Autonomous on-orbit servicing demands embodied agents that perceive through visual sensors, reason about 3D spatial situations, and execute multi-phase tasks over extended horizons. We present SpaceMind, a modular and self-evolving vision-language model (VLM) agent framework that decomposes knowledge, tools, and reasoning into three independently extensible dimensions: skill modules with dynamic routing, Model Context Protocol (MCP) tools with configurable profiles, and injectable reasoning-mode skills. An MCP-Redis interface layer enables the same codebase to operate across simulation and physical hardware without modification, and a Skill Self-Evolution mechanism distills operational experience into persistent skill files without model finetuning. Beyond the framework, we contribute an evaluation protocol that complements binary success with continuous behavioral metrics, including a delta-v proxy, path efficiency, and a failure-mode taxonomy, and release the platform with configurable stress injections as a benchmark for space agents. We validate SpaceMind through 363 closed-loop runs across five satellites, four task types, and two environments, a UE5 simulation and a physical laboratory, deliberately including degraded conditions to stress-test robustness. Under nominal conditions all modes achieve 90%-100% navigation success; under degradation, the Prospective mode uniquely succeeds in search-and-approach tasks where other modes fail. A self-evolution study shows that the agent recovers from failure in four of six groups from a single failed episode, including complete failure to 100% success and inspection scores improving from 12 to 59 out of 100. Real-world validation confirms zero-code-modification transfer to a physical robot with 100% rendezvous success. A challenging-scenario campaign of 171 runs then locates the capability boundary: the agent tolerates target rotation up to 2 degrees/s almost without loss, yet success drops to 47% under a delta-v budget and to 33% on a fly-around repositioning task, with failures dominated by long-range target acquisition and geometric self-assessment rather than maneuver execution. Code: https://github.com/wuaodi/SpaceMind.
We establish a quantitative relation between local spin polarization and quantum entanglement in two-qubit systems by mathematically proving a closed-form upper bound on the concurrence at fixed local polarization magnitudes. The bound shows how the maximal concurrence is jointly constrained by the two local polarizations. We further demonstrate that this bound is saturated by pure states in certain cases with identical polarizations. As a concrete physical application, we consider the parity-violating process e+e−→Z0→qq¯, which generates final-state spin polarization. We show that the maximal concurrence is attained in specific kinematic regions and is significantly reduced relative to the unpolarized case. These results establish a general, process-independent framework connecting local polarization, maximal entanglement, and pure states.
As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions.
Constant-rate depressurization, with control of the depressurization rate, has been proven to efficiently enhance hydrate dissociation in simulated confined natural gas hydrate (NGH) reservoirs. However, for unconfined reservoir simulation systems, the effects of depressurization rate on hydrate dissociation and gas production remain unknown. In this study, a stratified NGH reservoir environment was built to investigate CH4 hydrate dissociation in unconfined systems with varied depressurization rates. The results showed that the depressurization rate not only influenced heat and fluid transport within the original pore fluids, but also altered the flow pathways of the overlying water intruding into the reservoir. These changes affected the utilization efficiency of the sensible heat and the reformation characteristics of CH4 hydrates in the hydrate reservoir, resulting in significant spatial heterogeneity in the temperature and flow fields. Depressurization rate significantly influenced both CH4 recovery from NGH reservoirs and CH4 leakage into the overlying environment. By optimizing the depressurization rate to a moderate 0.10 MPa/min to balance factors such as overburden sediment collapse, overlying water intrusion, and hydrate dissociation, the CH4 recovery ratio increased by up to 17.17%, reaching 77.00%, while the CH4 leakage ratio decreased by as much as 11.12%, to 6.50%.
Prefix-based methods have emerged as a promising paradigm for Controllable Text Generation (CTG) due to their parameter efficiency. However, while effective in short sequences, their controllability tends to diminish as the generated sequence grows. In this paper, we identify Attention Dilution as a key factor behind this phenomenon: as the sequence length increases, the attention allocated to the control signal naturally decays due to the softmax mechanism, leading to a "fading" control effect. To address this, we propose LaPA^2 (Length-aware Prefix and Prompt Attention Augmentation), a training-free and model-agnostic framework designed to sustain robust control in long contexts. Specifically, LaPA^2 employs Length-Aware Logarithmic Scaling to dynamically amplify prefix attention weights, mathematically counteracting the dilution effect, while an optional Contextual Anchor Reinforcement applies synchronized augmentation to prompt tokens, preserving semantic coherence when strong attribute control risks overshadowing the original prompt. LaPA^2 is versatile, supporting both soft prefixes (continuous embeddings) and hard prefixes (discrete instructions). Experiments on multiple CTG tasks demonstrate that LaPA^2 consistently improves the performance of various prefix-based methods in long-form settings, leading to superior attribute controllability while preserving content relevance and fluency. Our code and data are publicly available at https://github.com/jiabingyang01/LaPA2.
