arXiv:2607.08173v1 Announce Type: new Abstract: Black box auditing of language models is an essential pre-deployment tool, but it may miss subtle forms of misalignment and hidden information. To better elicit hidden information during an auditing process, we introduce \emph{overthinking}: the process of using reasoning task vectors to amplify the propensity to think out loud of reasoning models. Given the parameters of a non-reasoning instruct model $M$ and reasoning-distilled model $R$, we define the \emph{overthinking model} as $\boldsymbol{\theta}_{\mathcal{O}_\alpha} = \boldsymbol{\theta}_
The increasing sophistication and black-box nature of large language models necessitate advanced auditing techniques to ensure safety and alignment before widespread deployment.
Sophisticated readers should care because this method offers a new approach to identify and mitigate hidden biases, misinformation, or unintended behaviors in AI models, which is crucial for ethical and secure AI integration.
The ability to 'overthink' models can reveal more about their internal reasoning processes, potentially speeding up both beneficial capabilities and the detection of misalignments.
- · AI Safety Researchers
- · AI Auditing Firms
- · Developers of Reasoning Models
- · Governments/Regulators
- · Malicious Actors (seeking to hide model flaws)
- · AI Developers (if models reveal significant flaws)
Increased transparency into AI model decision-making processes.
Faster identification and remediation of AI safety and ethical concerns, leading to more trustworthy AI systems.
Reduced risk of AI-induced systemic failures or societal harm due to undetected biases or hidden agendas within advanced models.
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Read at arXiv cs.AI