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Deep Learning · Large Language Models

Zhenting Qi, Susanna Maria Baby, Stefanie Baby, Kan Yuan, Da-Cheng Juan, Tu Vu, Andrew Tomkins, Cyrus Rashtchian

Recent work suggests that LLMs can improve their abilities through \textit{self-evolution}, using only internally generated supervision. A central open question, however, is not whether self-evolution can help, but: \textit{how far is it from oracle-supervised training under minimal assumptions?} To address this question, we present a controlled empirical analysis of LLM self-evolution under a strict formulation: self-evolution is allowed access only to (i) an unlabeled prompt set and (ii) a base language model, with all supervision signals generated from this model. Under this formulation, we can evaluate many self-evolution approaches in a unified preference optimization framework. Specifically, we analyze four representative self-evolution methods, ranging from single-round verification to multi-turn feedback, iterative training, and curriculum learning. For our primary analysis, we use a clean setting based on the Knights and Knaves logical reasoning dataset, which provides deterministic solutions, systematic verification, and a hierarchy of difficulty levels that enables an evaluation of easy-to-hard generalization. Across this controlled setting, we find that increasingly complex self-evolution strategies yield consistent but limited gains. In general, a substantial performance gap persists relative to oracle supervision. One strategy stands out as effective: we nearly match the oracle performance by using a larger model (Gemma 12B) with iterative revision based on natural language feedback. We also study self-evolution on the OpenThoughts reasoning corpus and evaluate on standard problem-solving benchmarks. In this regime, self-evolution only leads to modest improvements, including when using more resource-intensive strategies or online RL. Overall, our results shed new light on the empirical limits of various types of self-evolution.

Kaihang Pan, Wendong Bu, Yuruo Wu, Kai Shen, Yang Wu, Yun Zhu, Zehan Wang, liyunfei, ZhaoHang, Juncheng Li 等

While recent autoregressive models have achieved text-to-image generation performance comparable to diffusion models, they significantly struggle with fine-grained semantic alignment. To rigorously evaluate this limitation, we introduce DeltaBench, a benchmark featuring paired prompts with subtle fine-grained differences, which reveals that existing models fail to achieve precise control over visual tokens. To bridge this gap, we propose FineFocus, a comprehensive framework that enhances alignment by learning from subtle differences in similar text-image pairs. Specifically, we construct FineFocus-Data, a large-scale dataset of paired samples derived from image editing tasks to capture localized semantic shifts. Furthermore, we introduce Pair-GRPO, an improved reinforcement learning algorithm that extends GRPO to paired samples. Extensive experiments demonstrate that our approach outperforms most prior prominent methods on both DeltaBench and existing benchmarks.

Applications · Chemistry, Physics, and Earth Sciences

Jiyeon Kim, Youngjoon Hong, Won-Yong Shin

Mesh-based simulations provide high-fidelity solutions to partial differential equations (PDEs), but achieving such accuracy typically requires fine meshes, leading to substantial computational overhead. Super-resolution techniques aim to mitigate this cost by reconstructing high-resolution (HR), high-fidelity solutions from low-cost, low-resolution (LR) counterparts. However, training neural networks for super-resolution often demands large amounts of expensive HR supervision data. To address this challenge, we propose SuperMeshNet, an HR data-efficient super-resolution framework for mesh-based simulations aided by message passing neural networks (MPNNs). As its core, SuperMeshNet introduces complementary learning, a semi-supervised approach that effectively leverages both 1) a small amount of paired LR-HR data and 2) abundant unpaired LR data via two jointly trained, complementary MPNN-based models. Additionally, our model is enriched by inductive biases, which is empirically shown to further improve super-resolution performance. Extensive experiments demonstrate that SuperMeshNet requires 90% less HR data to achieve even lower root mean square error (RMSE) than that of the fully supervised benchmark without the inductive biases. The source code and datasets are available at https://anonymous.4open.science/r/SuperMeshNet/README.md.

