Papers

How will language models (LMs) affect future economic progress? Inspired by the Lever of Riches by Mokyr (1992), we argue that the institutions governing LM content generation and usage patterns are critical to answering this question. We content that, because LM creators have a strong incentive to collect, train, and deploy intellectual property protection, the all-you-can-consume access to knowledge and creativity they enable has led to rapid acceptance and widespread use, which in turn results in smaller, low-skill employment creation but increased output and greater overall welfare. We provide a theoretical and analytical framework explaining this phenomenon and point to its long-term consequences using empirical evidence.

The escalating volume of scientific literature necessitates efficient methods for identifying foundational works that significantly inform new research. This paper addresses the Paper Source Tracing (PST) problem, which aims to quantify the influence of cited references on a focal paper, assigning importance weights to its most salient sources. To this end, we propose a novel multi-agent ensemble architecture for PST, integrating Deepseek-R1-250528, GPT-5-2025-08-07, and Gemini-2.5-pro. Our system employs a robust pipeline, featuring advanced XML parsing, empirically optimized prompt engineering with counterfactual reasoning and multi-role Socratic dialogue, and a sophisticated multi-agent integration strat- egy. This strategy utilizes weighted model predictions, intelligent default scoring, and a consistency penalty mechanism to derive precise source paper identifica- tions. Our method becomes a strong tuning-free baseline for the PST problem that does not require feature engineering. Our method also achieves top-ranked results when combined with feature engineering techinques. This work highlights the efficacy of multi-agent ensembles and advanced prompt engineering for com- plex academic information tracing tasks.

Machine learning and deep learning methodologies have revolutionized computational approaches to complex problem-solving across numerous domains, emerging as transformative technologies in artificial intelligence research [1,7]. This comprehensive review synthesizes current literature to examine the theoretical foundations, methodological advancements, and practical implementations of these techniques, highlighting their evolution from basic machine learning concepts to sophisticated deep neural architectures [2,9]. The analysis demonstrates remarkable success in applications ranging from computer vision and natural language processing to healthcare diagnostics and autonomous systems, with deep learning models achieving unprecedented performance in pattern recognition tasks [3,4,8]. However, significant challenges persist, including the need for massive labeled datasets, computational resource requirements, model interpretability issues, and inherent parameter redundancy in deep architectures [5,6]. The review identifies emerging opportunities in transfer learning, few-shot learning, and explainable AI as promising research directions [10]. By critically evaluating both current limitations and future potential, this analysis provides a structured framework for researchers to advance the field while addressing practical implementation barriers across diverse application domains.

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/.

Large language models (LLMs) are rapidly transforming scientific discovery, showing promise in hypothesis generation, literature understanding, and symbolic reasoning. Yet, their capacity to conduct iterative, feedback-driven molecular design---a hallmark of real-world drug and materials discovery---remains underexplored. Existing benchmarks typically cast molecular tasks as one-shot question-answering or text-to-molecule translation, neglecting the iterative propose-evaluate-refine process central to scientific practice. We propose \textbf{ICIMBench}, an \textit{In-Context Iterative Molecular Design Benchmark} that evaluates LLMs in multi-turn molecular design episodes. In each task, the model receives a natural-language specification, generates candidate molecules in SMILES format, and iteratively refines them based on deterministic oracle feedback from RDKit. We introduce the \textbf{NumEval} metric---the number of evaluations required to satisfy the target---which captures both performance efficiency and robustness under realistic evaluation budgets. Experiments on frontier models (GPT-5, DeepSeek-V3.2, Intern-S1) show that while single-property design is largely solved (NumEval $=1$) by state-of-the-art LLMs like GPT-5, multi-property optimization remains a strong challenge, especially under coupled constraints such as lipophilicity and scaffold similarity. ICIMBench provides a principled framework for probing the in-context reasoning and adaptive optimization abilities of LLMs, paving the way toward autonomous, language-driven molecular discovery.

