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Beyond Surface Alignment: Grounding the Dynamics of Situational Understanding and Generative Control in LLMs

ArXiv|Sunday, August 30, 2026|2 days ago|Chenghao Yang

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Why It Matters

The current alignment tuning paradigm for Large Language Models (LLMs) prioritizes surface-level behaviors -- fluency, safety, and tonal consistency.

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Key Claims

While effective for casual chat, this thesis argues that such surface alignment masks a lack of grounding, creating models that are stylistically confident but situationally brittle.
We propose a framework of Grounded Alignment, analyzing how models process context (Input) and structure generation (Output), then aligning these grounded behaviors to human needs.
First, we evaluate failures in Situational Grounding.

Original description

The current alignment tuning paradigm for Large Language Models (LLMs) prioritizes surface-level behaviors -- fluency, safety, and tonal consistency. While effective for casual chat, this thesis argues that such surface alignment masks a lack of grounding, creating models that are stylistically confident but situationally brittle. We propose a framework of Grounded Alignment, analyzing how models process context (Input) and structure generation (Output), then aligning these grounded behaviors to human needs. First, we evaluate failures in Situational Grounding. SitTest shows that despite large...

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ID: http://arxiv.org/abs/2608.29610v1 · Indexed about 2 hours ago