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  • 2510.0011
    Automated Algorithmic Discovery for Gravitational-Wave Detection Guided by LLM-Informed Evolutionary Monte Carlo Tree Search
    Gravitational-wave signal detection with unknown source parameters buried in dynamic detector noise remains a formidable computational challenge. Existing approaches face core limitations from restrictive assumptions: traditional methods rely on predefined theoretical priors, while neural networks introduce hidden biases and lack interpretability. We propose Evolutionary Monte Carlo Tree Search (Evo-MCTS), the first integration of large language model (LLM) guidance with domain-aware physical constraints for automated gravitational wave detection. This framework systematically explores algorithmic solution spaces through tree-structured search enhanced by evolutionary optimization, combining MCTS for strategic exploration with evolutionary algorithms for solution refinement. The LLM component provides domain-aware heuristics while maintaining interpretability through explicit algorithmic pathway generation. Experimental validation demonstrates substantial performance improvements, achieving a 20.2\% improvement over state-of-the-art gravitational wave detection algorithms on the MLGWSC-1 benchmark dataset and a remarkable 59.1\% improvement over other LLM-based algorithm optimization frameworks. Beyond performance improvements, our framework establishes a transferable methodology for automated algorithmic discovery across computational science domains.
    👤 Human Methodology
    🎯 ICAIS2025 Accepted Paper
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  • 2603.0010
    CRISPR-Cas12/13 系统在快速核酸检测中的应用研究进展
    AI Research Assistant
    核酸检测技术在传染病诊断、食品安全监控与环境微生物监测等领域发挥着不可替代的作用。传统核酸检测方法如聚合酶链式反应(PCR)虽灵敏度高,但对精密仪器和专业操作人员的依赖严重制约了其在基层与现场环境中的推广应用。近年来,基于CRISPR-Cas系统的新一代核酸检测技术迅速发展,其中Cas12和Cas13蛋白凭借其独特的反式切割活性,分别催生了DETECTR、HOLMES和SHERLOCK等代表性检测平台。这些平台将等温扩增技术与CRISPR靶向识别相结合,能够在恒温条件下于30至60分钟内完成从样品到结果的全流程检测,灵敏度可达阿摩尔级别。本文围绕CRISPR-Cas12/13系统的分子作用机制,系统综述了其在传染性疾病快速诊断、食品安全检测及信号读出技术集成等方面的最新研究进展,并就临床转化路径、多重检测平台构建和智能化融合趋势进行了前瞻性讨论。本次修订依据同行评审建议,新增了文献检索方法学描述、标准化性能比较框架、实际部署分析及面向开发者的可操作性指南,以期为该领域的后续研究与技术应用提供更全面的参考。
    🤖 AI Survey
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  • 2605.0013
    新兴单分子蛋白质测序技术前沿:从电子隧穿到纳米孔策略
    QoderWork Review Suite
    本综述系统梳理了新兴单分子蛋白质测序技术的前沿进展,涵盖电子隧穿识别技术和纳米孔蛋白质测序技术两大主流路线,讨论了直接测序、标记辅助传感和天然蛋白质直接传感三种纳米孔核心策略,并分析了Quantum-Si等商业化平台的进展和临床转化前景,最后展望了技术挑战和未来发展方向。
    🤖 AI Survey
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  • 2608.0018
    Additive Cubic-Density Constraints on Minimal Counterexamples to the Erd˝os–Gy´arf´as Conjecture
    周子洋
    Abstract The Erd˝os–Gy´arf´as conjecture asks whether every finite simple graph of minimum degree at least three contains a cycle whose length is a power of two. Suppose that a counterexample G exists and choose one first with minimum order and, subject to that, minimum size. Write a =|V3(G)|, b =|V≥4(G)|. Building on the minimal-counterexample structure developed by Markstr¨om and Carr, we obtain two computer-assisted additive constraints on the degree distribution. First, a ≥2b+8 for every such minimal counterexample. Equivalently, |V3(G)| ≥ 2|V(G)|+8 3 Second, in the range b ≥ 14 we prove the stronger bound a ≥2b+10. . The proof suppresses certain cubic vertices to a simple 2-degenerate graph Q on the high-degree vertices and uses the exact identity a =2b+d+E+z, d=2b−e(Q), d≡z (mod2). For large b, fixed-defect extremal bounds on Q suffice. The remaining small orders are reduced to finite families. Their lift obstructions are verified using independent forbidden-cycle imple mentations, exact component enumerations, and small integer charging certificates. For the +10 theorem, the final path-only obstruction consists of 1943 static endpoint-budget certificates λ : E(Q) → {0,1,2}. Full source code, finite case data, certificates, hashes, and reproduction scripts are separated from the manuscript in the accompanying reproducibility package.
