Instruction tuning is crucial for optimizing Large Language Models (LLMs), as the quality and diversity of instructional data significantly influence model performance. This naturally underscores the importance of an effective and efficient data selection strategy. However, recent mainstream data selection methods typically rely on LLMs to score instruction quality—taking advantage of their capabilities, but at the cost of high computational overhead and reduced data diversity. To address these limitations, in this paper, we propose MergeIT, a novel LLM-based Merging strategy for better Instruction Tuning that shifts the focus from selection to synthesis. MergeIT consists of two stages: first, topic-aware filtering clusters and refines the dataset, preserving diversity while eliminating redundancy without relying on LLM-based scoring, significantly reducing time and computational cost. Second, LLM-based merging synthesizes semantically similar instructions into more informative and compact training data, enhancing data richness while further reducing the size of the dataset. Experimental results demonstrate that MergeIT enables efficient, diverse, and scalable instruction selection and synthesis, establishing LLM-based merging as a promising alternative to prior scoring-based selection methods for instruction tuning.

Long-video understanding (LVU) is increasingly framed as an agentic process that iteratively localizes task-critical moments before perception and reasoning. Moment localization is therefore a key bottleneck: under long-video context constraints, sparse sampling can easily miss decisive evidence, while similarity-based retrieval often returns semantically related but task-irrelevant clips. We present EviScan, an evidence-seeking moment localization model built on an MLLM, designed for task-oriented localization and near-second temporal scanning on hour-long videos. To learn task-oriented localization, we construct ToMo-Pile, a training set that pairs temporal intervals with rationale traces explaining why the interval resolves the task. To make near-second scanning feasible on long videos, EviScan uses DualKV Prefill for memory-efficient streaming inference, and Progressive Localization to first narrow down candidate intervals and then refine boundaries using high-resolution evidence. We further introduce ToMo-Bench, a benchmark for task-oriented moment localization across diverse long-video scenarios. EviScan consistently outperforms specialized localizers and general-purpose MLLMs on both ToMo-Bench and standard temporal grounding benchmarks. When used as a plug-in localization module, it yields substantial gains on downstream long-video understanding benchmarks (MLVU and LongVideoBench), and these gains are larger than those brought by other moment-localization methods. We will release the model, dataset, benchmark, and code.

AI models capable of comprehending humor hold real-world promise—for example, enhancing engagement in human-machine interactions. To gauge and diagnose the capacity of multimodal large language models (MLLMs) for humor understanding, we introduce v-HUB, a novel video humor understanding benchmark. v-HUB comprises a curated collection of non-verbal short videos, reflecting real-world scenarios where humor can be appreciated purely through visual cues. We pair each video clip with rich annotations to support a variety of evaluation tasks and analyses, including a novel study of environmental sound that can enhance humor. To broaden its applicability, we construct an open-ended QA task, making v-HUB readily integrable into existing video understanding task suites. We evaluate a diverse set of MLLMs, from specialized Video-LLMs to versatile OmniLLMs that can natively process audio, covering both open-source and proprietary domains. The experimental results expose the difficulties MLLMs face in comprehending humor from visual cues alone. Our findings also demonstrate that incorporating audio helps with video humor understanding, highlighting the promise of integrating richer modalities for complex video understanding tasks.

Despite the rapidly growing demand for multimodal retrieval, progress in this field remains severely constrained by a lack of training data. In this paper, we introduce MegaPairs, a novel data synthesis method that leverages vision language models (VLMs) and open-domain images, together with a massive synthetic dataset generated from this method. Our empirical analysis shows that MegaPairs generates high-quality data, enabling the multimodal retriever to significantly outperform the baseline model trained on 70× more data from existing datasets. Moreover, since MegaPairs solely relies on general image corpora and open-source VLMs, it can be easily scaled up, enabling continuous improvements in retrieval performance. In this stage, we produced more than 26 million training instances and trained several models of varying sizes using this data. These new models achieve state-of-the-art zero-shot performance across 4 popular composed image retrieval (CIR) benchmarks and the highest overall performance on the 36 datasets provided by MMEB. They also demonstrate notable performance improvements with additional downstream fine-tuning. Our code, synthesized dataset, and pre-trained models are publicly available at https://github.com/VectorSpaceLab/MegaPairs.