Auto-FL-Research: Agentic Search for Federated Learning Algorithms

arXiv:2603.25334v1 Announce Type: new Abstract: Distributed intelligence in industrial networks increasingly integrates sensing, communication, and computation across

arXiv:2604.22748v1 Announce Type: new Abstract: As AI systems move from generating text to accomplishing goals through sustained interaction, the ability to model environment

arXiv:2605.15871v1 Announce Type: new Abstract: Toward recursive self-improvement, we investigate LLM agents autonomously designing foundation models beyond standard Transformers.

arXiv:2605.13848v1 Announce Type: new Abstract: Agentic LLM frameworks that rely on prompted orchestration, where the model itself determines workflow transitions, often suffer

arXiv:2604.05013v1 Announce Type: cross Abstract: Current LLM coding agents are predominantly trained on composite benchmarks (e.g., bug fixing), which often leads to

arXiv:2604.08590v1 Announce Type: cross Abstract: We present AlphaLab, an autonomous research harness that leverages frontier LLM agentic capabilities to automate the full

arXiv:2605.11518v1 Announce Type: new Abstract: Effectively configuring scalable large language model (LLM) experiments, spanning architecture design, hyperparameter tuning, and

arXiv:2605.00011v1 Announce Type: cross Abstract: Federated Learning (FL) enables collaborative intelligence across decentralized data source devices in a privacy-preserving way.