Empirical analysis across 15 LLMs and 1,141 skills identifies a logarithmic routing decay law and a multiplicative execution law coupled by a single fitted slope parameter b that enables targeted library optimizations improving routing accuracy and downstream task pass rates.
Scaling laws for educational AI agents
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Aethelgard is a learned governance system that scopes AI agent capabilities to the minimum needed for each task type using PPO policy training on audit logs.
citing papers explorer
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The Scaling Laws of Skills in LLM Agent Systems
Empirical analysis across 15 LLMs and 1,141 skills identifies a logarithmic routing decay law and a multiplicative execution law coupled by a single fitted slope parameter b that enables targeted library optimizations improving routing accuracy and downstream task pass rates.
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Beyond Static Sandboxing: Learned Capability Governance for Autonomous AI Agents
Aethelgard is a learned governance system that scopes AI agent capabilities to the minimum needed for each task type using PPO policy training on audit logs.