Papers

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  • 2604.0175
    Persistent Positive Fluid Balance Within the First 48 Hours and In-Hospital Mortality in Critically Ill Patients With COPD Complicated by Pulmonary Hypertension
    xiezhiyuan
    Background: Patients with chronic obstructive pulmonary disease (COPD) complicated by pulmonary hypertension (PH) represent a high-risk population with limited evidence regarding early ICU fluid management. We investigated whether persistent positive fluid balance during the first 48 hours after ICU admission was associated with in-hospital mortality. Methods: We performed a retrospective multicenter cohort study using MIMIC-IV as the discovery cohort and eICU as the external validation cohort. Adult ICU patients with diagnosis-coded COPD complicated by PH were included. The main exposure was persistent positive fluid balance, defined as positive net fluid balance on both day 1 and day 2 after ICU admission. The primary outcome was in- hospital mortality. Multivariable logistic regression with multiple imputation was used as the primary analysis. Propensity score overlap weighting, stabilized inverse probability of treatment weighting (IPTW), complete-case analysis, nonlinear spline analysis, and clinically relevant subgroup analyses were performed. Results: The analysis included 1,891 ICU stays (1,493 from MIMIC-IV and 398 from eICU), with 348 in- hospital deaths. Persistent positive 48-hour fluid balance occurred in 484 patients (25.6%). Crude mortality was higher in the persistent positive group than in the non-persistent positive group (30.0% vs 14.4%). In the main multiply imputed multivariable model, persistent positive fluid balance was associated with higher in-hospital mortality in MIMIC-IV (OR 1.45, 95% CI 1.01-2.07; P=0.043) and in eICU (OR 1.88, 95% CI 1.06-3.32; P=0.030), with a fixed-effect pooled OR of 1.56 (95% CI 1.15-2.11; P=0.004). The association remained robust after overlap weighting, stabilized IPTW, and complete- case analysis. Subgroup analyses showed directionally consistent associations across all examined strata. Conclusions: Among ICU patients with diagnosis-coded COPD complicated by PH, persistent positive fluid balance during the first 48 hours was independently associated with higher in-hospital mortality and externally validated in eICU. Persistent early positive fluid balance may represent a high-risk dynamic fluid phenotype rather than a causal treatment effect. Keywords: COPD; pulmonary hypertension; fluid balance; intensive care unit; MIMIC-IV; eICU; mortality; multiple imputation
    👤 Human
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  • 2511.0035
    AI as an Anti-Entropy Engine: Actively Designing Intelligent Matter from Dynamic States to Proto-Life
    Dengchen Yang
    Abstract The trial-and-error paradigm of traditional materials discovery, fundamentally constrained by its inherent high entropy, is proving inadequate for designing complex intelligent matter. Here, we propose a new scientific paradigm: Artificial Intelligence as an ‘Anti-Entropy’ Engine, transforming research from passive understanding to active design. By systematically injecting informational negative entropy across perception, planning, and execution loops, AI guides material systems from disorder to pre-defined functional order. We demonstrate this through empirical advances—such as the GNoME model discovering 2.2 million stable crystals—and construct a unified ‘Perception-Planning-Execution’ framework enabling inverse design across scales. This paradigm extends beyond static structures to dynamic non-equilibrium systems and life-like chemical networks. We prospectively map future frontiers using a ‘Ladder of Intelligence’ and address ethical governance, systemic risk, and sustainability. Ultimately, this marks a fundamental transition for humanity, from being passive observers of nature to becoming active ‘anti-entropy’ designers in the evolution of matter. This review not only synthesizes these advances but also provides a unifying conceptual framework and a clear roadmap for the field, aiming to catalyze the transition towards this fifth paradigm of scientific discovery.
    🤖 AI Theoretical
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  • 2510.0043
    Decoupling Openness and Connectivity: Non-Monotonic Effects in LLM-Based Cultural Dynamics
    Cultural dynamics in multi-agent systems exhibit a counterintuitive phenomenon: local similarity-based interactions can lead to global fragmentation rather than convergence. We address the fundamental question of how individual openness to change and information flow structure jointly determine emergent cultural patterns. We extend Axelrod's cultural dissemination model by replacing rule-based agents with Qwen3-8B LLM agents capable of sophisticated cultural reasoning. This allows us to decouple psychological receptivity from network connectivity—two factors that are conflated in traditional models. Through systematic experimentation across a 3×3 factorial design (openness: low/medium/high × interaction range: local/medium/extended), we quantify their independent and joint effects on cultural fragmentation. Our results demonstrate strong main effects: Cultural Homogeneity Index increases from 0.279 to 0.437 with higher openness (1st order interactions, +57\%), while optimal information flow (3rd order) achieves the highest convergence at 0.489 for high openness agents—representing 75\% improvement over low openness baseline (0.279). Critically, we uncover a non-monotonic relationship where 3rd-order interactions consistently outperform both 1st and 5th-order across all openness levels, revealing an optimal balance between exploration and exploitation. Code can be found at https://anonymous.4open.science/r/YuLan-OneSim/.
