EMBER: Autonomous Cognitive Behaviour from Learned Spiking Neural Network Dynami

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

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:2604.00555v1 Announce Type: new Abstract: Enterprise adoption of Large Language Models (LLMs) is constrained by hallucination, domain drift, and the inability to enforce

arXiv:2605.00402v1 Announce Type: cross Abstract: Spiking Neural Networks (SNNs) provide a promising framework for energy-efficient and biologically grounded computation; however,

arXiv:2603.30031v1 Announce Type: new Abstract: Current autonomous AI agents, driven primarily by Large Language Models (LLMs), operate in a state of cognitive weightlessness:

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:2608.27857v1 Announce Type: new Abstract: Spiking neural networks (SNNs) offer a path to energy-efficient language modeling through sparse encoding and event-driven