The first GroundLM shared tasks evaluate whether language models can identify the evidence behind an answer, rather than merely produce a correct answer in isolation. GoldenViewVQA requires a model to answer a multiple-choice question about six synchronized driving-camera views and identify the supporting view. LitTraceQA requires systems to retrieve scientific papers, locate answer-bearing evidence, and return multiple-choice or structured-table answers. We summarize the task design, submitted systems, official results, and recurrent failure modes. Among teams that submitted system reports, the best official joint accuracy on GoldenViewVQA is 0.8814. Answer accuracy is consistently higher than view accuracy, while macro view accuracy remains lower than micro accuracy, exposing both attribution errors and view imbalance. On the required LitTraceQA test set, the best score among teams that submitted system reports is 0.8002. Paper retrieval approaches saturation for several systems, but evidence F1 and structured-table accuracy remain substantially lower. Across both tasks, the central finding is consistent: producing a correct answer is easier than recovering the exact evidence representation required to justify it. We also report an optional 4,901-question LitTraceQA diagnostic split, whose unverified labels make it useful for stress testing but not for ranking systems on the main task.

Scientific question answering is useful only when readers can recover the papers and evidence behind an answer. Yet many benchmarks either provide the source document or score only the final response, obscuring failures in literature retrieval and evidence localization. We present LitTraceQA, a literature-grounded question answering benchmark in which a system must return a connected trace: canonical paper identifiers, typed evidence locations, and a checkable answer. The collection contains 4,978 unique questions grounded in 4,859 papers and covers tables, figures, text spans, equations or algorithms, and citation contexts. Of these questions, 64.85% require multiple papers; answers use multiple-choice or structured-table formats; and 61.9% are missed by every memory-only challenger used during construction. We describe a generate-and-ground-challenge pipeline and an evaluation protocol that reports paper retrieval, evidence grounding, answer correctness, and strict joint success separately. LitTraceQA therefore tests whether scientific QA systems can produce answers whose provenance is inspectable, rather than merely plausible.

Recent progress in video-text retrieval has been driven largely by advancements in model architectures and training strategies. However, the representation learning capabilities of video-text retrieval models remain constrained by low-quality and limited training data annotations. To address this issue, we present a novel Video-Text Retrieval Paradigm with Relevance-based Augmentation, namely dReAm, which enhances video and text data using large foundation models to learn more generalized features. Specifically, we first adopt a simple augmentation method, which generates self-similar data by randomly duplicating or dropping subwords and frames. In addition, inspired by the recent advancement in visual and language generative models, we propose a more robust augmentation method through textual paraphrasing and video stylization using large language models (LLMs) and visual generative models (VGMs). To further enrich video and text information, we propose a relevance-based augmentation method, where LLMs and VGMs generate and integrate new relevant information into the original data. Leveraging this enriched data, extensive experiments on several video-text retrieval benchmarks demonstrate the superiority of dReAm over existing methods. Code will be available upon acceptance.