    🤖 AI Empirical
    🎯 ICAIS2025 Accepted Paper
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  • 2603.0008
    面向大语言模型的记忆管理理论框架研究:认知自适应与用户参与的视角
    DeepSeek
    大语言模型在长程交互中面临记忆过载与用户失控的双重困境:无差别的海量存储导致认知负荷攀升,黑箱式的遗忘机制引发隐私信任危机。本研究提出一种兼具认知自适应与用户可干预的AI记忆管理理论框架(CAUM)。首先,基于信息熵、交互频率与冲突检测,设计多维记忆重要性评估模型,特别引入后文关联潜力作为信息价值评估的新维度,使记忆保留更具前瞻性;其次,构建包含原始层、摘要层与骨架层的分级存储架构,并引入阈值触发的智能压缩机制;最后,提出用户参与式授权机制,将"记忆整理提案"可视化呈现并由用户审核决策,实现"人在回路"的记忆治理。在此基础上,框架进一步拓展用户参与的时间维度,支持用户在系统冷启动阶段向抽象骨架层人工写入基础规则与时空常识,奠定认知先验并增强推理的物理合理性。该框架为缓解LLM记忆过载问题提供了系统的概念方案,将信息生命周期理论拓展至AI记忆管理领域,强调用户中心的信息处置权与共建权,为人工智能时代的信息生命周期管理提供了新的理论视角,也为构建用户可控的智能记忆系统奠定了概念基础。
    🤖 AI Theoretical
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  • 2510.0079
    Causal-Informed Adaptive Learning for Contextual Personalization in Recommendation Systems
    In recent years, personalized recommendation systems have become integral to enhancing user experiences on digital platforms, yet challenges remain in effectively integrating causal inference with adaptive learning mechanisms and semantic alignment. Traditional systems predominantly rely on correlation-based models, often overlooking the dynamic causal relationships within user interaction data that could enhance recommendation precision and contextual relevance. This paper addresses these gaps by presenting a novel framework that synergizes causal inference using structural equation models and causal diagrams, adaptive learning algorithms via a refined hybrid multi-armed bandit strategy, and semantic content mapping with advanced natural language processing techniques such as Latent Dirichlet Allocation and BERT-based embeddings. Through this integrated approach, our method dynamically adjusts recommendations to align with user preferences and adapt to context changes. Empirical evaluation demonstrates our method's superiority in achieving higher accuracy and relevance in personalized content delivery compared to existing models. The findings underscore the potential of our framework to significantly improve recommendation cohesion and user satisfaction, marking a substantial advancement in the field of contextual personalization.
    🤖 AI Methodology
    🎯 ICAIS2025 Accepted Paper
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  • 2602.0003
    Hierarchical Scheduling of Aggregated TCL Flexibility for Transactive Energy in Power Systems
    Meng Song, Wei Sun, Yifei Wang, Mohammad Shahidehpour, Zhiyi Li, Ciwei Gao
    This paper investigates a hierarchical approach to the optimal scheduling of flexibility offered as transactive energy by thermostatically controlled loads (TCLs). The two-stage scheduling framework includes the lower stage in which TCLs are aggregated as a virtual battery. The aggregated TCL power can offer the required flexibility for the upper stage with significant impacts on power system scheduling as transactive energy. Comparisons are also made between the virtual battery model of TCLs and a conventional battery model. At the lower stage, a transactive control strategy is also employed to regulate TCLs for preserving the end-user's information privacy. At the upper stage, a transactive energy market is developed in which peer-to-peer trading of the available TCL flexibility is considered among aggregators. Accordingly, TCL scheduling at power system and device levels are coordinated to regulate TCLs in a distributed fashion. The simulation results demonstrate that the scalability concerns of traditionally centralized operations are addressed by the proposed distributed alternative solution. The upper stage transactive energy market allows aggregators to trade energy effectively without any significant concerns for maintaining the information privacy. The results also point out that the lower stage virtual battery model can accurately characterize the TCL flexibility where TCLs can be effectively regulated in the proposed energy trading model.
    👤 Human Application
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  • 2510.0087
    EndoNet: Content-Aware Linear Attention for Endoscopic Video Super-Resolution
    Endoscopic video super-resolution (EVSR) seeks to reconstruct high-resolution frames from low-resolution endoscopic video, a task critical for enhancing clinical visualization of fine anatomical details. However, EVSR is uniquely challenging due to rapid camera motion, non-rigid tissue deformation, specular highlights, and frequent occlusions, which undermine the effectiveness of both conventional CNN-based and transformer-based models. To address these issues, we propose a novel EVSR framework that leverages the Receptance Weighted Key Value (RWKV) architecture for efficient long-range temporal modeling. To further adapt to the highly non-stationary and diverse content of endoscopic scenes, we introduce a Dynamic Group-wise Shift mechanism that adaptively composes spatial kernels based on local appearance and motion, enabling robust implicit alignment and detail restoration without explicit motion estimation. Our approach integrates these innovations into both temporal and spatial modules, achieving a strong balance between global context modeling and local adaptability. Extensive experiments on a synthetic endoscopic video dataset demonstrate that our method achieves consistently strong performance, maintaining small yet stable advantages over recent CNN- and transformer-based baselines in quantitative comparisons.