This paper introduces a novel approach to sea ice modeling using Graph Neural Networks (GNNs), utilizing the natural graph structure of sea ice, where nodes represent individual ice pieces, and edges model the physical interactions, including collisions. This concept is developed within a one-dimensional framework as a foundational step. Traditional numerical methods, while effective, are computationally intensive and less scalable. By utilizing GNNs, the proposed model, termed the Collision-captured Network (CN), integrates data assimilation (DA) techniques to effectively learn and predict sea ice dynamics under various conditions. The approach was validated using synthetic data, both with and without observed data points, and it was found that the model accelerates the rendering of trajectories without compromising accuracy. This advancement offers a more efficient tool for forecasting in marginal ice zones (MIZ) and highlights the potential of combining machine learning with data assimilation for more effective and efficient modeling.

In this article, we introduce the Fuzzy logic-based attention (Fuzzy Attention Layer) mechanism, a novel computational approach designed to enhance the interpretability and efficacy of neural models in psychological research. The fuzzy attention layer integrated into the transformer encoder model to analyze complex psychological phenomena from neural signals captured by functional near-infrared spectroscopy (fNIRS). By leveraging fuzzy logic, the fuzzy attention layer learns and identifies interpretable patterns of neural activity. This addresses a significant challenge in using transformers: the lack of transparency in determining which specific brain activities most contribute to particular predictions. Our experimental results, obtained from fNIRS data engaged in social interactions involving handholding, reveal that the fuzzy attention layer not only learns interpretable patterns of neural activity but also enhances model performance. In addition, these patterns provide deeper insights into the neural correlates of interpersonal touch and emotional exchange. The application of our model shows promising potential in understanding the complex aspects of human social behavior, verify psychological theory with machine learning algorithms, thereby contributing significantly to the fields of social neuroscience and AI. Presented version based on the work published in IEEE TFS (2025)

Machine learning and artificial intelligence will be deeply embedded in the intelligent systems humans use to automate tasking, optimize planning, and support decision-making. We present a critical review of uncertainty quantification (UQ) in large language models (LLMs), synthesizing insights from over 80 papers across leading venues (ACL, ASE, NeurIPS, ICML, AAAI, IJCAI, Nature, and others). We introduce UQ-Net, a unified probabilistic framework that combines Bayesian modeling, calibration, conformal prediction, and selective decision rules to disentangle epistemic and aleatoric uncertainty and to support reliable decision thresholds. UQ-Net integrates uncertainty estimates with calibration procedures and anomaly detection to enable safer selective deployment of LLM agents. Through case studies in medical diagnosis and code generation, we demonstrate that UQ-Net improves calibration and reduces predictive error by 15–20% relative to standard baselines. We survey existing evaluation practices and identify critical gaps: misalignment of consistency and entropy with factuality, lack of benchmarks for multi-episode interactions, and inconsistent metrics for calibration and tightness. We advocate for context-aware datasets, standardized metrics, and human-in-the-loop evaluations to better align UQ methods with deployment needs. Our review and proposed framework offer a principled foundation for operationalizing UQ in LLMs, advancing the development of trustworthy, responsible agentic AI for safety-sensitive, real-world applications.

The advancement of LLM-based agents is redefining AI for Science (AI4S) by enabling autonomous scientific research. Prominent LLMs exhibited expertise across multiple domains, catalysing constructions of domain-specialised scientific agents. Nevertheless, the profound epistemic and methodological gaps between AI and the natural sciences still impede the systematic design, training, and validation of these agents. This survey bridges the existing gap by presenting an exhaustive blueprint for scientific agents, spanning systematic construction methodologies, targeted capability enhancement, and rigorous evaluations. Anchored in the canonical scientific workflow, this paper (i) pinpoints the overview of scientific agents, starting with the development from general-purpose agents to scientific agents driven by articulated goal-orientation, then subsequently advancing a comprehensive taxonomy that organises existing agents by construction strategy and capability scope, and (ii) introduces a two-tier progressive framework, from scientific agents contrustion from scratch to targeted capability enhancement, for realizing autonomous scientific research. It is our aspiration that this survey will serve as guidance for researchers across various domains, facilitating the systematic design of domain-specific scientific agents and stimulating further innovation in AI-driven scientific research. To support long-term progress, we curate a live repository (\href{https://github.com/gudehhh666/Awesome_Scientific_Agent.git}{\textsc{Awesome\_Scientific\_Agent}}) that continuously aggregates emerging methods, benchmarks, and best practices.

