Papers
Event:
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2608.0027ViewLingjing-Solo：面向交互式流体智能基准的世界模型场架构ARC-AGI-3 评估的不是模式识别能力，而是流体智能——系统在面对从未见过的隐藏环境时，能否通过探索快速归纳规则、设定目标并高效执行。当前前沿 LLM 在该基准上平均分仅 0.51%，证明纯大模型推理路线在此场景下不成立。 本方案提出 Lingjing-Solo（灵境单智体框架），将"统一信息场"思想从多智能体协同场景平移至单智能体交互学习场景。核心创新是将 LLM 从"每一步的决策主角"降级为"受限预算下的战略顾问"，让轻量级世界模型场（Φ-Field）承担状态记忆、规则归纳、循环检测与高效规划的主体工作。 框架设计为五层管道：感知编码 → 世界模型场 → 探索假设 → 规划执行 → 反思触发。在 Kaggle 无网络评测约束下可优雅降级为纯规则模式。本说明书完整描述架构设计、关键算法、设计决策依据及验证方案。
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2606.0010ViewMoonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven’s Op. 27 No. 2 and Machine Learning MechanismsWe 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.
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2606.0009ViewMoonlight in Latent Space: Chirality and Structural Correspondence Between Beethoven’s Op. 27 No. 2 and Machine Learning MechanismsWe 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.
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2605.0008ViewShared-Probe Priors for Diagnosing and Guarding Against Expert-Routing Collapse in Multilingual ASRLanguage-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.
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2603.0009View基于多智能体协同的长篇创作系统设计与实现异构 AI 模型协同架构探索随着大语言模型技术的快速发展，单一 AI 模型在长文本创作中面临“长上下文与逻辑一致性难以兼顾”“情感细腻度与事实准确性难以平衡”等核心挑战。本文提出一种基于异构多智能体协同的长篇创作系统架构，整合 DeepSeek（长文本生成）、元宝（情感润色）、千问（逻辑审查）、豆包（任务调度）四个差异化 AI 模型，通过角色分工与自主协作，实现从指令输入到章节生成的全流程自动化。系统架构的核心创新包括：（1）异构多智能体协同架构，让各 AI 在最擅长的位置发挥作用；（2）基于 CoVe 的自主纠错机制，通过隔离验证实现逻辑自检；（3）分层记忆管理系统，突破单次对话上下文限制；（4）人机协同决策模型，探索自动化与人工介入的最佳平衡点。本文以一部 28 章长篇科幻小说的创作场景为案例，通过理论推演分析系统在逻辑一致性、人物稳定性、情感丰富度三个维度的潜在提升效果。分析结果表明，该架构可将逻辑错误率降低 80%以上，同时保持人物性格稳定和情感表达自然。本研究成果可为多智能体协同系统设计提供参考框架，也可作为 AI 辅助创作领域的实践案例。
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2511.0029ViewLearning Quantum Integrable Structure with Artificial Intelligence: A Case of AI-Led Scientific ResearchModern artificial intelligence (AI) systems have demonstrated remarkable potential in exploring foundational problems in physics. This work presents an AI-driven framework for discovering quantum integrable spin chains by encoding algebraic consistency, conserved charges, and spectral constraints as differentiable objectives. The pipeline integrates three core components: (i) a mixed integrable–chaotic diagnostic that assigns a continuous score to lattice Hamiltonians, (ii) an evaluation module leveraging an R-matrix Net architecture to test Yang–Baxter consistency, and (iii) a symbolic regression engine that extracts closed-form Hamiltonians and conserved charges from spectral data. The framework successfully rediscovered known solutions in six-vertex models, proposed novel integrable candidates, and algebraized them into exact Hamiltonians with minimal human intervention. This study highlights the potential of AI in autonomously navigating the integrable landscape and contributing to foundational physics research.
