Large language models (LLMs), have shown strong potential in scientific discovery, yet existing methods still face substantial challenges in the design of research workflows and multi-role collaboration mechanisms.To mitigate these issues, we propose EvoSci, a multi-agent scientific collaboration framework, which integrates bio-inspired evolution with knowledge graph modeling.To iteratively generate, evaluate, and refine research ideas, EvoSci incorporates multiple role-based agents, including mentor, researcher, and reviewer.By combining collaborative reasoning, shared memory, and evolutionary feedback, EvoSci significantly enhances the coherence and creativity of scientific exploration.Experiments on real-world research topics demonstrate that EvoSci significantly outperforms strong baselines in LLM-based structured peer-review and comparative ranking evaluations, achieving the highest overall peer-review score (ICLR 4.90) and top ranking (Top-10 = 54).These results suggest its superiority in both scientific idea generation and continuous discovery.

We present TopoGaussian, a holistic, particle-based pipeline for inferring the interior structure of an opaque object from easily accessible photos and videos as input. Traditional mesh-based approaches require tedious and error-prone mesh filling and fixing process, while typically output rough boundary surface. Our pipeline combines Gaussian Splatting with a novel, versatile particle-based differentiable simulator that simultaneously accommodates constitutive model, actuator, and collision, without interference with mesh. Based on the gradients from this simulator, we provide flexible choice of topology representation for optimization, including particle, neural implicit surface, and quadratic surface. The resultant pipeline takes easily accessible photos and videos as input and outputs the topology that matches the physical characteristics of the input. We demonstrate the efficacy of our pipeline on a synthetic dataset and four real-world tasks with 3D-printed prototypes. Compared with existing mesh-based method, our pipeline is 5.26x faster on average with improved shape quality. These results highlight the potential of our pipeline in 3D vision, soft robotics, and manufacturing applications.

Swimming in nature achieves remarkable performance through diverse morphological adaptations and intricate solid-fluid interaction, yet exploring this capability in artificial soft swimmers remains challenging due to the high-dimensional control complexity and the computational cost of resolving hydrodynamic details. Traditional approaches often rely on morphology-dependent heuristics and simplified fluid models, which constrain exploration and preclude advanced strategies like vortex exploitation. To address this, we propose an automated framework that combines a unified, reduced-mode control space with a high-fidelity GPU-accelerated simulator. Our control space naturally captures deformation patterns for diverse morphologies, minimizing manual design, while our simulator efficiently resolves the crucial fluid-structure interactions required for learning. We evaluate our method on a wide range of morphologies, from bio-inspired to unconventional. From this general framework, high-performance swimming patterns emerge that qualitatively reproduce canonical gaits observed in nature without requiring domain-specific priors, where state-of-the-art baselines often fail, particularly on complex topologies like a torus. Our work lays a foundation for future opportunities in automated co-design of soft robots in complex hydrodynamic environments. The code is available at https://github.com/changyu-hu/FreeFlow.

In this paper, a novel adjustable-stiffness force sensor is developed for multitask measurements requiring different force resolutions and ranges. The applied force of the force sensor is indirectly measured through the linear deformation instead of the structure strain through an optical linear encoder. The main structure of the force sensor is actually a linear variable stiffness mechanism with a compact size and a large stiffness change. Its stiffness can be continuously adjusted by changing the effective second moment of area of the structure. Thus, the force sensor has an adjustable range and resolution since the displacement resolution of the optical linear encoder is constant. The stiffness modeling of the sensor is performed based on the matrix method, which is then evaluated by the finite element analysis. A principle prototype is finally fabricated for the adjustable-stiffness test and a concrete application example. The testing results show that the stiffness and resolution of the force sensor can be changed by the proposed stiffness adjustment. Moreover, it is effective to measure different-resolution forces. This adjustable-stiffness approach can be also extended to the design of a torque sensor or a force/torque sensor.

There have been many large-scale investigations of users' mobile app launch behaviour, but all have been conducted on Android, even though recent reports suggest iPhones account for a third of all smartphones in use. We report on the first large-scale analysis of app usage patterns on iPhones. We conduct a reproduction study with a cohort of over 10,000 jailbroken iPhone users, reproducing several studies previously conducted on Android devices. We find some differences, but also significant similarities: e.g. communications apps are the most used on both platforms; similar patterns are apparent of few apps being very popular but there existing a 'long tail' of many apps used by the population; users show similar patterns of 'micro-usage'; almost identical proportions of people use a unique combination of apps. Such similarities add confidence but also specificity about claims of consistency across smartphones. As well as presenting our findings, we discuss issues involved in reproducing studies across platforms.

Several previous approaches attempted to predict bursty topics on Twitter. Such approaches have usually reported that the time information (e.g. the topic popularity over time) of hashtag topics contribute the most to the prediction of bursty topics. In this paper, we propose a novel approach to use time features to predict bursty topics on Twitter. We model the popularity of topics as density curves described by the density function of a beta distribution with different parameters. We then propose various approaches to predict/classify the bursty topics by estimating the parameters of topics, using estimators such as Gradient Decent or Likelihood Maximization. In our experiments, we show that the estimated parameters of topics have a positive effect on classifying bursty topics. In particular, our estimators when combined together improve the bursty topic classification by 6.9 in terms of micro F1 compared to a baseline classifier using hashtag content features.

Flickr allows its users to generate galleries of "faves", i.e., pictures that they have tagged as favourite. According to recent studies, the faves are predictive of the personality traits that people attribute to Flickr users. This article investigates the phenomenon and shows that faves allow one to predict whether a Flickr user is perceived to be above median or not with respect to each of the Big-Five Traits (accuracy up to 79\% depending on the trait). The classifier - based on Gaussian Processes with a new kernel designed for this work - allows one to identify the visual characteristics of faves that better account for the prediction outcome.