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TOPIC · ENTITY #1649

agent skills

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RESEARCH · 1 SOURCE · arXiv cs.AI

SkillNEO: non-elitist evolutionary optimization of agent skills (arXiv:2610.20926v1)

arXiv:2610.20926v1 presents SkillNEO (Non-Elitist Evolutionary Optimization for Agent Skills), which decouples which skill version is executed from which versions are retained for further edits so that low-scoring intermediates can be revised rather than discarded. On 86 SkillsBench tasks across eight domains (e.g., software engineering and office work), SkillNEO raises GPT-5.4 Mini's average reward from 49.1% to 66.4%; reused modified skills improve GPT-5.6 Luna by 10.5 percentage points on 41 modified tasks and place GPT-5.5 10.6 points above its published original-skill reference.

7.0

MODELS · 1 SOURCE · InfoQ AI, ML & Data Engineering

Integrating DMN Decision Models, LLMs and NeMo to Produce Auditable Agentic Architectures

Alex Porcelli outlines a response to a key enterprise AI problem—non-deterministic outputs and lack of accountability—by describing how combining DMN decision models with LLMs, agent skills and NeMo guardrails can yield auditable, deterministic agentic architectures that let business leaders own decision logic while engineers retain architectural governance.

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