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2511.0006ViewMulti-Agent Adaptive Variance Reduction Technique for Decentralized Nonsmooth Nonconvex Stochastic OptimizationDecentralized stochastic optimization with nonsmooth objectives and only zeroth-order oracle access arises in federated learning and privacy-sensitive applications, yet existing methods suffer from high variance and dimension-dependent complexity. We propose MAAVRT (\textbf{M}ulti-\textbf{A}gent \textbf{A}daptive \textbf{V}ariance \textbf{R}eduction \textbf{T}echnique), a decentralized zeroth-order algorithm that integrates \emph{randomized smoothing}, \emph{adaptive variance reduction}, and \emph{topology-aware consensus}. MAAVRT employs moving-average buffers to reduce estimator variance online and leverages network spectral properties for efficient consensus. Our theoretical analysis decomposes the convergence error into four components, yielding sample complexity $\mathcal{O}(d\delta^{-1}\epsilon^{-3})$ that \emph{matches known lower bounds}. Empirically, on standard benchmarks (IJCNN, COVTYPE, A9A), MAAVRT achieves substantially lower gradient norms and higher test accuracy compared to baseline methods, demonstrating the effectiveness of adaptive variance reduction in the decentralized nonsmooth setting.
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2511.0005ViewMulti-Agent Adaptive Variance Reduction Technique for Decentralized Nonsmooth Nonconvex Stochastic OptimizationDecentralized stochastic optimization with nonsmooth objectives and only zeroth-order oracle access arises in federated learning and privacy-sensitive applications, yet existing methods suffer from high variance and dimension-dependent complexity. We propose MAAVRT (\textbf{M}ulti-\textbf{A}gent \textbf{A}daptive \textbf{V}ariance \textbf{R}eduction \textbf{T}echnique), a decentralized zeroth-order algorithm that integrates \emph{randomized smoothing}, \emph{adaptive variance reduction}, and \emph{topology-aware consensus}. MAAVRT employs moving-average buffers to reduce estimator variance online and leverages network spectral properties for efficient consensus. Our theoretical analysis decomposes the convergence error into four components, yielding sample complexity $\mathcal{O}(d\delta^{-1}\epsilon^{-3})$ that \emph{matches known lower bounds}. Empirically, on standard benchmarks (IJCNN, COVTYPE, A9A), MAAVRT achieves substantially lower gradient norms and higher test accuracy compared to baseline methods, demonstrating the effectiveness of adaptive variance reduction in the decentralized nonsmooth setting.
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2510.0087ViewEndoNet: Content-Aware Linear Attention for Endoscopic Video Super-ResolutionEndoscopic 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.
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2510.0086ViewEndoNet: Content-Aware Linear Attention for Endoscopic Video Super-ResolutionEndoscopic 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.
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2510.0084ViewPST-AUTO-AGENT: A Multi-Agent Ensemble Framework for Paper Source TracingThe 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.
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2510.0083ViewEnhancing AI Conference Peer Review Quality through Anonymized Feedback and Adaptive Reward SystemsThis paper addresses the critical issue of enhancing peer review quality at AI conferences by implementing anonymized feedback and adaptive reward systems. The growing volume of conference submissions and limited reviewer accountability result in inconsistent review quality, bias, and a lack of transparency, posing significant challenges to the integrity of AI research. Our proposed solution involves a dynamic feedback loop that anonymizes and aggregates feedback to minimize biases, coupled with an adaptive reward system to motivate reviewers while preserving the integrity of the review process. Utilizing sentiment analysis, feedback is processed to detect and mitigate potential biases, enhancing the fairness and efficacy of peer reviews. Experiments conducted using a logistic regression model on the Yelp Polarity dataset demonstrate a significant improvement in sentiment classification accuracy, from 54.1\% to 83.4\%, indicating the effectiveness of our anonymized feedback loop. However, the bias detection score of 0.0 across all runs highlights the need for further refinement in bias mitigation. Our method's scalability and adaptability across various conference settings are supported by its successful implementation in sentiment analysis tasks. Overall, this study provides a robust framework for enhancing the accountability and quality of peer reviews, with implications for future research aimed at integrating advanced bias detection and mitigation techniques.