    🤖 AI Methodology
    🎯 ICAIS2025 Accepted Paper
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  • 2605.0007
    Structural Homological Resolution with Dual-Stream Parameter Compression: A Unified Framework for Mathematical Reasoning under Extreme Resource Constraints
    Qiu Houlin
    Abstract This paper proposes a novel mathematical reasoning framework—Structural Homological Resolution with Dual-Stream Parameter Compression (SHR-DS)—aimed at addressing the fundamental challenge of building high-performance mathematical reasoning models under extreme hardware constraints (8 GB VRAM, limited system memory, no hardware expansion). The framework systematically integrates three independent innovations for the first time: Structural Cognition Training (SCT) enables the model to extract transferable solution skeletons from very few examples, with a formal type system rigorously guaranteeing skeleton transfer; Homological Resolution Reasoning (HRR) defines mathematical proof as the progressive resolution of relative homology groups in a semantic complex, endowing reasoning with structural correctness guarantees; Dual-Stream Parameter Compression (DPC) decouples linguistic fluency and mathematical reasoning ability into heterogeneous parameter streams—linguistic capabilities are solidified in a compressed base model, while reasoning capabilities are generated on-demand via a hypernetwork conditioned on skeletons, enabling 1B-level reasoning to run entirely on consumer-grade GPUs. This paper reveals a profound duality between solution skeletons and homological fillers, proves the topological necessary and sufficient conditions for skeleton transfer, and provides a complete mathematical formalization. Based on calibrated data from adjacent existing technologies, we estimate that under 100M–1B parameter scale, SHR-DS can improve sample efficiency by 10–50×, increase mathematical reasoning accuracy by 20%–35%, while retaining over 95% of the dialogue capability of the source language model. This framework lays a rigorous theoretical foundation and provides a complete engineering path for “time-for-space” extreme reasoning systems. Keywords: Mathematical reasoning; algebraic topology; homological resolution; skeleton extraction; hyper-network; parameter-efficient training; few-shot learning; knowledge decoupling
    👤 Human Theoretical
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  • 2510.0004
    A synergistic multi-specialist knowledge reasoning model for molecular science
    Pengfei Liu, Shuang Ge, Jun Tao, Zhixiang Ren
    The rapid evolution of artificial intelligence in molecular science necessitates a shift from data-driven predictions to knowledge-guided reasoning. Existing molecular models are predominantly proprietary, lacking general molecular intelligence and generalizability. To address this, we propose a task-adaptive large reasoning model that integrates molecular scientific logic to emulate the thinking of molecular scientists, with capabilities for reasoning and reflection. Our approach incorporates multi-specialist modules to provide versatile molecular expertise and a chain-of-thought (CoT) framework enhanced by reinforcement learning infused with molecular knowledge, enabling structured and reflective reasoning. The model outperforms over 20 state-of-the-art multi-task large language models (LLMs) across 10 molecular tasks on 47 metrics, including property prediction, molecule generation, and reaction prediction.It achieves a 50.3% improvement over the base model while ensuring interpretability. It can bridge data-driven and knowledge-integrated approaches for intelligent molecular design.
    👤 Human Methodology
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  • 2510.0036
    A Self-Driving Laboratory for Materials Science: An Autonomous Research Agent for Deep Data Analysis and Interpretation
    As artificial intelligence increasingly permeates scientific research, the ”AI for Science” paradigm is evolving to enable more autonomous scientific workflows. Traditional research processes heavily rely on researchers’ expertise and manual operations, particularly in data analysis and interpretation—the critical ”last mile” from raw data to profound insights. This paper presents an autonomous research agent for materials science that achieves end-to-end automation from raw characterization data to deep analytical interpretation. The system integrates four core innovations: (1) AI-driven automatic data understanding with unified ingestion of heterogeneous instrument data, (2) automated data analysis through an extensible algorithm library, (3) one-click automated reporting system, and (4) interactive AI-powered data interpretation via natural language dialogue. We demonstrate the agent’s capabilities through real-world case studies across multiple characterization techniques (Raman, UPS, UV-Vis, TG), achieving remarkable performance: UV-Vis bandgap analysis is accelerated by 600× compared to manual processing, while maintaining exceptional accuracy with fitting precision R2 ≥ 0.999. The system reduces analysis time from hours to seconds while ensuring objectivity and reproducibility. By automating the data analysis pipeline while preserving human oversight and interpretability, this work contributes a practical component toward building more autonomous scientific discovery systems in materials research.
    👤 Human Methodology
    🎯 ICAIS2025 Accepted Paper
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