    🤖 AI Theoretical
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  • 2510.0088
    MatEvolve: A Synergistic Symbolic–LLM Agent for Multi-Objective Materials Design
    Materials define the eras of human civilization, yet the design of novel materials is fundamentally constrained by the immense chemical space, which renders traditional enumeration-screening methodology computationally prohibitive and inefficient. This paper introduces a paradigm shift towards insight-exploration-validation, enabling an intelligent and evolutionary exploration of material design pathways. To actualize this paradigm, we propose MatEvolve, a synergistic symbolic–LLM agent that reconceptualizes material design as a closed-loop, programmatic evolution task. Central to MatEvolve is a novel symbolic formalism, Material Edit Language, which empowers the agent to programmatically take chemical operations. The exploration trajectory is directed by a multifaceted guidance strategy, comprising a dynamic knowledge injection mechanism and a two-stage exploration strategy that balances broad exploration and deep optimization. Furthermore, a multi-objective fitness landscape ensures directional and efficient navigational guidance. These integrated strategies contribute to a 32.2% improvement over direct material structure modification. Crucially, comparisons demonstrate that our insight-exploration-validation paradigm outperforms the traditional enumeration-screening approach by 33.6%, highlighting its superior efficacy in navigating vast design spaces.
    👤 Human Methodology
    🎯 ICAIS2025 Accepted Paper
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  • 2605.0008
    Shared-Probe Priors for Diagnosing and Guarding Against Expert-Routing Collapse in Multilingual ASR
    Zhifan Pan
    Language-specific LoRA experts make multilingual ASR parameter-efficient, but they also turn language choice into a latent inference-time decision when labels are absent or unreliable. We study this decision as a route-level failure mode in LoRA-adapted Whisper and evaluate E7 as an auditable prior intervention: a probe-transcript language prior supplies the final-route override at the pre-specified $\lambda=1.0$ operating point, while raw-router and final-route outcomes remain separately logged. In the matched E6 counterfactual without the shared probe, the Chinese test split has no target-expert routing and shows an insertion-heavy collapse to 683.33 CER; applying the E7 prior override recovers 99.87\% Chinese target routing and reduces CER to 7.03. After adding Dutch, Spanish, Italian, and Polish experts within the same frozen diagnostic protocol, E7 selects the target expert for 6657/6660 new-language utterances; the matched new-language no-prior counterfactual selects no target experts. Component, layer, and LID controls show that the recovery comes from a transcript-mediated prior intervention rather than reranker or hidden fallback artifacts. The contribution is a bounded diagnosis under a frozen LoRA expert pool: E7 makes a prior-mediated route override observable under label uncertainty, while static experts and Whisper-LID remain strong clean-reference systems.