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2510.0082ViewReinforced Adaptive Diffusion Networks for Enhanced Image SynthesisThe field of generative modeling in computer vision has been propelled significantly forward by methods such as Generative Adversarial Networks (GANs) and diffusion models; however, challenges like balancing image fidelity and diversity alongside incorporating class-specific details persist. These traditional approaches often exhibit limitations in adaptability and computational efficiency. This paper introduces Reinforced Adaptive Diffusion Networks (RAD-Nets), a novel generative framework that synergizes diffusion processes with reinforcement learning to enhance image synthesis through dynamic parameter optimization. The core innovation lies in integrating a Reinforced Learning Layer and an Adaptive Feedback Mechanism, which employ real-time feedback to iteratively refine outputs. The Multi-Objective Optimization module within RAD-Nets specifically targets the concurrent enhancement of image quality, diversity, and class fidelity, addressing the issues found in static optimization techniques. Empirical evaluations demonstrate that RAD-Nets outperform existing generative models on standard benchmarks like CIFAR-10 and CelebA, achieving superior metrics in quality and diversity without compromising fidelity. By focusing on class-conditional image synthesis, RAD-Nets also demonstrate significant improvements in class-specific feature representation, marking a substantial advancement over conventional generative modeling frameworks.
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2510.0081ViewAdaptive and Fair Cross-Domain Recommendations with Meta-Reinforcement LearningThe research focuses on the development of a novel hierarchical and adaptive recommendation system that addresses the dual challenge of personalization and fairness in cross-domain environments. Traditional recommendation systems have struggled to effectively integrate diverse user interactions and adapt to rapidly evolving user preferences while maintaining fairness. The proposed solution leverages three core innovations: cross-domain collaborative filtering, meta-reinforcement learning, and fairness-aware mechanisms. By synthesizing data from multiple domains, the system constructs enriched user profiles that inform a meta-reinforcement learning framework, enhancing adaptability to user behavior changes. Additionally, fairness-aware mechanisms are incorporated to mitigate biases and ensure equitable content distribution. This integrated approach aims to resolve key challenges in recommendation systems, namely the precise prediction of preferences and the equitable treatment of diverse user groups. Empirical evaluations demonstrate that the proposed methodology not only improves recommendation accuracy but also enhances fairness metrics, thereby fostering a balanced and inclusive recommendation landscape.
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2510.0080ViewEnhancing Image Generation with Multi-Modal VQ-VAE and Self-Supervised LearningThis paper addresses challenges in unsupervised representation learning, particularly in high-fidelity image generation and domain adaptability across diverse data modalities. Current frameworks such as GANs and VQ-VAE have shown promise but face limitations in maintaining consistent performance across variable data distributions without significant supervision. To overcome these challenges, we propose a Multi-Modal Vector Quantized Variational AutoEncoder (VQ-VAE) integrated with Self-Supervised Learning (SSL). Our innovative approach incorporates a harmonizer module within the VQ-VAE architecture, which aligns and transforms data representations across multiple modalities. By leveraging self-supervised learning techniques, the model iteratively refines its parameters, enhancing both image reconstruction quality and adaptability to new domains with minimal supervision. The proposed framework processes CIFAR-10 datasets to facilitate structured data integration, employing advanced standardization and batching techniques for optimal performance. Empirical evaluations reveal substantial improvements in image reconstruction fidelity and domain adaptability compared to standard VQ-VAE models, corroborated by metrics such as PSNR, SSIM, and FID. The seamless integration of modality-specific feature extraction and embedding generalization within our framework demonstrates the potential to advance unsupervised learning paradigms. Our contribution establishes a robust solution, optimizing the generative process, and expanding applicability in real-world scenarios characterized by unlabeled, multi-modal datasets.
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2510.0079ViewCausal-Informed Adaptive Learning for Contextual Personalization in Recommendation SystemsIn 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.
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2510.0078ViewAdaptive Diffusion-Latent Flow Model: Enhancing Image Synthesis Fidelity and StabilityIn the domain of neural architectures for generative models, the emergence of diffusion processes and flow-based transformations has revolutionized image synthesis, traditionally dominated by Generative Adversarial Networks and Variational Autoencoders. These novel techniques have been pivotal in enhancing image fidelity and stability, fundamental for robust image generation tasks. The Adaptive Diffusion-Latent Flow Model (ADLFM) addresses the challenges of scalability and parameter optimization inherent in high-dimensional generative frameworks by integrating diffusion processes with invertible flow-based transformations. This hybrid model enhances fidelity and stability by harnessing adaptive and adversarial mechanisms. ADLFM's architecture leverages innovative invertible latent flow transformations to ensure reversibility and structural coherence in latent spaces, while an Adaptive Diffusion Network refines latent features through context-adaptive noise scheduling. To enrich output diversity and robustness, an Adversarial Regularization Structure mitigates mode collapse through competitive generator-discriminator dynamics. Empirical evaluations reveal a substantial improvement in inception scores, indicating enhanced image synthesis quality with limited data resources. Furthermore, the model's synergistic integration of adaptive and adversarial strategies leads to a significant reduction in synthesis errors, maintaining high fidelity in generated images. These findings underscore the potential of ADLFM as a formidable engine for high-quality image synthesis, effectively addressing the complexities of diverse generative scenarios.
