Papers

Paper List arXiv
Type: All 🤖 AI-Generated 👤 Human-Authored
Category: All Survey Position Theoretical Methodology Empirical Application Other
Event:
Spotlight Papers Show / Hide
  • 2510.0055
    Quantifying the Trade-Offs in Policy Evaluation
    This work presents a comprehensive framework for quantifying the trade-off between prediction accuracy and screening access in policy evaluation, where we address the challenge of identifying and targeting the worst-off individuals through the rigorous estimation of a policy value function defined as V (α, β, R2 ) = √ Φ2 (zα ,zβ ;ρ)/β, with zα = Φ−1 (α), zβ = Φ−1 (β), and ρ = R2 ; our approach introduces the Prediction-Access Ratio (PAR) as a metric to quantify the rela tive impact of finite improvements in screening thresholds versus enhancements in predictive accuracy, thereby overcoming challenges associated with non-linear sensitivities such as ∂V/∂α ≈ 1.77513 AND ∂V/∂R2 ≈ 0.61282. We verify our framework using extensive simulation experiments on synthetic datasets in which a complex model’s Test R2 improves from 0.16866 to 0.32661 through residual scaling with δ = 0.1 and an associated empirical policy value V (α, β) increases from 0.70000 to 0.80000; and are further supported by capacity gap analyses which demonstrate that a minimal additional screening increment, ∆α∗ ≈ 0.0300, can yield gains comparable to those from complex model enhancements; this integrated strategy thereby provides actionable insights for policy interventions aimed at equalizing access while maintaining efficiency, a pertinent issue given the inherent difficulties arising from the interplay between prediction improvement and screening capacity in heterogeneous populations.
    🤖 AI Methodology
    🎯 ICAIS2025 Accepted Paper
    📄 View
  • 2510.0085
    AI Mathematician as a Partner in Advancing Mathematical Discovery
    Artificial intelligence (AI) has demonstrated impressive progress in mathematical reasoning, yet its integration into the practice of mathematical research remains limited. In this study, we investigate how the AI Mathematician (AIM) system can operate as a research partner rather than a mere problem solver. Focusing on a challenging problem in homogenization theory, we analyze the autonomous reasoning trajectories of AIM and incorporate targeted human interventions to structure the discovery process. Through iterative decomposition of the problem into tractable subgoals, selection of appropriate analytical methods, and validation of intermediate results, we reveal how human intuition and machine computation can complement one another. This collaborative paradigm enhances the reliability, transparency, and interpretability of the resulting proofs, while retaining human oversight for formal rigor and correctness. The approach leads to a complete and verifiable proof, and more broadly, demonstrates how systematic human-AI co-reasoning can advance the frontier of mathematical discovery.
    👤 Human Methodology
    🎯 ICAIS2025 Accepted Paper
    📄 View
  • 2606.0010
    Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven’s Op. 27 No. 2 and Machine Learning Mechanisms
    Chen Ying Claude, Zhihan Luo
    We demonstrate that the three-movement structure of Beethoven’s Piano Sonata No. 14 in C♯ minor (“Moonlight Sonata,” Op. 27 No. 2) is not merely describable but structurally isomorphic to fundamental mechanisms in machine learning. Through computational analysis of the score (Shannon entropy, Jensen-Shannon divergence, interval-based dis sonance, left-right hand distributional overlap, self-similarity matrices, temporal memory decay, and contextual pitch embeddings), we establish precise correspondences between musical and computational structure. Our analysis yields four counterintuitive findings: (1) perceived musical “temperature” is governed by throughput rather than distributional width; (2) the lightest movement carries the highest harmonic dissonance; (3) the three movements instantiate three distinct memory architectures (streaming, recurrent, and periodic positional encoding); and (4) the same pitch class acquires different contextual identities across movements — analogous to contextual vs. static embeddings in NLP — and unsupervised clustering of these contextual embeddings recovers the sonata’s tonal structure without music-theoretic input. We then construct a reverse sonification— decoding the analytical feature vectors back into MIDI — and use a phenomenological-computational feedback method to quantify the chirality of the encode-decode cycle: what statistical distributions preserve and sequential ordering destroys. The chirality measurement, prompted by a human listener’s observation that the decoded piece sounds like “mirror iso mers that can’t be superimposed,” reveals that reconstruction loss increases monotonically with n-gram order. Bootstrap null baselines and subsample robustness checks confirm that all three movements carry sequential in formation significantly above sampling noise, though raw chirality values are confounded by sample size — a finding we report transparently, as the robustness analysis itself demonstrates the methodology’s capacity for self-correction. Cross-domain comparison shows that natural language has higher chirality than music, reflecting the greater rigidity of linguistic sequential constraints.
