Understanding the Weakness of Large Language Model Agents within a Complex Android Environment

Large language models (LLMs) have empowered intelligent agents to execute intricate tasks within domain-specific software such as browsers and games. However, when applied to general-purpose software systems like operating systems, LLM agents face three primary challenges. Firstly, the action space is vast and dynamic, posing difficulties for LLM agents to maintain an up-to-date understanding and deliver accurate responses. Secondly, real-world tasks often require inter-application cooperation}, demanding farsighted planning from LLM agents. Thirdly, agents need to identify optimal solutions aligning with user constraints, such as security concerns and preferences. These challenges motivate AndroidArena, an environment and benchmark designed to evaluate LLM agents on a modern operating system. To address high-cost of manpower, we design a scalable and semi-automated method to construct the benchmark. In the task evaluation, AndroidArena incorporates accurate and adaptive metrics to address the issue of non-unique solutions. Our findings reveal that even state-of-the-art LLM agents struggle in cross-APP scenarios and adhering to specific constraints. Additionally, we identify a lack of four key capabilities, i.e., understanding, reasoning, exploration, and reflection, as primary reasons for the failure of LLM agents. Furthermore, we provide empirical analysis on the failure of reflection, and improve the success rate by 27% with our proposed exploration strategy. This work is the first to present valuable insights in understanding fine-grained weakness of LLM agents, and offers a path forward for future research in this area. Environment, benchmark, and evaluation code for AndroidArena are released at https://github.com/AndroidArenaAgent/AndroidArena

Usnic acid ameliorates bleomycin-induced pulmonary fibrosis in mice via inhibition of inflammatory responses and oxidative stress

Purpose: To Investigate the effect of usnic acid (UA) on bleomycin (BLM)-induced pulmonary fibrosis in mice, and the underlying mechanism. Methods: Male Kunming mice with bleomycin-induced pulmonary fibrosis (PF) were exposed to different concentrations of usnic acid. Lung coefficient and histopathological changes were determined, while MDA, superoxide dismutase (SOD) activity, and expression levels of hydroxyproline, tumor necrosis factor-α, interleukins-1β & 6, and transforming growth factor-β1 were assayed in lung homogenates. Results: UA significantly mitigated lung coefficient and histopathological changes in mice. Compared to the bleomycin group, MDA level was significantly reduced while the content of SOD markedly increased after UA pretreatment (p < 0.05). Moreover, UA significantly reduced the expression levels of all the parameters, relative to bleomycin group (p < 0.05). Conclusion: These results indicate that UA protects mice against bleomycin-induced PF via a mechanism associated with attenuation of pro-oxidant stress and inflammation. Therefore, UA has therapeutic potential for the management of pulmonary fibrosis

Identifying risk factors for cesarean scar pregnancy: a retrospective study of 79 cases

 Objectives: To explore the possible risk factors for cesarean scar pregnancy (CSP), the incidence of which is increasing rapidly in China. Material and methods: 79 patients with CSP and 69 non-CSP expectant mothers with at least 1 previous cesarean section were employed in the study. The obstetric histories of the participants were collected and analyzed using Chi square test. Results: We found that 77.2% CSP patients had ≥ 3 pregnancies and only 36.2% women had ≥ 3 pregnacies in non-CSP group. During the previous cesarean delivery, 21.5% of CSP patients had entered the first stage of labor, which was 43.5% in non-CSP group (P < 0.05). Cephalopelvic disproportion occurred in 51.9% of CSP patients, which was significantly higher than that (23.2%) in non-CSP group (P < 0.01). 11.4% of CSP patients had undergone cesarean section due to breech and shoulder presentation in the past, which was only 1.4% in non-CSP group. However, no significance was noted (P > 0.05). We did not find significant differences between the CSP and non-CSP patients in maternal age, multiple cesarean sections, gestational age, emergency or elective caesarean section. Conclusions: Multiple pregnancies, absence of the first stage of labor, and cephalopelvic disproportion might be the risk factors for the occurrence of CSP.  

VIDiff: Translating Videos via Multi-Modal Instructions with Diffusion Models

Diffusion models have achieved significant success in image and video generation. This motivates a growing interest in video editing tasks, where videos are edited according to provided text descriptions. However, most existing approaches only focus on video editing for short clips and rely on time-consuming tuning or inference. We are the first to propose Video Instruction Diffusion (VIDiff), a unified foundation model designed for a wide range of video tasks. These tasks encompass both understanding tasks (such as language-guided video object segmentation) and generative tasks (video editing and enhancement). Our model can edit and translate the desired results within seconds based on user instructions. Moreover, we design an iterative auto-regressive method to ensure consistency in editing and enhancing long videos. We provide convincing generative results for diverse input videos and written instructions, both qualitatively and quantitatively. More examples can be found at our website https://ChenHsing.github.io/VIDiff

Tetra­aqua­(1,10-phenanthroline)nickel(II) 3,6-dicarboxy­bicyclo­[2.2.2]oct-7-ene-2,5-dicarboxyl­ate

In the title compound, [Ni(C12H8N2)(H2O)4](C12H10O8), the NiII ion is six-coordinated by two N atoms from one phenanthroline ligand and by the O atoms of four water mol­ecules in a distorted octa­hedral geometry. In the crystal, inter­molecular O—H⋯O hydrogen bonds form an extensive three-dimensional network, which consolidates the crystal packing