arXiv:2606.01202v1 Announce Type: cross Abstract: Language models do not simply choose an answer at the output layer. In a 9,000-trajectory MMLU study across Qwen2.5-7B-Instruct, Llama-3.1-8B-Instruct, and Mistral-7B-Instruct-v0.3, the score of the answer moves across depth in structured ways. We describe each trajectory with three quantities: the current answer margin, the next-layer change in that margin, and the distance from a decision flip. The main empirical picture is that correctness and stability are different: the largest group is unstable-correct, not stable-correct. A traced subset
The proliferation of language models and increasing scrutiny on their decision-making processes necessitate deeper understanding of their internal mechanics.
Understanding the internal 'thought processes' of large language models is crucial for improving their reliability, trustworthiness, and for diagnosing failure modes at scale.
This research provides a more granular view into how LLMs arrive at answers, moving beyond simple output layer analysis to trajectory-based insights, revealing unexpected patterns like 'unstable-correctness'.
- · AI researchers
- · Developers of safety & alignment tools
- · Companies using LLMs in critical applications
- · Developers relying solely on output-layer observation
- · LLM evaluators using simplistic metrics
Improved debugging and interpretability for language models will lead to more robust and reliable AI systems.
New evaluation metrics and training methodologies will emerge, specifically targeting decision stability and correctness across model layers.
The concept of 'unstable-correctness' might inform how we design human-AI collaboration, emphasizing the need for robust verification loops.
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Read at arXiv cs.CL