Applications · Chemistry, Physics, and Earth Sciences

Jiyeon Kim, Byungju Lee, Won-Yong Shin

Unlike most static material properties widely studied in the machine learning literature, ionic transport properties are inherently dynamic, making their fast and accurate prediction from static atomic structures challenging. The current standard approach, molecular dynamics (MD) simulations, suffers from prohibitively high computational cost. Recent autoregressive learning-based MD acceleration methods requiring sequential inference remain slow and prone to error accumulation; in contrast, existing non-autoregressive material property prediction models are less accurate because they fail to exploit dynamics. Moreover, existing methods typically benefit from datasets either with or without atomic trajectories, but not both. To overcome these limitations, we propose a non-autoregressive learning framework based on modality reduction, which treats atomic trajectories as an auxiliary modality during training but does not require them at inference. This enables a predictor to learn dynamics without requiring sequential inference and to benefit from both types of datasets. As a result, our framework achieves over 200 times speedup compared to autoregressive models on the dataset with atomic trajectories while substantially reducing prediction error relative to non-autoregressive benchmarks across both types of datasets. Our code is available at https://anonymous.4open.science/r/2026.

Applications · Robotics

Fengshuo Bai, Yufeng Li, Ruihai Wu, Peishuo Wang, Yuhan Wang, Bernie Zhu, Yuanfei Wang, Tawei Chou, Gao, Runchuan Zhu 等

Laboratory automation driven by scientific embodied agents represents a critical frontier in modern laboratories. Unlike conventional robotic domains, laboratory environments impose zero-tolerance constraints on manipulation precision and collision, as minor deviations can lead to irreversible chemical hazards or equipment damage. This naturally makes the automated laboratory an ideal testbed for advancing embodied safety. However, existing benchmarks predominantly feature high-tolerance manipulation tasks where intermediate failures are largely reversible. More critically, current Vision-Language-Action (VLA) models trained via static imitation learning cannot satisfy these strict constraints. Because they merely mimic successful demonstrations, they lack the ability to recover from execution drift, leading to catastrophic compounding errors in precision-critical domains. Overcoming this limitation requires transitioning from static datasets to interactive environments that support Reinforcement Learning (RL) for dynamic error recovery. To this end, we introduce SafeLab, a generative simulation benchmark designed for the full lifecycle of safe robot learning. Grounded in a high-fidelity chemistry lab, our framework integrates an {LLM engine} for procedural task synthesis, an {automated expert} for scalable demonstration collection, and an {interactive environment} for continuous RL refinement. Leveraging this infrastructure, we release a dataset of 6,000+ {complex trajectories} to evaluate state-of-the-art VLA models. Experiments reveal that current embodied agents fail significantly under these safety constraints. In contrast, our RL post-training pipeline enables agents to learn active error correction, mitigating hazardous failures and improving success rates by 37\%, thereby establishing SafeLab as a critical platform for developing reliable and safe generalist agents.

General Machine Learning · Unsupervised and Semi-supervised Learning

nan cao, Xu Zhao, Teng Zhang

Multi-instance partial-label learning (MIPL) is a recently proposed learning paradigm to address tasks that multi-instance bags are associated with a candidate label set comprising one ground-truth label and several false positive labels. Existing MIPL methods rely on simple instance level information, and can hardly find the key instances under noisy labels. In this paper, we propose a novel algorithm termed AGOPMIPL, i.e., Average Gradient Outer Product based Multi-Instance Partial-Label Learning to address the problem. AGOP derives a data-dependent metric in the embedding space by computing the outer product of classifier gradients, which stretches discriminative feature dimensions and facilitates more accurate key instance identification. Moreover, AGOP aggregates gradient information across all training samples, providing inherent robustness to label noise. Additionally, we introduce a progressive label disambiguation strategy that gradually refine the learning targets. Experimental studies on benchmark and real-world datasets demonstrate the superiority of AGOPMIPL over existing MIPL methods.

Ze Cai, Hanzhe Liang, Sihang Zeng, Binbin Zhou, Jun Wen

High-order relations involving multiple interacting entities are commonly encountered, particularly in biomedical domains. Existing relational learning methods typically learn static entity representations and assume symmetric relation inference, which can be inadequate for capturing context-dependent entity functions and the inherent asymmetry of high-order relations. In this paper, we propose Contextualized High-order Contrastive Learning (CONTEXTOR), a general and plug-and-play framework that formulates high-order relation inference as a dynamic query–response process. Specifically, CONTEXTOR decomposes each high-order relation into multiple incomplete query tuples and their corresponding response entities. Given a query tuple, we contextualize candidate response entity representations via an asymmetric conditional modulation, and align queries with their corresponding contextualized responses through multi-fold contrastive learning. Extensive experiments on benchmark datasets spanning multiple biomedical tasks demonstrate that CONTEXTOR consistently outperforms existing methods across diverse evaluation settings. Code is available at https://anonymous.4open.science/r/CONTEXTOR-94EE.

