Searching agents have achieved significant progress in NLP, yet their multimodal counterparts are constrained by a lack of suitable evaluation benchmarks requiring reasoning over continuous video, audio, and images. We propose long-form TV dramas as a natural solution and introduce DramaSearch, encompassing 3,600 riddles across 9 dramas totaling 239 hours. Our dataset is generated via a graph-constrained pipeline that processes enriched captions into structured events and multi-granularity evidence views. By employing character-to-view and entity-to-view indexing, we ensure controllable clue disclosure and strictly unique answers. The riddles are categorized into multi-clue, multi-hop, and compositional types, requiring agents to aggregate isolated clues and navigate complex dependency chains. Experiments demonstrate that a Qwen3-8B agent, trained on a held-out set using SFT and RL, achieves an accuracy of 48.3%. This result establishes a strong baseline that performs favorably against zero-shot GPT-5 (37.0%), while indicating significant room for future research. All data and code will be publicly released.

Text-based knowledge graph completion (KGC) methods utilize pre-trained language models for triple encoding and further fine-tune the model to achieve completion. Despite their excellent performance, they neglect the knowledge context in inferring process. Intuitively, knowledge contexts, which refer to the neighboring triples around the target triples, are important information for triple inferring, since they provide additional detailed information about the entities. To this end, we propose a novel framework named KnowC, which models the knowledge context as additional prompts with pre-trained language models for knowledge graph completion. Given the substantial number of neighbors typically associated with entities, along with the constrained input token capacity of language models, we further devise several strategies to sample the neighbors. We conduct extensive experiments on common datasets FB15k-237, WN18RR and Wikidata5M, experiments show that KnowC achieves state-of-the-art performance.

With the great success of pre-trained language models, the pretrain-finetune paradigm now becomes the undoubtedly dominant solution for natural language understanding (NLU) tasks. At the fine-tune stage, target task data is usually introduced in a completely random order and treated equally. However, examples in NLU tasks can vary greatly in difficulty, and similar to human learning procedure, language models can benefit from an easy-to-difficult curriculum. Based on this idea, we propose our Curriculum Learning approach. By reviewing the trainset in a crossed way, we are able to distinguish easy examples from difficult ones, and arrange a curriculum for language models. Without any manual model architecture design or use of external data, our Curriculum Learning approach obtains significant and universal performance improvements on a wide range of NLU tasks.