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ai-research-preprints

topic2 events
papersTODAY 04:00 UTC

Dreaming in Code: Curriculum Learning for Open-Ended Worlds

A revised arXiv paper proposes a method for generating training environments as code, aiming to support curriculum learning in open-ended settings where agents face a continually expanding space of tasks. It builds on prior work that uses foundation models to programmatically produce diverse environments, extending it toward structured curricula. The work is a research preprint and has not been peer-reviewed.

curriculum-learning/open-ended-learning/foundation-models/arxiv
papersSEP 12 04:00 UTC

arXiv Paper Introduces Headroom-Closed Index for Recursive Self-Improvement

A new arXiv preprint examines recursive self-improvement, the idea that AI systems can convert feedback and experience into lasting upgrades to both their abilities and their future improvement process. The author proposes a metric called the Headroom-Closed Index (HCI) to frame and diagnose a problem in this area. The work is a conceptual research contribution rather than a released model or product.

research/self-improvement/arxiv/evaluation