Zhaokun Yan, Shan Xu, Wuzheng Dong, Zhaohan Liu, Lijie Feng, Chengxiao Dai, Chen Tianqi, Yingting Li, Yi Zhang, Yunpu Ma 等

Public health reasoning requires population-level inference grounded in scientific evidence, expert consensus, and safety constraints. However, it remains underexplored as a structured machine learning problem with limited supervised signals and benchmarks. We introduce GlobalHealthAtlas, a large-scale multilingual dataset of 280,210 instances spanning 15 public health domains and 17 languages, stratified into three difficulty levels from health literacy to epidemiological and policy reasoning. Instances are derived from openly available public health sources and labeled by language, domain, and difficulty to support supervised learning and slice-based evaluation. We further propose a large language model (LLM) assisted construction and quality-control pipeline with retrieval, duplication, evidence-grounding checks, and label validation to improve consistency at scale. Finally, we present a domain-aligned evaluator distilled from high confidence judgments of diverse LLMs to assess outputs along six dimensions: Accuracy, Reasoning, Completeness, Consensus Alignment, Terminology Norms, and Insightfulness. Together, these contributions enable reproducible training and evaluation of LLMs for public health reasoning beyond conventional QA benchmarks.

Deep Learning · Other Representation Learning

Chunyu Xie, Bin Wang, Fanjing Kong, Jincheng Li, Dawei Liang, Ji Ao, Dawei Leng, Yuhui Yin

Fine-grained vision-language understanding requires precise alignment between visual content and linguistic descriptions, a capability that remains limited in current models, particularly in non-English settings. While models like CLIP perform well on global alignment, they often struggle to capture fine-grained details in object attributes, spatial relations, and linguistic expressions, with limited support for bilingual comprehension. To address these challenges, we introduce FG-CLIP 2, a bilingual vision-language model designed to advance fine-grained alignment for both English and Chinese. Our approach leverages rich fine-grained supervision, including region-text matching and long-caption modeling, alongside multiple discriminative objectives. We further introduce the Textual Intra-modal Contrastive (TIC) loss to better distinguish semantically similar captions. Trained on a carefully curated mixture of large-scale English and Chinese data, FG-CLIP 2 achieves powerful bilingual performance. To enable rigorous evaluation, we present a new benchmark for Chinese multimodal understanding, featuring long-caption retrieval and bounding box classification. Extensive experiments on 29 datasets across 8 tasks show that FG-CLIP 2 outperforms existing methods, achieving state-of-the-art results in both languages. We will release the model, code, and benchmark to facilitate future research on bilingual fine-grained alignment.

Applications · Computer Vision

Yuanyuan Gao, Hao Li, Yifei Liu, Xinhao Ji, Yuning Gong, Yuanjun Liao, Fangfu Liu, Manyuan Zhang, Yuchen Yang, Dan Xu 等

The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question–answer (QA) pairs from a limited number of manually annotated datasets, rather than systematically annotating new large-scale 3D scenes from raw web data. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets. In this work, we propose \textbf{Holi-Spatial}, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline. Holi-Spatial supports multi-level spatial supervision, ranging from geometrically accurate 3D Gaussian Splatting (3DGS) reconstructions with rendered depth maps to object-level and relational semantic annotations, together with corresponding spatial Question–Answer (QA) pairs. Following a principled and systematic pipeline, we further construct \textbf{Holi-Spatial-4M}, the first large-scale, high-quality 3D semantic dataset, containing 12K optimized 3DGS scenes, 1.3M 2D masks, 320K 3D bounding boxes, 320K instance captions, 1.2M 3D grounding instances, and 1.2M spatial QA pairs spanning diverse geometric, relational, and semantic reasoning tasks. Holi-Spatial demonstrates exceptional performance in data curation quality, significantly outperforming existing feed-forward and per-scene optimized methods on datasets such as ScanNet, ScanNet++, and DL3DV. Furthermore, fine-tuning Vision-Language Models (VLMs) on spatial reasoning tasks using this dataset has also led to substantial improvements in model performance.