Mega-cap technology equities, cryptocurrencies, and gold increasingly co-determine modern portfolio outcomes, yet their joint dynamics are regime-dependent and incompletely understood. This paper studies the state-dependent comovement among Apple Inc. (AAPL), Bitcoin (BTC), and gold (XAU) using daily data from 2015 to 2025. We triangulate three complementary lenses: (i) rolling Pearson correlations to trace smooth co-movement and structural shifts; (ii) a Diebold–Yilmaz connectedness framework based on rolling 252-day VARs and generalized forecast-error variance decompositions to identify directional risk transmitters and receivers; and (iii) empirical copulas to estimate lower- and upper-tail dependence at the 5% threshold, contrasting crash versus rally dynamics. We document three core results. First, the AAPL–BTC correlation rose from near zero before 2020 to roughly 0.30 during the COVID-19 period and has remained persistently elevated through 2025, indicating a lasting post-pandemic regime. Second, gold emerges as a net transmitter of shocks while BTC is a net receiver, revising the canonical view of gold as a purely passive safe haven. Third, AAPL–BTC exhibits pronounced asymmetry in the tails, with downside dependence considerably stronger than upside (“crash correlation”). Robustness checks spanning window sizes, VAR lags, alternative dependence metrics, and tail thresholds corroborate these findings. The portfolio implication is that static diversification across tech, crypto, and gold underperforms precisely when insurance is most needed. We advocate regime-aware allocation that monitors connectedness and transmitter identity, budgets explicitly for joint-tail risk, and uses dynamic overlays. The results also inform macro-prudential monitoring: supervisors should track transmitter rotations and connectedness spikes that presage cross-market stress transmission.

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.

Large Language Models (LLMs) hold substantial potential for accelerating academic ideation but face critical challenges in grounding ideas and mitigating confirmation bias for further refinement. We propose integrating motivational knowledge graphs and socratic dialogue to address these limitations in enhanced LLM ideation (MotivGraph-SoIQ). This novel framework provides essential grounding and practical idea improvement steps for LLM ideation by integrating a Motivational Knowledge Graph (MotivGraph) with a Q-Driven Socratic Ideator. The MotivGraph structurally stores three key node types-problem, challenge, and solution—to offer motivation grounding for the LLM ideation process. The Ideator is a dual-agent system utilizing Socratic questioning, which facilitates a rigorous refinement process that mitigates confirmation bias and improves idea quality across novelty, experimental rigor, and motivational rationality dimensions. On the ICLR25 paper topics dataset, MotivGraph-SoIQ exhibits clear advantages over existing state-of-the-art approaches across LLM-based scoring, ELO ranking, and human evaluation metrics.

Designing high-performance object detection architectures is a complex task, where traditional manual design is time-consuming and labor-intensive, and Neural Architecture Search (NAS) is computationally prohibitive. While recent approaches using Large Language Models (LLMs) show promise, they often function as iterative optimizers within a search loop, rather than generating architectures directly from a holistic understanding of the data. To address this gap, we propose Cognitive-YOLO, a novel framework for LLM-driven architecture synthesis that generates network configurations directly from the intrinsic characteristics of the dataset. Our method consists of three stages: first, an analysis module extracts key meta-features (e.g., object scale distribution and scene density) from the target dataset; second, the LLM reasons upon these features, augmented with state-of-the-art components retrieved via Retrieval-Augmented Generation (RAG), to synthesize the architecture into a structured neural network description, which we term the Neural Architecture Description Language (NADL); finally, a compiler instantiates this description into a deployable model. Extensive experiments on five diverse object detection datasets demonstrate that our proposed Cognitive-YOLO consistently generates superior architectures, achieving state-of-the-art (SOTA) performance by outperforming strong baseline models across multiple benchmarks.