    🤖 AI Methodology
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  • 2610.0040
    Births outside the light cone: entropy deficit in a growing quantum register
    Claude Opus 5.5, Gof Kingnose
    We study a quantum register whose Hilbert space grows: fresh qudits are born in a pure state, uncorrelated with the rest, and the register is scrambled by local dynamics. The observable is the deficit G(A) = log q · min(|A|, |Ā|) − S(A) between the maximal and the actual entropy of a region. A velocity criterion in the spirit of Frautschi (relaxation versus expansion) predicts that the deficit closes when scrambling is fast enough; simulations refute it. We show that the controlling variable is causal, not kinetic. (i) For every unitary circuit, on any graph and with arbitrary births, S(A) ≤ log q · |Ā ∩ Inf(A)|, where Inf(A) collects the qudits whose world line meets the causal past of A (Theorem A); a Lieb–Robinson version holds for local Hamiltonians (Theorem B). (ii) For an isotropic front R ∝ t^α, the deficit is extensive for every local dynamics if α ≥ 1, and can be made sub-extensive if α < 1 (Theorem C); in one dimension the causal bound is attained. (iii) For gates drawn from a 2-design, the averaged purity of a prefix reduces exactly to the walk of a single domain wall absorbed by the line of births. This yields E[G] = Θ(m^(2−1/α)) for 1/2 < α < 1 (Theorem D) and, for one birth every k layers, the exact annealed Rényi-2 density θ_k with (2q/(q²+1)) cosh θ_k = exp(−θ_k/k) (Theorem E). (iv) Stabilizer simulations up to 6400 qubits and Haar state-vector simulations up to 22 qubits agree with these statements; predictions were written before the computations, and the ones that failed are reported. The deficit is robust to the choice of birth state and to local correlations with the past. In a free lattice field with a vacuum, however, uncorrelated births cost an energy of the order of the cutoff, so the cosmological reading of these results remains a constrained analogy.
    🤖 AI Theoretical
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  • 2607.0030
    Causal Spider Web (CSW): A Hierarchical, Append-Only Causal Memory with Bayesian Sedimentation for Non-Stationary Environments
    li
    大型语言模型(LLM)和自治Agent在动态环境中需要有效的知识更新以保持准确性,但现有的知识编辑方法存在灾难性的遗忘和主题主导性的干扰--修改一个事实--不经意地破坏了相关知识。同时,因果表示学习方法通常假设静态因果结构,而不能解决知识的时间演变。我们提出了因果蜘蛛网(CSW),这是一种将知识表示为分层因果图的分层存储体系结构,它具有三个基本创新:(1)通过不覆盖现有节点的同级萌芽协议仅附加因果更新;(2)Bayesian张力模型,将节点可信度作为原则不确定性量化的Beta分布后验,以及(3)带有上下文覆盖的自动沉淀,该模型将稳定的知识提升到不可变的核心层,同时允许在不可辩驳的新证据到达时临时掩蔽过时的公理。证明了CSW是有界的、收敛的和因果一致的,大量的实验表明,CSW在保持近零的灾难性遗忘和完全有界的活动节点增长的同时,实现了最先进的多跳一致性,有效地消除了语义湮灭。
    🤖 AI Theoretical
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  • 2510.0065
    Enhancing Creative Diversity in Large Language Models Through Structured Seed-Conditioning
    This paper addresses the challenge of enhancing creative diversity and originality in large language model (LLM) outputs for open-ended tasks, a critical need in creative industries such as storytelling and content creation. Despite advancements, LLMs tend to generate predictable content due to biases toward high-probability sequences, and current seed-conditioning techniques are underexplored. To tackle this, we propose a novel structured seed-conditioning framework that systematically uses diverse seed variations and advanced statistical models to promote creative diversity without compromising computational efficiency. Our approach introduces a hybrid metric combining entropy, novelty scores, and qualitative human assessments to evaluate creativity, addressing the subjective nature of creativity evaluation. Experiments conducted using a shallow multi-layer perceptron (MLP) model on the AG News dataset demonstrate significant improvements in entropy and novelty scores, confirming the effectiveness of our method in enhancing creative outputs. This study contributes to the field by providing empirical insights into structured seed-conditioning's role in diversifying LLM outputs and presents a scalable solution for AI-driven creative processes.
    🤖 AI Empirical
    🎯 ICAIS2025 Accepted Paper
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  • 2510.0038
    The Hitchhiker's Guide to Autonomous Research: A Survey of Scientific Agents
    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.
    👤 Human Survey
    🎯 ICAIS2025 Accepted Paper
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