Yuanyuan Gao, Hao Li, Yifei Liu, Xinhao Ji, Yuning Gong, Yuanjun Liao, Fangfu Liu, Manyuan Zhang, Yuchen Yang, Dan Xu 等

The pursuit of spatial intelligence fundamentally relies on access to large-scale, fine-grained 3D data. However, existing approaches predominantly construct spatial understanding benchmarks by generating question–answer (QA) pairs from a limited number of manually annotated datasets, rather than systematically annotating new large-scale 3D scenes from raw web data. As a result, their scalability is severely constrained, and model performance is further hindered by domain gaps inherent in these narrowly curated datasets. In this work, we propose \textbf{Holi-Spatial}, the first fully automated, large-scale, spatially-aware multimodal dataset, constructed from raw video inputs without human intervention, using the proposed data curation pipeline. Holi-Spatial supports multi-level spatial supervision, ranging from geometrically accurate 3D Gaussian Splatting (3DGS) reconstructions with rendered depth maps to object-level and relational semantic annotations, together with corresponding spatial Question–Answer (QA) pairs. Following a principled and systematic pipeline, we further construct \textbf{Holi-Spatial-4M}, the first large-scale, high-quality 3D semantic dataset, containing 12K optimized 3DGS scenes, 1.3M 2D masks, 320K 3D bounding boxes, 320K instance captions, 1.2M 3D grounding instances, and 1.2M spatial QA pairs spanning diverse geometric, relational, and semantic reasoning tasks. Holi-Spatial demonstrates exceptional performance in data curation quality, significantly outperforming existing feed-forward and per-scene optimized methods on datasets such as ScanNet, ScanNet++, and DL3DV. Furthermore, fine-tuning Vision-Language Models (VLMs) on spatial reasoning tasks using this dataset has also led to substantial improvements in model performance.

Applications · Everything Else

Steven Liu, Jane Luo, Xin Zhang, Aofan Liu, Hao Liu, Jie Wu, Ziyang Huang, Yangyu Huang, Yu Kang, Scarlett Li

Given that Large Language Models (LLMs) are increasingly applied to automate software development, comprehensive software assurance spans three distinct goals: regression prevention, reactive reproduction, and proactive discovery. Current evaluations systematically overlook the third goal. Specifically, they either constrain models to a compliance trap by treating existing code as the ground truth for regression prevention, or rely on post-failure artifacts (e.g., issue reports) for reactive bug reproduction, failing to expose defects before they manifest as failures. To bridge this gap, we present TestExplora, a benchmark designed to evaluate LLMs as proactive testers within full-scale, realistic repository environments. Comprising 2,389 tasks across 482 repositories, TestExplora conceals all defect-related information, forcing models to uncover bugs by identifying discrepancies between implementation and documentation-derived intent—utilizing documentation as the reference oracle. Furthermore, to ensure sustainable evaluation and mitigate risks of data leakage in static datasets, we propose a continuous, time-aware data collection framework. Our evaluation reveals a significant capability gap: state-of-the-art models achieve a maximum Fail-to-Pass ($F2P$) rate of only 16.06%. Further analysis indicates that navigating complex cross-module interactions and leveraging agentic exploration are critical to advancing LLMs toward autonomous software quality assurance. Consistent with this, SWEAgent instantiated with GPT-5-mini achieves an $F2P$ of 17.27% and an $F2P@5$ of 29.7%, highlighting the effectiveness and promise of agentic exploration in proactive bug discovery tasks.

General Machine Learning · Everything Else

Haemin Park, Diego Klabjan, Martin Braun, Xiuqi Li, Balakrishnan Ananthanarayanan

Class imbalance poses a critical challenge in federated learning (FL), where underrepresented classes suffer from poor predictive performance yet cannot be addressed by standard centralized techniques due to privacy and heterogeneity constraints. We propose FedCGNM (Federated Class-Grouped Normalized Momentum), a client-side optimizer in FL that partitions classes into a small number of groups based on minimum within-group variance, maintains a momentum per group, normalizes each group momentum to unit length, and uses the summation of the normalized group momentums as an update direction. This design both equalizes gradient magnitude across majority and minority groups and mitigates the noise inherent in rare-class gradients. We further provide a theoretical convergence analysis explicitly accounting for time-varying resampling rates. Additionally, to efficiently optimize these rates in small-client regimes, we introduce FedHOO, an X-armed-bandit (XAB) based algorithm that exploits federated parallelism that evaluates many combinations of two candidate rates per client at linear cost. Empirical evaluation on four public long-tailed benchmarks and a proprietary chip-defect dataset demonstrates that FedCGNM consistently outperforms baselines, with FedHOO yielding further gains in small-scale federations.