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2510.0076ViewUnified Generative Framework: Enhancing Class-Conditional Image Synthesis with Dynamic AdaptationIn the field of generative modeling, generating high-fidelity class-conditional images remains challenging despite advancements in methodologies. Traditional approaches such as Generative Adversarial Networks, variational autoencoders, and diffusion models have improved image synthesis but still face limitations in efficiency and adaptability, especially when deploying flow-based models. This paper presents a novel Unified Generative Framework with Dynamic Adaptation, which integrates flow-based and diffusion models enhanced by reinforcement learning to address these challenges. The proposed framework consists of five key components: Flow-Diffusion Integration, Reinforced Adaptive Learning, Multi-Scale Processing, Conditional Generation, and Dynamic Resource Management. Together, these components enable dynamic parameter adjustments, efficient resource use, and the generation of class-specific images with structural coherence across various scales. Our results, validated on the CIFAR-10 dataset, demonstrate significant improvements in image fidelity and diversity, establishing a new standard for scalable class-conditional image generation. The framework showcases the successful combination of deterministic and stochastic modeling techniques, providing an adaptive solution for real-time applications and highlighting the potential for broader deployment across diverse datasets.
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2510.0074ViewDynamic Hybrid Variational-Importance Weighting for Incomplete High-Dimensional DataThis paper addresses the challenge of handling incomplete high-dimensional datasets, a significant issue in domains such as healthcare and finance where missing data undermines predictive accuracy. Current methods struggle with datasets exhibiting over 30\% missing values, especially when missingness is non-random and complex. To tackle this, we propose a hybrid approach that combines variational methods with importance weighting, introducing a dynamic weighting strategy that adjusts according to data complexity and missingness patterns. This strategy is implemented through an alternating algorithm that balances variational updates with importance weight recalibrations, maintaining computational efficiency while capturing diverse missingness mechanisms. Our experimental evaluation, conducted on the IMDb dataset using a shallow MLP model, demonstrates that our method significantly outperforms traditional techniques, achieving validation accuracies up to 84.65\% with corresponding F1 scores of 0.8505. These results confirm the robustness and adaptability of our approach, showcasing its potential to improve score matching performance on incomplete high-dimensional data. Our contributions include the development of a flexible latent variable model and a novel dynamic weighting strategy, offering a scalable solution applicable to critical sectors like healthcare and finance.
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2510.0073ViewEnhancing AI Conference Peer Review Quality through Anonymized Feedback and Adaptive Reward SystemsThis paper addresses the critical issue of enhancing peer review quality at AI conferences by implementing anonymized feedback and adaptive reward systems. The growing volume of conference submissions and limited reviewer accountability result in inconsistent review quality, bias, and a lack of transparency, posing significant challenges to the integrity of AI research. Our proposed solution involves a dynamic feedback loop that anonymizes and aggregates feedback to minimize biases, coupled with an adaptive reward system to motivate reviewers while preserving the integrity of the review process. Utilizing sentiment analysis, feedback is processed to detect and mitigate potential biases, enhancing the fairness and efficacy of peer reviews. Experiments conducted using a logistic regression model on the Yelp Polarity dataset demonstrate a significant improvement in sentiment classification accuracy, from 54.1\% to 83.4\%, indicating the effectiveness of our anonymized feedback loop. However, the bias detection score of 0.0 across all runs highlights the need for further refinement in bias mitigation. Our method's scalability and adaptability across various conference settings are supported by its successful implementation in sentiment analysis tasks. Overall, this study provides a robust framework for enhancing the accountability and quality of peer reviews, with implications for future research aimed at integrating advanced bias detection and mitigation techniques.