Online platforms continue to witness harmful expressions targeting LGBTQ+ individuals, particularly in the form of homophobic and transphobic comments. While detection of such content has received substantial attention, generating constructive counter-narratives remains comparatively underexplored. In this shared task, we focus on counter-narrative generation in English and Tamil. Participants were provided with social media comments labeled as homophobic or transphobic and were required to generate respectful, contextually appropriate responses that challenge prejudice and promote empathy. Systems were evaluated using both reference-based metrics (Distinct-2 and BERTScore-F1) and rubric-based human evaluation metrics measuring politeness (PRS), quality (QS), and contextual coherence (CCNC). The results demonstrate variation in system performance across languages, with English systems showing stronger lexical diversity and Tamil systems excelling in politeness and contextual coherence. This paper presents dataset statistics, evaluation methodology, system performance analysis, and key observations from the shared task.