Deep Learning · Graph Neural Networks

Hangyuan Du, Rong Wang, Weihong Zhang, Lu Bai, Liang Bai, Yu Xie, Wenjian Wang

Graph neural networks (GNNs) excel in graph analyzing tasks but often suffer from poor generalization under Out-of-Distribution (OOD) environments. Although this problem has attracted increasing attention, most solutions primarily rely on empirical designs, lacking effective mechanisms to characterize and quantify invariance for graph representation learning. To address these limitations, we propose DIGL, a novel graph learning method that improves the OOD generalization of GNNs. Our work makes an initial attempt to geometrize invariance for graphs by introducing computational optimal transport (OT) theory to characterize invariance principle. Specifically, we formulate the underlying invariant prototype shared by graphs across different environments as a distribution barycenter, and consider graph representations in each specific environment as distortions of the prototype. Building on this idea, we establish an invariant learning framework to promote the model to learn purely invariant graph representations for downstream tasks. Moreover, we derive a unified optimization objective for model implementation and provide theoretical analysis to justify our method. Extensive experiments on a broad range of benchmark datasets demonstrate the superior generalization ability of our method compared with baseline methods under various OOD settings.

Deep Learning · Large Language Models

Meihua Dang, Linxin Song, Honghua Zhang, Jieyu Zhao, Guy Van den Broeck, Stefano Ermon

Generations from large language models often fail to reliably conform to logical constraints such as JSON schema. Existing locally-constrained decoding (LCD) approaches enforce constraints by myopically masking out next tokens, resulting in biased sampling and degradation in downstream performance. Recent work introduces sequential Monte Carlo (SMC) methods to mitigate such sampling biases, but designing effective proposal distributions or potential functions remains a key challenge. In this work, we propose a generic approach to construct proposals and potentials for SMC sampling from $p_{\texttt{lm}}( \cdot \mid \texttt{constraint})$. First, we show that constraints specified as finite automata (FA) can be tensorized for efficient execution on GPUs, which we use to construct *globally-constrained decoding* (GCD) proposals. In addition, leveraging the fact that a tensorized FA shares the same *circuit structure* as hidden Markov models (HMM), we circuit-multiply it with an HMM to obtain the *probabilistic GCD* (P-GCD) proposal that encodes both logical and probabilistic information about the target distribution $p_{\texttt{lm}}( \cdot \mid \texttt{constraint})$. We evaluate (P-)GCD on xLAM, a widely adopted function-calling dataset, and on CommonGen, a keyword-based constrained generation benchmark. Experiments show that compared to LCD proposals, under the same SMC sampling setup, (P-)GCD achieve faster convergence to the target distribution with significantly fewer particles.

Reinforcement Learning · Deep RL

Quang Anh PHAM, Tien Mai, Akshat Kumar

Imitation Learning (IL) has demonstrated strong capabilities in learning high-quality policies from expert demonstrations for sequential decision-making tasks. Nonetheless, its effectiveness is significantly constrained in low-expert-data regimes. To mitigate this issue, previous works introduce ``*offline IL with supplementary data*" which augments expert demonstrations with additional, low-cost data generated by suboptimal policies. A prominent framework for this setting is Distribution Correction Estimation (DICE), which estimates the optimal density ratio by solving the dual of a divergence minimization problem between the learned policy and the expert visitation distribution. Despite their theoretical appeal, existing DICE-based methods often require adding a dataset regularizer to the divergence objective, or rely on a strict coverage assumption. These weaknesses limit the capability of DICE-based methods, causing them to be inefficient in some contexts. In this paper, we introduce ReDICE, a new method to address these limitations. ReDICE is derived by formulating an objective under a mixture distribution from the KL divergence between expert and learned policy occupancies. We formally prove that the dual of this formulation is mathematically equivalent to a stable Gumbel regression objective. Furthermore, we introduce a novel policy extraction mechanism that significantly improves performance in practice. Experiments across diverse benchmarks show that ReDICE achieves state-of-the-art results relative to prior offline IL baselines.

