We describe a submission to the AstroCLIMB shared task at WASP 2026. The task requires classifying a pair of astronomy figures and/or captions as the same figure, from the same paper, from citation-related papers, or from unrelated papers. Our system casts classification as constrained next-token prediction with Qwen3-VL and adapts the model using 4-bit quantization and low-rank adapters. On the 10,000-pair Kaggle test set, three Qwen3-VL-8B models with language-attention adapters obtain 0.73230–0.73307 macro-F1 individually. Averaging their four-class probabilities raises the score to 0.73604, and masking the impossible same-figure class for caption–caption and image–image pairs produces 0.73725. This paper documents empirical system engineering rather than proposing a new learning algorithm; broader visual adaptation, restricted-loss blending, and citation-focused auxiliary training do not improve the result.

Large multimodal models tend to hallucinate visual detail fluently, which limits their deployment for fine-grained interpretation. We present HALDETECT, our system for the English hallucination-detection track (Task 1b) of ImageEval 2026, in which a system must identify, from an image and three culturally plausible statements, the single visually grounded one. We frame the item as one contrastive decision, emit the answer before its explanation, and structure reasoning around colour/texture, shape/form, and context. Our best submitted adapter fine-tunes Qwen2.5-VL-7B-Instruct with 4-bit QLoRA while freezing the vision encoder and reaches Contrastive Instability (CI) 0.035 on the 1,000-item test set; we placed third of eight teams. Development experiments show that answer order can matter more than model scale and that adaptation beats prompting alone. Retrospective paired analysis of the released gold labels confirms the QLoRA gain over the best prompt but not the small gap between the devtest-selected and best-test adapters, and reseeding all four training sizes shows that the apparent data-scaling curve does not survive a seed change. The 35 residual errors are culturally plausible function, material, and recognition distinctions; naive adapter voting does not help.

This paper introduces the approach of “Gradient Masters” for BLP-2025 Task 1: “Bangla Multitask Hate Speech Identification Shared Task”. We present an ensemble-based fine-tuning strategy for addressing subtasks 1A (hate-type classification) and 1B (target group classification) in YouTube comments. We propose a hybrid approach on a Bangla Language Model, which outperformed the baseline models and secured the 6th position in subtask 1A with a micro F1 score of 73.23% and the third position in subtask 1B with 73.28%. We conducted extensive experiments that evaluated the robustness of the model throughout the development and evaluation phases, including comparisons with other Language Model variants, to measure generalization in low-resource Bangla hate speech scenarios and data set coverage. In addition, we provide a detailed analysis of our findings, exploring misclassification patterns in the detection of hate speech.