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.

Redundancy of visual tokens in multi-modal large language models (MLLMs) significantly reduces their computational efficiency. Recent approaches, such as resamplers and summarizers, have sought to reduce the number of visual tokens, but at the cost of visual reasoning ability. To address this, we propose LEO-Mini, a novel MLLM that significantly reduces the number of visual tokens and simultaneously boosts visual reasoning capabilities. For efficiency, LEO-Mini incorporates CoTR, a novel token reduction module to consolidate a large number of visual tokens into a smaller set of tokens, using the similarity between visual tokens, text tokens, and a compact learnable query. For effectiveness, to scale up the model’s ability with minimal computational overhead, LEO-Mini employs MMoE, a novel mixture of multi-modal experts module. MMoE employs a set of LoRA experts with a novel router to switch between them based on the input text and visual tokens instead of only using the input hidden state. MMoE also includes a general LoRA expert that is always activated to learn general knowledge for LLM reasoning. For extracting richer visual features, MMoE employs a set of vision experts trained on diverse domain-specific data. To demonstrate LEO-Mini’s improved efficiency and performance, we evaluate it against existing efficient MLLMs on various benchmark vision-language tasks.