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Graph-native Python reimplementation of the Information Dynamics of Music (IDyOM) model that represents predictive memories as explicit graph objects for musical expectation modeling and network analysis.
Physics-aware end-to-end deep reinforcement learning methodology for quadcopter control with actuator dynamics modeling.
Reinforced Dreamer methodology for asymmetric reinforcement learning using latent guidance to improve world model representations and behaviors in model-based RL.
Based on SOC occupation classification
Showing SKILL.md
| name | agentic-evolution-path-evolving-llms |
| description | Agentic Evolution is the Path to Evolving LLMs |
| metadata | {"arxiv_id":"2601.10007","published":"January 2026","authors":"","tags":[]} |
This skill is based on the arXiv paper: 2601.10007 - "Agentic Evolution is the Path to Evolving LLMs" (January 2026).
TODO: Extract key innovations from the paper.
TODO: Describe the methodology from the paper.
TODO: Discuss the implications of the work.
TODO: List potential pitfalls and limitations.
agentic-evolution-path-evolving-llms, agentic, evolution, is, the, path, to, evolving, llms
arXiv:2601.10007 - "Agentic Evolution is the Path to Evolving LLMs" (January 2026)
