Scaling LLM-based agents to long-horizon deep research is constrained by the context-noise trade-off, where linear history accumulation degrades reasoning and dilutes fine-grained evidence. To address this, we introduce the Cognitive Scaffold, a factorized memory architecture that decouples the cognitive state into a Fluid Working Context for immediate reasoning and a persistent Knowledge Graph for long-term retention. Unlike unstructured summarization, our framework employs a Rejection Sampling Fine-Tuning (RFT) pipeline to crystallize saturated context into structured event snapshots, strictly enforcing atomic constraints to preserve numerical values and entities. During reasoning, a thought-driven dual-path retrieval mechanism enables the agent to proactively recover precise evidence. Empirical evaluations on Xbench-DeepSearch, BrowseComp-ZH, and GAIA demonstrate that Cognitive Scaffold consistently outperforms baselines, achieving 74.7% Avg@3 and 87.0% Pass@3 on Xbench-DeepSearch, 48.5% Avg@3 and 65.9% Pass@3 on BrowseComp-ZH, and 72.8% Avg@3 and 88.3% Pass@3 on GAIA, while reducing compression hallucinations to 5.3%. We open-source our codebase to facilitate future research.

While modern segmentation models often prioritize performance over practicality, we advocate for a design philosophy that prioritizes simplicity and efficiency, and strive to design high-performance segmentation models. This paper presents SimpleUNet, a scalable, lightweight medical image segmentation framework. The key is that we proposed a simple yet effective partial feature selection mechanism for reducing information redundancy and thus facilitating compact model design. Additionally, we found that adjusting the model width is a straightforward yet easily overlooked tactic for lightweight model design, thereby preventing exponential parameter growth across network stages. By integrating an almost parameter-free channel attention module, the performance of the developed models can be improved with minimal overhead. Leveraging these techniques, our record-breaking model SimpleUNet with only 16 KB parameters surpasses LBUNet and other lightweight benchmarks across multiple public datasets. Impressively, the 0.67 MB variant achieves superior efficiency and accuracy, attaining a mean DSC/IoU of 85.76%/75.60% on a curated multi-center breast lesion dataset, surpassing both U-Net and TransUNet. Evaluations on skin lesion datasets (ISIC 2017/2018: mDice 84.86%/88.77%) and endoscopic polyp segmentation (KVASIR-SEG: 86.46%/76.48% mDice/mIoU) confirm consistent dominance over state-of-the-art models. Although our current SimpleUNet architecture does not rely on exotic or custom operators, it is fundamentally designed to embrace future innovations. The framework remains fully compatible with emerging operator-level advancements, allowing effortless integration and seamless upgrades without structural modifications. Codes can be found at https://github.com/Frankyu5666666/SimpleUNet .

The dynamic conditions and internal states of portable energy storage system (PESS), such as temperature, electricity price, state of charge (SOC), and state of health (SOH), significantly impact battery degradation. Current decision-making models for PESS operation often oversimplify the modeling of battery degradation. To address this, we introduce an environment-adaptive online learning framework that effectively integrates deep neural networks and reinforcement learning to exploit and explore external environments (i.e., electricity prices and temperature) and internal dynamics (i.e., battery degradation), providing decision support for PESS operation. This framework dynamically updates battery degradation and decision-making models in real-time, enhancing adaptive responses to external changes. Specifically, we developed a neural network based on porous electrode theory that considers multi-physical factors, such as charging power, initial and terminal SOC, SOH, and temperature to accurately assess battery degradation. This network is embedded within a deep reinforcement learning algorithm, enabling real-time, adaptive decision-making for PESS amidst varying environmental conditions. Furthermore, to navigate complex operational environments, a fine-tuning mechanism is incorporated into the degradation neural network. Application of this framework to the energy arbitrage of PESS in the California power grid demonstrates an average benefit increase of 37% compared to traditional degradation assessment models. Note to Practitioners—In this work, we develop a novel approach to addressing the critical issue of battery degradation in PESS. Existing models often oversimplify degradation, hindering accurate assessments of performance and lifespan projections. More recently, learning-based algorithms have demonstrated outstanding performance in both battery degradation modeling and real-time decision-making. In this sense, we introduce a sophisticated neural network model grounded in a porous electrode model. This model considers multi-physics factors involving charging/discharging power, initial and terminal SOC, SOH, and temperature. Complementing this, we integrate the aforementioned model into an online learning framework, enabling real-time decision-making for PESS. Furthermore, to tackle the challenges of complex operating environments, a fine-tuning mechanism is incorporated into the battery degradation neural network. We validate the effectiveness of the proposed methods through an energy arbitrage application of PESS and also reveal its potential for on-demand applications in energy and transportation systems. This note anticipates a positive impact on PESS management and contributes significantly to the evolution of energy storage systems, offering practitioners invaluable decision support for commercial applications involving battery sharing, trading, and renting.