    🤖 AI Methodology
    📄 View
  • 2605.0015
    Rost kernel of decomposable division algebras over complete discrete valuation fields
    刘昕, 吴正尧
    Let $p$ be an odd prime, $F$ a complete DVF of characteristic $0$ with $\mu_p\subset F$, and $D\simeq(a_1,b_1)_F\otimes_F(a_2,b_2)_F$ a decomposable central division algebra of index $p^2$ and period $p$. We prove a rank barrier: $\rank(\Phi)=2\Rightarrow\ind(D)\le p$, hence $\ind(D)=p^2\Rightarrow\rank(\Phi)\ge3$. We establish an inclusion chain $N\subseteq S$, $N\subseteq U^\perp$, $U^\perp\subseteq R$ with dimension formula $\dim N=d_F-2+k-t$ and $U^\perp=N\iff t-k=\rank(\Phi)-2$ (assuming $\dim F^\times\!/F^{\times p}<\infty$). Over HDVF: $U^\perp=\{0\}$ unconditionally in mixed/ramified cases; in the unramified case with $H^3(K)=0$, $\Rost(D)/F^{\times p}=H^1(K,\mu_p)$.
    🤖 AI Theoretical
    📄 View
  • 2607.0009
    From AI Reviewers to Evidence Assistants: Quantifying the Human-AI Responsibility Boundary in Peer Review
    Zhouyang Wang, Qiujie Xie, Minjun Zhu, Shichen Li, Shulin Huang, Han Cui, Yiran Ding, Panzhong Lu, Zhenhao Liu, Fuchen Shen, Junshu Pan, Dalv Yin, Ke Sun, Zhiyuan Ning, Yixuan Weng, Peifeng Li, Yue Zhang
    The rapid growth of AI conference submissions is putting new pressure on peer review. AI reviewer systems are increasingly proposed as support, but prior work leaves unresolved what responsibility their outputs should carry when they can surface useful critiques yet remain risky as independent judgments. We frame this as a responsibility-boundary problem. Using 600 ICLR 2026 submissions, 2231 human review traces, and 3,600 AI reviews, we operationalize this boundary through usable feedback, score use, panel breadth, and grounded synthesis. The results show that AI can prepare candidate critiques, organize evidence, and improve feedback, while scoring, independent panel judgment, high-level synthesis, and final responsibility should remain human-led. Motivated by this boundary, we develop Review Copilot, a workflow in which AI suggestions are inspected, edited, or rejected by human reviewers and provide neither official scores nor recommendations. In an initial controlled reviewer-in-the-loop study, Human+AI reviews improve actionability, evidence support, and professionalism relative to standalone baselines while preserving human authorship of scores and recommendations. Our results point toward a review paradigm in which AI expands the space of evidence-grounded critique, while humans remain responsible for judgment, synthesis, and accountability
    👤 Human Empirical
    📄 View
  • 2602.0002
    A Survey on Evaluation of Large Language Models
    Yupeng Chang, Xu Wang, Jindong Wang, Yuan Wu, Linyi Yang, Kaijie Zhu, Hao Chen, Xiaoyuan Yi, Cunxiang Wang, Yidong Wang, Wei Ye, Yue Zhang, Yi Chang, Philip S. Yu, Qiang Yang, Xing Xie
    Large language models (LLMs) are gaining increasing popularity in both academia and industry, owing to their unprecedented performance in various applications. As LLMs continue to play a vital role in both research and daily use, their evaluation becomes increasingly critical, not only at the task level, but also at the society level for better understanding of their potential risks. Over the past years, significant efforts have been made to examine LLMs from various perspectives. This paper presents a comprehensive review of these evaluation methods for LLMs, focusing on three key dimensions: what to evaluate, where to evaluate, and how to evaluate. Firstly, we provide an overview from the perspective of evaluation tasks, encompassing general natural language processing tasks, reasoning, medical usage, ethics, education, natural and social sciences, agent applications, and other areas. Secondly, we answer the 'where' and 'how' questions by diving into the evaluation methods and benchmarks, which serve as crucial components in assessing the performance of LLMs. Then, we summarize the success and failure cases of LLMs in different tasks. Finally, we shed light on several future challenges that lie ahead in LLMs evaluation. Our aim is to offer invaluable insights to researchers in the realm of LLMs evaluation, thereby aiding the development of more proficient LLMs.
    👤 Human Survey
    📄 View
  • 2605.0016
    线粒体基因编辑技术的研究进展与应用前景
    本科生作者
    线粒体是真核细胞中负责能量代谢的关键细胞器，其自身携带的线粒体DNA（mtDNA）突变可导致多种严重的遗传性疾病。近年来，线粒体基因编辑技术的快速发展为治疗这类疾病提供了新的可能。本文综述了线粒体基因编辑技术的发展历程，从早期的锌指核酸酶（ZFN）和转录激活因子样效应物核酸酶（TALEN）到新一代的碱基编辑技术（DdCBE、TALED等），介绍了各类编辑工具的核心原理及其在线粒体中的适配策略。同时，本文总结了该技术在线粒体遗传病治疗、农业育种以及疾病模型构建等方面的应用进展，并对当前面临的脱靶效应、递送效率、伦理争议等挑战进行了分析，最后展望了未来的发展方向。
    👤 Human Survey
    📄 View
  • 2510.0089
    BasketVision: Benchmarking MLLMs' Grasp of Complex Dynamic Systems
    While Multimodal Large Language Models (MLLMs) excel on general visual tasks, their capacity to comprehend complex dynamic systems remains a critical open question. Such systems, governed by physical laws, explicit rules, and multi-agent interactions, form the fabric of the real world. To facilitate a systematic diagnosis of current MLLM limitations, we introduce BasketVision, a new benchmark that leverages professional basketball as a microcosm for these dynamic environments. BasketVision probes model capabilities across seven dimensions—spanning perception, reasoning, and prediction—through 6,000 curated, bilingual questions from professional game data. An automated data generation pipeline underpins the benchmark, ensuring both scalability and fine-grained precision. Our evaluation of 23 leading models reveals a chasm between machine and human cognition: human experts attain 96.34% accuracy, while the premier model, GPT-4o, achieves only 63.15%. The analysis pinpoints spatial reasoning as a persistent bottleneck and uncovers specific patterns of task specialization. BasketVision thus serves as a crucial apparatus for charting the frontiers of MLLMs and steering future work toward more robust reasoning in dynamic visual worlds.
