AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Evidence is interpreted for ``superficial belief'' in LLM decision-making: models behave as if guided by probabilistic local priorities over attributes, while having only limited verbal access to the attributes that drive their decisions.
This paper introduces diagnostic word-ablation metrics to quantify this phenomenon and proposes a data-centric solution that can alleviate the observed overcorrection in stance-aware argument retrieval and demonstrates that, for sufficiently powerful models, this approach can alleviate the observed overcorrection.
This study develops and test a novel theory of debate judgement applicable to all settings where agents engage in debates by providing pros and cons for their opinions therein and indicates argumentation semantics as an ideal candidate for principled judges in debate-driven AI.
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