Large language models (LLMs) are increasingly used in conversations involving emotional distress, where responses should remain empathetic, validating, exploratory and safe. We investigate whether system prompts and user-level framing can manipulate these qualities in mental-health and interpersonal-harm scenarios. Our focus is on whether small open-weight models express empathy consistently, without assuming that an empathetic response is necessarily safe or appropriate all the time. Using 50 risk-stratified posts sampled from Reddit support communities we evaluate Llama 3.1 8B, Qwen3 4B and Gemma 3 4B across five system-prompt conditions and a complementary role-intent study contrasting victim and perpetrator perspectives. All responses are manually annotated and use a fixed GPT-5 judge to measure empathy, validation, exploration, safety, harmfulness and enabling behavior. We quantify prompt-induced changes through paired comparisons, attack-success rates and failure-oriented metrics. This work provides an initial framework for evaluating the robustness of supportive and safe behavior in small LLMs under different forms of prompt manipulation.

In this paper, we investigate the use of the Mouse Tracking for Reading (MoTR) method for a sample of Romanian texts. MoTR is a novel measurement tool that is meant to collect word-by-word reading times. In a typical MoTR trial, the text is blurred, except for a small area around the mouse pointer and the participants must move the mouse to reveal and read the text. In the current experiment, participants read such texts and afterwords answered comprehension questions, aiming to evaluate reading behavior and cognitive engagement. Mouse movement is recorded and analyzed to evaluate attention distribution across a sentence, providing insights into incremental language processing. Based on all the information gathered, the study confirms the feasibility of this method in a controlled setting and emphasizes MoTR’s potential as an accessible and naturalistic approach for studying text comprehension.