When answering factual questions, language models may respond correctly, hallucinate, or abstain. We investigate whether this epistemic disposition is encoded in hidden states before any answer token is generated. Using a balanced Wikipedia QA corpus with pre/post article-date splits and rollout-consensus labels, we analyze prompt-only MLP activations from four instruction-tuned 7–9B models. PCA reveals a dominant pre-generation epistemic routing axis that orders outcomes as ABSTAIN, HALLUCINATION, and CORRECT. We then apply activation steering as a bounded causal stress test. Dense residual-write edits can shift many hallucinations toward abstention or correctness, while sparse MLP-intermediate edits localize the steering signal. These results frame the axis as a steering audit: it identifies where fixed edits improve factual behavior and where correct loss or broken outputs call for conditional controllers.

Realignment techniques are often employed to enhance cross-lingual transfer in multilingual language models, still, they can sometimes degrade performance in languages that differ significantly from the fine-tuned source language. This paper introduces AlignFreeze, a method that freezes either the layers’ lower half or upper half during realignment. Through controlled experiments on 4 tasks, 3 models, and in 35 languages, we find that realignment affects all the layers but can be the most detrimental to the lower ones. Freezing the lower layers can prevent performance degradation. Particularly, AlignFreeze improves Part-of-Speech (PoS) tagging performances in languages where full realignment fails: with XLM-R, it provides improvements of more than one standard deviation in accuracy in seven more languages than full realignment.

Answering “Where is the X button?” with “It’s next to the Y button” is unhelpful if the user knows neither location. Useful answers require obvious landmarks as a reference point. We address this by generating from a vehicle dashboard diagram a spatial knowledge graph (SKG) that shows the spatial relationship between a dashboard component and its nearby landmarks and using the SKG to help answer questions. We evaluate three distinct generation pipelines (Per-Attribute, Per-Component, and a Single-Shot baseline) to create the SKG using Large Vision-Language Models (LVLMs). On a new 65-vehicle dataset, we demonstrate that a decomposed Per-Component pipeline is the most effective strategy for generating a high-quality SKG; the graph produced by this method, when evaluated with a novel Significance score, identifies landmarks achieving 71.3% agreement with human annotators. This work enables downstream QA systems to provide more intuitive, landmark-based answers.