General Machine Learning · Data

Serin Kim, Sangam Lee, Dongha Lee

Large language models have advanced web agents, yet current agents lack personalization capabilities. Since users rarely specify every detail of their intent, practical web agents must be able to interpret ambiguous queries by inferring user preferences and contexts. To address this challenge, we present Persona2Web, the first benchmark for evaluating personalized web agents on the real open web, built upon the clarify-to-personalize principle, which requires agents to resolve ambiguity based on user history rather than relying on explicit instructions. Persona2Web consists of: (1) user histories that reveal preferences implicitly over long time spans, (2) ambiguous queries that require agents to infer implicit user preferences, and (3) a reasoning-aware evaluation framework that enables fine-grained assessment of personalization. We conduct extensive experiments across various agent architectures, backbone models, history access schemes, and queries with varying ambiguity levels, revealing key challenges in personalized web agent behavior. For reproducibility, our codes and datasets are publicly available at https://anonymous.4open.science/r/Persona2Web-73E8

Applications · Everything Else

Luankang Zhang, Hao Wang, Zhongzhou Liu, MINGJIA YIN, Yonghao Huang, Jiaqi Li, Wei Guo, Yong Liu, Huifeng Guo, Defu Lian 等

The scarcity of high-quality training data presents a fundamental bottleneck to scaling machine learning models. This challenge is particularly acute in recommendation systems, where extreme sparsity in user interactions leads to rugged optimization landscapes and poor generalization. We propose the Recursive Self-Improving Recommendation (RSIR) framework, a paradigm in which a model bootstraps its own performance without reliance on external data or teacher models. RSIR operates in a closed loop: the current model generates plausible user interaction sequences, a fidelity-based quality control mechanism filters them for consistency with user’s approximate preference manifold, and a successor model is augmented on the enriched dataset. Our theoretical analysis shows that RSIR acts as a data-driven implicit regularizer, smoothing the optimization landscape and guiding models toward more robust solutions. Empirically, RSIR yields consistent, cumulative gains across multiple benchmarks and architectures. Notably, even smaller models benefit, and weak models can generate effective training curricula for stronger ones. These results demonstrate that recursive self-improvement is a general, model-agnostic approach to overcoming data sparsity, suggesting a scalable path forward for recommender systems and beyond. Our anonymized code is available at https://anonymous.4open.science/r/RSIR-7C5B.

Applications · Neuroscience, Cognitive Science

Wu S. Zihan, Ariane Delrocq, Wulfram Gerstner, Guillaume Bellec

While end-to-end self-supervised learning with backpropagation (global BP-SSL) has become central for training modern AI systems, theories of local self-supervised learning (local-SSL) have struggled to build functional representations in deep neural networks. To establish a link between global and local rules, we first develop a theory for deep linear networks: We identify conditions for local-SSL algorithms (like Forward-forward or CLAPP) to implement exactly the same weight update as a global BP-SSL. Starting from the theoretical insights, we then develop novel variants of local-SSL algorithms to approximate global BP-SSL in deep non-linear convolutional neural networks. Variants that improve the similarity between updates of local-SSL with those of global BP-SSL also show better performance on image datasets (CIFAR-10, STL-10, and Tiny ImageNet). The best local-SSL rule with the CLAPP loss function matches the performance of a comparable global BP-SSL with InfoNCE or CPC-like loss functions, and improves upon state-of-the-art for layer-wise SSL on these benchmarks.

Deep Learning · Large Language Models

Sina Mansouri, Mohit Marvania, Abolfazl Safikhani

The widespread adoption of large language models (LLMs) has intensified the demand for principled methods to distinguish human- from machine-generated text. Watermarking provides a promising avenue, yet existing detectors exhibit sharp performance deterioration under multiple paraphrasing and when applied to shorter texts. We introduce Pattern Stability Score (PSS), a novel detection framework that leverages local statistical features and stability dynamics across paraphrased variants. Specifically, the proposed method combines global and local z-score features with higher-order statistics of run-length patterns, enriched by autocorrelation signals and stability scores computed over paraphrase depth. Numerical evaluations are performed on three benchmark datasets (PG-19, CNN/DailyMail, and WikiText) using multiple LLMs (Llama-3-8B, Qwen2-7B) and paraphrasers (Mistral-7B, Qwen2-7B, Gemma-7B), systematically stress-testing robustness under up to eight rounds of paraphrasing. Compared to prior z-score thresholding baselines and some state-of-the-art deep learning methods, our approach improves detection AUC (area under the receiver operating characteristic curve) by over 10-15 percentage points across different token lengths. Additionally, extensive cross-domain experiments demonstrate that a single universal classifier generalizes across different LLMs, paraphrasers, and text domains without retraining, maintaining above 83.7\% AUC even when all components differ from training.