Aerial Visual Object Search (AVOS) tasks in urban environments require Unmanned Aerial Vehicles (UAVs) to autonomously search for and identify target objects based on visual inputs without external guidance. Existing approaches struggle in complex urban environments due to redundant semantic processing, similar object ambiguity, and the exploration-exploitation dilemma. To advance research and support the AVOS task, we introduce CityAVOS, the first benchmark dataset for autonomous search of static urban objects. It features 2,420 tasks of varying difficulty across six object categories, designed to rigorously evaluate UAV search strategies. To solve the AVOS task, we also propose PRPSearcher (Perception-Reasoning-Planning Searcher), a novel agentic method powered by multi-modal large language models (MLLMs) that enables a UAV agent to think and reason like humans on visual cues when searching for objects. Specifically, PRPSearcher constructs three specialized maps: an object-centric dynamic semantic map enhancing spatial perception, a 3D cognitive map based on semantic "attraction" values for target reasoning, and a 3D uncertainty map for balanced exploration-exploitation search. Moreover, we propose a denoising mechanism to mitigate interference from similar objects and design an Inspiration Promote Thought prompting mechanism for adaptive action planning. Experimental results on CityAVOS demonstrate that PRPSearcher surpasses existing baselines in both success rate and search efficiency (on average: +37.69% SR, +28.96% SPL, -30.69% MSS, and -46.40% NE). Our work paves the way for future advances in embodied visual target search.

Large Language Model (LLM)-based optimization has recently shown promise for autonomous problem solving, yet most approaches still cast LLMs as passive constraint checkers rather than proactive strategy designers, limiting their effectiveness on complex Constraint Optimization Problems (COPs).To address this, we present AutoCO, an end-to-end Automated Constraint Optimization method that tightly couples operations-research principles of constraint relaxation with LLM reasoning.A core innovation is a unified triplerepresentation that binds relaxation strategies, algorithmic principles, and executable codes.This design enables the LLM to synthesize, justify, and instantiate relaxation strategies that are both principled and executable.To navigate fragmented solution spaces, AutoCO employs a bidirectional global-local coevolution mechanism, synergistically coupling Monte Carlo Tree Search (MCTS) for global relaxationtrajectory exploration with Evolutionary Algorithms (EAs) for local solution intensification.This continuous exchange of priors and feedback explicitly balances diversification and intensification, thus preventing premature convergence.Extensive experiments on three challenging COP benchmarks validate AutoCO's consistent effectiveness and superior performance, especially in hard regimes where current methods degrade.Results highlight Au-toCO as a principled and effective path toward proactive, verifiable LLM-driven optimization.

Drug combination synergy is crucial in pharmacology, as it can enhance disease treatment efficacy or reduce drug resistance when administered in combination. Accurate prediction of drug combination synergy is vital for optimizing therapeutic regimens and improving treatment effectiveness. However, existing computational methods primarily rely on drug sequence and structural features, making them difficult to capture complex network relationships and global information-especially lacking the ability to perform cross-modal fusion. In this study, we proposed a method called SDCGDCP for predicting drug combination synergy. It processed drug molecular structures (graph structures and molecular fingerprints), target biological activity information and integrated cell line whole-genome expression profiles to construct multi-level combined node representations. A drug-cell line pair graph was accordingly generated. SDCGDCP updated node and edge representations via a GatedGCN module and derived five types of structural encodings (centrality, spatial, edge, Laplacian positional and node-similarity encodings), after which the graph language model Graphormer is employed to capture long-range node interactions. Finally, drug combination synergy was predicted using an MLP and SoftMax classifier. Extensive evaluations showed that SDCGDCP outperforms other state-of-the-art methods on the DrugCombDB dataset, achieving an AUROC of 0.923 and AUPRC of 0.885. Ablation experiments validated the effectiveness of each feature and encoding module. Meanwhile, we conducted case analyses on the predicted drug combination synergies. The results were supported by evidence from several pharmaceutical studies. This highlights potential of SDCGDCP in enhancing drug synergy prediction and optimizing combination therapies. The source code and data for SDCGDCP are available at https://github.com/Philosopher-Zhao/SDCGDCP.