latent states.

By Klaus Schertler, Xiomara Runge, Andrea Ceni, David Kappel, Claudio Gallicchio

llms rag benchmarks

arXiv Machine Learning

Jul 9

LEMUR 2: Unlocking Neural Network Diversity for AI

arXiv:2607. 06839v1 Announce Type: new Abstract: Existing NAS benchmarks (e.

By Tolgay Atinc Uzun, Waleed Khalid, Saif U Din, Sai Revanth Mulukuledu, Akashdeep Singh, Chandini Vysyaraju, Raghuvir Duvvuri, Avi Goyal, Yashkumar Rajeshbhai Lukhi, Muhammad A. Hussain, Krunal Jesani, Usha Shrestha, Yash Mittal, Roman Kochnev, Pritam Kadam, Mohsin Ikram, Harsh R. Moradiya, Alice Arslanian, Dmitry Ignatov, Radu Timofte

llms rag diffusion fine-tuning multimodal benchmarks

arXiv AI

Aug 11

NeuroAda: Activating Each Neuron's Potential for Parameter-Efficient Fine-Tuning

arXiv:2510. 18940v2 Announce Type: replace-cross Abstract: Existing parameter-efficient fine-tuning (PEFT) methods primarily fall into two categories: addition-based and selective in-situ adaptation.

By Zhi Zhang, Yixian Shen, Congfeng Cao, Ekaterina Shutova

fine-tuning benchmarks

arXiv AI

Jul 10

Linear Attention Architectures: Mechanisms, Trade-offs, and Cross-Layer Routing

arXiv:2607. 07953v1 Announce Type: cross Abstract: Self-attention lets each token retrieve information from the full context, but its quadratic cost in sequence length limits training and inference at long context.

By Tommaso Cerruti, Tim Rieder, George Rowlands, Lingfeng Jin, Imanol Schlag

efficiency benchmarks

arXiv Machine Learning

Aug 4

GraphIR: Architecture-Level Search States for LLM-Guided Neural Architecture Evolution

arXiv:2608. 01633v1 Announce Type: new Abstract: Large language models (LLMs) enable neural architecture search (NAS) directly over executable neural network programs.

By Zhen Liu, Wanqi Zhou, Shuanghao Bai, Yuhan Liu, Jinjun Wang, Jingwen Fu

llms benchmarks

arXiv Machine Learning

Jun 11

On Subquadratic Architectures: From Applications to Principles

arXiv:2606. 12364v1 Announce Type: new Abstract: Transformers dominate modern sequence modeling, but their quadratic attention incurs substantial computational cost.

By Anamaria-Roberta Hartl, Levente Z\'olyomi, David Stap, Pieter-Jan Hoedt, Niklas Schmidinger, Lukas Hauzenberger, Sebastian B\"ock, G\"unter Klambauer, Sepp Hochreiter

llms efficiency

arXiv AI

Jul 9

Exploration of Fast-Slow Latent Recurrence for Train-Short, Test-Long Generalization

arXiv:2604. 01577v3 Announce Type: replace-cross Abstract: We study out of distribution generalization in streaming tasks where models are trained on short sequences but must operate over much longer, unknown horizons under bounded memory.

By Shota Takashiro, Masanori Koyama, Takeru Miyato, Yusuke Iwasawa, Yutaka Matsuo, Kohei Hayashi

llms reinforcement-learning safety

arXiv Machine Learning

Aug 6

Echo Flow Networks

arXiv:2509. 24122v3 Announce Type: replace Abstract: At the heart of time-series forecasting (TSF) lies a fundamental challenge: how can models efficiently and effectively capture long-range temporal dependencies across ever-growing sequences?

By Hongbo Liu, Jia Xu

benchmarks

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