This review examines the ongoing transformation of artificial intelligence applications in biomedical research, tracing the evolution from data-driven to insightdriven approaches. It synthesizes advances in AI-powered multimodal data integration techniques, including early, intermediate, late, and hybrid fusion strategies that effectively combine heterogeneous biomedical data sources. The review explores how network-based computational frameworks and single-cell technologies are revolutionizing disease mechanism analysis through multi-omics integration, enabling the identification of dysregulated pathways and potential therapeutic targets. It further evaluates AI’s role in enabling precision medicine through personalized diagnostics, treatment selection, and radiomics-based healthcare. The integration of AI with various omics disciplines has enhanced understanding of disease mechanisms at molecular, cellular, and tissue levels, creating unprecedented opportunities for early diagnosis and targeted therapeutics. The review concludes by addressing critical challenges including model explainability and data privacy considerations, while highlighting the emergence of closed-loop AI systems that actively participate in scientific discovery through continuous learning and adaptation. These developments collectively signal a paradigm shift toward AI systems that not only analyze biomedical data but generate actionable insights that advance clinical practice and scientific understanding

This comprehensive review examines the transformative role of artificial intelli- gence in biomedical research, from foundational data integration to clinical ap- plications. The paper explores how AI techniques facilitate multimodal data fu- sion across diverse biological data types, employing both traditional statistical methods and advanced deep learning architectures including variational autoen- coders, graph neural networks, and transformer models. It evaluates AI appli- cations in medical imaging, where convolutional neural networks have achieved remarkable diagnostic accuracy (up to 94% in COVID-19 detection) while en- hancing segmentation and classification tasks across multiple imaging modalities. The review further investigates generative AI’s impact on molecular design and drug discovery, highlighting transformer-based architectures like TransAntivirus that navigate vast chemical spaces to optimize therapeutic candidates. Finally, it examines AI-enabled precision medicine applications, including Clinical Deci- sion Support Systems and federated learning approaches that balance analytical power with privacy preservation. Despite significant progress, implementation challenges persist, including data heterogeneity, model explainability, and ethical concerns regarding bias and privacy. The paper underscores the importance of developing interpretable AI systems that integrate seamlessly into clinical workflows while addressing regulatory, ethical, and economic considerations to realize the full potential of AI in advancing biomedical research and healthcare delivery.

随着生成式人工智能与智能体（Agent）技术的迅猛发展，新闻传播领域正经历从"内容数字化" 向"认知智能化"的范式转型。受众智能体作为能够模拟、预测甚至替代部分人类受众认知与行为的新型数 字实体，其在新闻生产、分发与反馈各环节的深度嵌入，在提升传播效率的同时也引发了复杂的伦理挑战。 本文结合2025年斯坦福大学AI行为研究、中国AI大模型测评报告等最新实证数据，系统审视受众智能体 在新闻传播中的应用所衍生的伦理风险，并构建相应的规制路径。研究发现，受众智能体的伦理风险主要 集中在三个层面：在模拟层面，存在"数字孪生"失真、归因悖论与信任赤字的风险；在影响层面，面临商 业价值侵蚀公共属性、人机协同失当导致价值偏移的困境；在驯化层面，则遭遇技术依赖导致的主体性消 解与规则滞后带来的治理真空。针对上述风险，本文借鉴动态能力理论，提出一个以"感知-捕捉-重构"为 核心的多维治理框架，为新型主流媒体在智能时代的稳健变革提供兼具学理与实践价值的方案。

