Parent-order execution is a core problem in algorithmic trading, where the goal is to split a large order into smaller orders while reducing execution costs. Existing approaches either rely on pre-specified market assumptions that may not hold in practice, or require task-specific training that limits adaptability to new settings. To overcome these limitations, we present the first systematic study of large language models (LLMs) for parent-order execution. This extends the use of LLMs in finance from what to trade to how to execute. We propose PACE (Plan-Ahead Controlled Execution), a hierarchical framework that decomposes parent-order execution into long-horizon planning and short-horizon execution, requiring neither explicit market assumptions nor task-specific training. Experiments on Shenzhen Stock Exchange Level-1 data show that PACE outperforms TWAP, Almgren-Chriss, and learning-based baselines, exceeding the strongest baseline by 0.65 bps. Behavioral analysis reveals that LLMs make execution decisions differently from human investors: higher model confidence predicts better performance rather than worse returns, and the model trades earlier rather than procrastinating toward the deadline. These findings suggest that LLMs can complement human traders in execution decisions.
Current clinical management of periodontitis, a chronic inflammatory disease driven by dysbiotic biofilms, faces a persistent challenge: biofilm-associated infections remain difficult to eradicate owing to the resilient energy metabolism and high virulence of key pathogens such as Porphyromonas gingivalis. To address this challenge, we developed ultrasmall AHMP-stabilized gold nanoclusters (AHMP@AuNCs) based on a bioenergetics-centered "Metabolic Trap" paradigm. Their sub-2-nm architecture supports bacterial-interior access, while preferential bacterial accumulation may be facilitated by the pyrimidine-mimetic ligand environment, potentially through pyrimidine-associated recognition or uptake processes. A proton-responsive Au-ligand interface undergoes reversible electronic-state modulation, with near-neutral to weakly alkaline intracellular conditions favoring a charge-transfer-associated state. Following bacterial accumulation, AHMP@AuNCs disrupt proton homeostasis and energetic coupling, leading to ATP and NAD depletion, nucleotide metabolic imbalance, secondary oxidative stress, and suppression of T9SS-dependent virulence. Integrated metabolomic and transcriptomic analyses reveal coordinated rewiring of energy, nucleotide, and virulence networks, supporting the "Metabolic Trap" concept. Across oral biofilm models, AHMP@AuNCs inhibit biofilm formation and access internal regions of mature biofilms, disrupting established architecture while showing limited cytotoxicity in the evaluated host-cell models. In experimental periodontitis, local administration preserved epithelial barrier integrity, attenuated inflammation, reduced the P. gingivalis-associated burden, and limited periodontal tissue destruction, with favorable short-term tolerability. This strategy demonstrates that targeting intracellular energy vulnerabilities can achieve antibacterial, antibiofilm, and antivirulence effects against persistent infections.
