ShotFinder: Imagination-Driven Open-Domain Video Shot Retrieval via Web Search

In recent years, large language models (LLMs) have made rapid progress in information retrieval, yet existing research has mainly focused on text or static multimodal settings. Open-domain video shot retrieval, which involves richer temporal structure and more complex semantics, still lacks systematic benchmarks and analysis. To fill this gap, we introduce ShotFinder, a benchmark that formalizes editing requirements as keyframe-oriented shot descriptions and introduces five types of controllable single-factor constraints: Temporal order, Color, Visual style, Audio, and Resolution. We curate 1,210 high-quality samples from YouTube across 20 thematic categories, using large models for generation with human verification. Based on the benchmark, we propose ShotFinder, a text-driven three-stage retrieval and localization pipeline: (1) query expansion via video imagination, (2) candidate video retrieval with a search engine, and (3) description-guided temporal localization. Experiments on multiple closed-source and open-source models reveal a significant gap to human performance, with clear imbalance across constraints: temporal localization is relatively tractable, while color and visual style remain major challenges. These results reveal that open-domain video shot retrieval is still a critical capability that multimodal large models have yet to overcome.

RPO:Reinforcement Fine-Tuning with Partial Reasoning Optimization

Within the domain of large language models, reinforcement fine-tuning algorithms necessitate the generation of a complete reasoning trajectory beginning from the input query, which incurs significant computational overhead during the rollout phase of training. To address this issue, we analyze the impact of different segments of the reasoning path on the correctness of the final result and, based on these insights, propose Reinforcement Fine-Tuning with Partial Reasoning Optimization (RPO), a plug-and-play reinforcement fine-tuning algorithm. Unlike traditional reinforcement fine-tuning algorithms that generate full reasoning paths, RPO trains the model by generating suffixes of the reasoning path using experience cache. During the rollout phase of training, RPO reduces token generation in this phase by approximately 95%, greatly lowering the theoretical time overhead. Compared with full-path reinforcement fine-tuning algorithms, RPO reduces the training time of the 1.5B model by 90% and the 7B model by 72%. At the same time, it can be integrated with typical algorithms such as GRPO and DAPO, enabling them to achieve training acceleration while maintaining performance comparable to the original algorithms. Our code is open-sourced at https://github.com/yhz5613813/RPO.

FMCHS: Advancing Traditional Chinese Medicine Herb Recommendation with Fusion of Multiscale Correlations of Herbs and Symptoms

Traditional Chinese medicine (TCM) exhibits remarkable therapeutic efficacy in disease treatment and healthcare through personalized herb prescriptions. However, current herb recommendation models inadequately capture the multiscale relations between herbs and clinical symptoms, particularly neglecting latent correlations at the chemical-molecular scale. To address these limitations, we propose the Fusion of Multiscale Correlations of Herbs and Symptoms (FMCHS), an innovative framework that synergistically integrates molecular-scale chemical characteristics of herbs with clinical symptoms. The framework employs multi-relational graph transformer layers to generate enriched embeddings that preserve both structural and semantic features within herbs and symptoms. Through systematic incorporation of herb chemical profiles into node embeddings and implementation of attention-based feature fusion, FMCHS effectively utilizes multiscale correlations. Comprehensive evaluations demonstrate FMCHS's superior performance over the state-of-the-art (SOTA) baseline, achieving relative improvements of 8.85% in Precision@5, 12.30% in Recall@5, and 10.86% in F1@5 compared to the SOTA model on benchmark datasets. This work facilitates the practical application of TCM in disease treatment and healthcare.