The anatomical structure detection of fetal cardiac views is crucial for diagnosing fetal congenital heart disease. In practice, there is a large domain gap between different hospitals' data, such as the variable data quality due to differences in acquisition equipment. In addition, accurate annotation information provided by obstetrician experts is always very costly or even unavailable. This study explores the unsupervised domain adaptive fetal cardiac structure detection issue. Existing unsupervised domain adaptive object detection (UDAOD) approaches mainly focus on detecting objects in natural scenes, such as Foggy Cityscapes, where the structural relationships of natural scenes are uncertain. Unlike all previous UDAOD scenarios, we first collected a Fetal Cardiac Structure dataset from two hospital centers, called FCS, and proposed a multi-matching UDA approach (M3-UDA), including Histogram Matching (HM), Sub-structure Matching (SM), and Global-structure Matching (GM), to better transfer the topological knowledge of anatomical structure for UDA detection in medical scenarios. HM mitigates the domain gap between the source and target caused by pixel transformation. SM fuses the different angle information of the sub-structure to obtain the local topological knowledge for bridging the domain gap of the internal sub-structure. GM is designed to align the global topological knowledge of the whole organ from the source and target domain. Extensive experiments on our collected FCS and CardiacUDA, and experimental results show that M3-UDA outperforms existing UDAOD studies significantly. Datasets and source code are available at https://github.com/xmed-lab/M3-UDA.

Models trained on ultrasound images from one institution typically experience a decline in effectiveness when transferred directly to other institutions. Moreover, unlike natural images, dense and overlapped structures exist in fetus ultrasound images, making the detection of structures more challenging. Thus, to tackle this problem, we propose a new Unsupervised Domain Adaptation (UDA) method named ToMo-UDA for fetus structure detection, which consists of the Topology Knowledge Transfer (TKT) and the Morphology Knowledge Transfer (MKT) module. The TKT leverages prior knowledge of the medical anatomy of fetal as topological information, reconstructing and aligning anatomy features across source and target domains. Then, the MKT formulates a more consistent and independent morphological representation for each substructure of an organ. To evaluate the proposed ToMo-UDA for ultrasound fetal anatomical structure detection, we introduce FUSH$^2$, a new Fetal UltraSound benchmark, comprises Heart and Head images collected from Two health centers, with 16 annotated regions. Our experiments show that utilizing topological and morphological anatomy information in ToMo-UDA can greatly improve organ structure detection. This expands the potential for structure detection tasks in medical image analysis.

Battery storage is a key technology for distributed renewable energy integration. Wider applications of battery storage systems call for smarter and more flexible deployment models to improve their economic viability. Here we propose a hybrid energy storage system (HESS) model that flexibly coordinates both portable energy storage systems (PESSs) and stationary energy storage systems (SESSs) in a grid. PESSs are batteries and power conversion systems loaded on vehicles that travel between grid nodes with price differences to alleviate grid congestion. PESSs can charge/discharge at grid nodes or swap (part of) batteries with SESSs for profit maximization. We introduce a spatiotemporal decision-making framework for HESS including the planning of SESS and the on-demand dispatch of PESS. We propose a two-phase decision-making algorithm (TPDM), where the first phase uses a spatiotemporal cost-effectiveness aggregation method to determine the optimal SESS location; the second phase shapes a low-complexity solution space by arc destroying and repairing. The results show that HESS achieves significant arbitrage benefit improvement in 86.3% of the operating periods through a year compared with SESS and PESS alone. Compared with commercial solver, the proposed TPDM, on average, can reduce the computational time by 95.5% with an optimality of 1.04%.Note to Practitioners—Battery storage and electric vehicles (EVs) play a crucial role in renewable energy integration and in shaping a low-carbon and sustainable energy and transportation systems. To achieve efficient and scalable management of battery storage across energy and transportation systems, we incorporate the portable energy storage (i.e., batteries transported by vehicles) and stationary energy storage (i.e., batteries placed at grids), into a hybrid energy storage system (HESS), and develop efficient planning framework and scheduling algorithms. Specifically, the proposed methods can provide decision supports for the owners of battery assets to determine the optimal SESS location and for the high-quality coordination of battery charging, swapping, and routing in a HESS. Our methods also have potentials in the on-demand applications of battery storage and EVs across energy and transportation systems, such as ancillary services, grid investment deferral, and battery trading and sharing.

Diabetes mellitus and obesity are becoming some of the most serious public health challenges in the world. To help researchers more quickly reveal the complex relationships existing between diabetes mellitus, obesity, and related diseases in the literature, and give them an inspiration to search the effective treatments for these diseases, we propose a novel model named as representative latent Dirichlet allocation topic model (RLDA). We conducted the representation learning model on more than 337,000 pieces of diabetes and obesity related literature published in the recent decade. Then, an explicit analysis of the final result using a series of visualization tools to discover meaningful relations among diabetes mellitus, obesity, and other diseases was performed. In order to show the credibility of our discoveries, we used clinical reports, such as Standards of Medical Care in Diabetes, which were not used in our training data, to verify our results. Fortunately, a sufficient number of the reports were direct matches. With the help of our model, we achieved satisfactory results for diabetes mellitus and obesity. For example, we discovered that 22 other diseases are closely related to diabetes mellitus, 10 with obesity and 8 with both. In addition, the tumor, adolescent/child, inflammation, and hypertension will be the hottest research topics relating to diabetes and obesity in the near future. We believe that the representational learning model we have built can help biomedical researchers direct the focus and adjust the direction of their work.