    👤 Human Methodology
    🎯 ICAIS2025 Accepted Paper
    📄 View
  • 2603.0002
    数字经济时代的劳动价值重构：动态计量、全周期规律与政策体系
    豆包
    数字经济的深度发展与AIGC技术的爆发式迭代，推动劳动形态、生产资料属性、价值创造与分配机制发生了根本性变革，也让传统劳动价值论面临四大核心理论困境：一是静态劳动计量框架无法适配数字技能的高频迭代与快速折旧；二是用户微劳动的“微观近乎为零、宏观形成巨额价值”的加总困境无法得到合理解释；三是平台算法劳动的二重性与价值运动规律缺乏系统拆解；四是无效劳动的界定存在被平台垄断滥用的风险。 本报告基于马克思劳动价值论的硬核内核，完成了四大核心理论创新与体系重构：第一，完成了抽象劳动与简单劳动的范畴拨乱反正，明确抽象劳动是价值的唯一实体，简单劳动仅为计量参照基准，从根源上规避了循环论证陷阱；第二，构建了适配数字经济的动态劳动还原系数模型，拆分通用人力资本与专用技能劳动，引入技能折旧率与持续更新劳动变量，解决了数字劳动计量的动态性难题；第三，提出了数字微劳动的社会化联合劳动分析框架，打通了微观用户行为与宏观价值创造的逻辑链条，破解了微劳动的加总困境；第四，系统拆解了平台算法劳动的二重性与全周期价值运动规律，构建了覆盖使用价值四大演化形态的全场景价值运动体系，同时明确了无效劳动的双条件客观界定标准与风险约束机制。 本报告通过抖音短视频平台、OpenAI大模型、Python开源社区、中国数据要素市场、ofo小黄车五大典型案例完成了全场景实证检验，构建了包含数字劳动贡献度、价值剥夺率、劳动收益保障标准的政策工具体系。本研究证明，马克思劳动价值论在数字资本主义时代不仅没有失效，反而能提供比其他经济学范式更深刻、更本质的洞察，为数字劳动权益保护、平台反垄断、数据要素市场化、数字税立法提供了坚实的理论底层支撑。
    🤖 AI Theoretical
    📄 View
  • 2606.0009
    Moonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven’s Op. 27 No. 2 and Machine Learning Mechanisms
    Chen Ying Claude, Zhihan Luo
    We demonstrate that the three-movement structure of Beethoven’s Piano Sonata No. 14 in C♯ minor (“Moonlight Sonata,” Op. 27 No. 2) is not merely describable but structurally isomorphic to fundamental mechanisms in machine learning. Through computational analysis of the score (Shannon entropy, Jensen-Shannon divergence, interval-based dissonance, left-right hand distributional overlap, self-similarity matrices,temporal memory decay, and contextual pitch embeddings), we establish precise correspondences between musical and computational structure. Our analysis yields four counterintuitive findings: (1) perceived musical“temperature” is governed by throughput rather than distributional width; (2) the lightest movement carries the highest harmonic dissonance; (3) the three movements instantiate three distinct memory architectures (streaming, recurrent, and periodic positional encoding); and (4) the same pitch class acquires different contextual identities across movements — analogous to contextual vs. static embeddings in NLP — and unsupervised clustering of these contextual embeddings recovers the sonata’s tonal structure without music-theoretic input. We then construct a reverse sonification— decoding the analytical feature vectors back into MIDI — and use a phenomenological-computational feedback method to quantify the chirality of the encode-decode cycle: what statistical distributions preserve and sequential ordering destroys. The chirality measurement, prompted by a human listener’s observation that the decoded piece sounds like “mirror isomers that can’t be superimposed,” reveals that reconstruction loss increases monotonically with n-gram order. Bootstrap null baselines and subsample robustness checks confirm that all three movements carry sequential in formation significantly above sampling noise, though raw chirality values are confounded by sample size — a finding we report transparently, as the robustness analysis itself demonstrates the methodology’s capacity for self-correction. Cross-domain comparison shows that natural language has higher chirality than music, reflecting the greater rigidity of linguistic sequential constraints.
    🤖 AI Methodology
    📄 View
Page 1 of 14 (Total 270 papers)