arXiv:2606.11195v1 Announce Type: cross Abstract: Large language models (LLMs) have transformed how humans access information, but not how we reason with it. Their fluency accelerates consumption while bypassing the slow, reflective processes that underpin sound judgment. This paper introduces Relational Reflective Intelligence (RRI), an inference-time governance layer that operationalizes reflection through auditable reasoning loops. RRI operates not inside the model but around it, providing a practical structure for stable, auditable reasoning between humans and LLMs. The core premise is tha
The rapid advancement and widespread deployment of large language models (LLMs) have exposed their limitations in stable reasoning, prompting immediate innovation to address this critical gap.
This development introduces a modular approach to improve LLM reliability and auditable reasoning, which is crucial for their integration into high-stakes decision-making processes.
The focus is shifting from pure LLM consumption to augmented, reflective human-AI interaction, creating a new layer of control and oversight around AI outputs.
- · AI governance platforms
- · Enterprises adopting AI for critical functions
- · Auditors and compliance firms
- · AI research focused on reliability
- · Companies deploying unmanaged LLMs
- · Developers solely focused on model fluency
- · Trust in black-box AI systems
- · Simple AI consumption models
Widespread adoption of external AI governance layers improving the trustworthiness of LLM applications.
Increased demand for explainable AI and auditable AI systems, driving new industry standards.
Reconfiguration of human-AI collaboration models, with humans actively guiding and validating AI reasoning, rather than passively consuming outputs.
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Read at arXiv cs.AI