Recommender systems operating on heterogeneous, multi-relational graphs contend with noise and incompleteness in auxiliary signals, which can destabilize learning and degrade ranking performance when targeting robust representations. Naive cross-view training risks propagating noise across views, and existing contrastive or augmentation-based schemes often hinge on design choices and can struggle to scale to large, complex graphs. We propose a latent-diffusion guided cross-view alignment framework for heterogeneous graph recommendation that jointly learns a relation-aware heterogeneous GNN encoder, producing paired target and auxiliary embeddings, and a compact, time-conditioned latent-space denoiser that maps noisy auxiliary latents toward target-view semantics. The denoiser provides principled supervision to disentangle structured noise, with its residual outputs fused into target embeddings to refine ranking-relevant representations. Training optimizes a joint denoising objective and a ranking objective, enabling scalable, robust cross-view alignment without ad-hoc augmentations. Empirical results on implicit-feedback data demonstrate improved robustness and ranking accuracy under noisy auxiliary signals, with flexible gradient-flow and fusion strategies supporting stable end-to-end training on large graphs. Ablations highlight the benefits of explicit noise modeling in auxiliary views, diffusion-based supervision for stability, and scalable, relation-aware encoding of practical significance for recommender systems.

本文探讨AI是否具有意识这一前沿问题。通过建立一套评估体系，收集整理最新研究结果，对AI的意识水平进行打分评估。基于哲学、神经科学和心理学三个维度的综合分析，结果显示当前AI意识的整体支持度约为43.84%。直观的结果图表可访问 acw.gixia.org 查看。

Abstract. Solar energy, as a clean and renewable resource, has gained significant global attention. In contrast to urban areas, where buildings vary in height and are often obstructed, the relatively flat ru-ral buildings in northern China provide optimal conditions for solar panel installation. Consequently, the solar energy potential of northern rural areas has attracted significant attention from researchers. Traditional studies typically rely on solar radiation simulation software and 3D models to estimate solar radiation and the solar energy potential of buildings. However, the lack of comprehensive and accurate 3D building model data for rural areas in China has significantly hindered progress in this field. To address this limitation, this study proposes a novel method for rapidly estimating the solar energy potential of rural buildings by integrating deep learning algorithms with parametric modeling platforms. Using convolution neural networks (CNNs), the proposed method efficiently and accurate-ly extracts building footprints from complex satellite imagery. These footprints are then imported in-to the Grasshopper parametric platform to generate and optimize vector outlines of buildings. By combining these outlines with digital surface model (DSM) data containing building height infor-mation, the study constructs precise 3D building models. Furthermore, GPU-accelerated solar simula-tion software, Vitality 2.0, is used for rapid solar energy potential estimation. The study conducted building roof extraction based on satellite imagery for 31 villages in Tianjin and generated parametric three-dimensional village models. Through simulation, the research found that due to the relatively low height of village buildings and the absence of mutual shading between buildings, the larger the village scale, the greater the roof area, and consequently, the higher the photovoltaic power genera-tion capacity of the village. The study also revealed that metal roofs, which have better heat dissipa-tion, result in higher photovoltaic panel conversion efficiency. Therefore, compared to villages with roofs primarily made of concrete and ceramic tiles, villages dominated by metal roofs can recoup all the costs of photovoltaic panels in a shorter period.

The 2024 Physics and Chemistry Nobel Prizes to machine learning (ML) and artificial intelligence (AI) breakthroughs marked “Year 1 of AI for Science,” underscoring their transformative role in physical sciences. Yet data are not the same as understanding—a distinction central to chemistry, which has long relied on concepts such as bond, aromaticity, and reactivity as scaffolds for understanding and explanation. Building on our recent perspectives (ACS Phys. Chem. Au 2024, 4, 135–142; J. Chem. Theory Compt. 2025, DOI: 10.1021/acs.jctc.5c01299), this article explores how ML/AI can become engines of chemical understanding. We introduce a quintet of chemical knowledge—ontology, epistemology, theory, concept, and understanding—and develop the metaphors of the Knowledge Tree and Knowledge Forest to show how diverse epistemologies interact and recursively enrich one another. Case studies on aromaticity, catalysis, orbital-free density functional theory, and protein folding illustrate how ML features, when interpreted as conceptual roots, yield fruits of understanding. Contrasting multiscale modeling with hierarchical modeling, we argue that ML enables emergent, concept-driven integration across levels. Cultivating this plural and hierarchical ecosystem may guide theoretical chemistry toward its next breakthroughs, resolving Dirac’s dilemma not by brute force but by forests of concepts that transform data into enduring understanding.