OpenRaiser/Intern-Atlas huggingface dataset:
A Near-Real-Time Reduction-Based Algorithm for Coloring Massive Graphs
Venue: arXiv cs.DS 2025
Year: 2025

The graph coloring problem is a classical combinatorial optimization problem with important applications such as register allocation and task scheduling, and it has been extensively studied for decades. However, near-real-time algorithms that can deliver high-quality solutions for very large real-world graphs within a strict time frame remain relatively underexplored. In this paper, we try to bridge this gap by systematically investigating reduction rules that shrink the problem size while preserving optimality. For the first time, domination reduction, complement crown reduction, and independent set reduction are applied to large-scale instances. Building on these techniques, we propose RECOL, a reduction-based algorithm that alternates between fast estimation of lower and upper bounds, graph reductions, and heuristic coloring. We evaluate RECOL on a wide range of benchmark datasets, including SNAP, the Network Repository, DIMACS10, and DIMACS2. Experimental results show that RECOL consistently outperforms state-of-the-art algorithms on very large sparse graphs within one minute. Additional experiments further highlight the pivotal role of reduction techniques in achieving this performance.
FlowLUT: Efficient Image Enhancement via Differentiable LUTs and Iterative Flow Matching
Venue: arXiv cs.CV 2025
Year: 2025

Deep learning-based image enhancement methods face a fundamental trade-off between computational efficiency and representational capacity. For example, although a conventional three-dimensional Look-Up Table (3D LUT) can process a degraded image in real time, it lacks representational flexibility and depends solely on a fixed prior. To address this problem, we introduce FlowLUT, a novel end-to-end model that integrates the efficiency of LUTs, multiple priors, and the parameter-independent characteristic of flow-matched reconstructed images. Specifically, firstly, the input image is transformed in color space by a collection of differentiable 3D LUTs (containing a large number of 3D LUTs with different priors). Subsequently, a lightweight content-aware dynamically predicts fusion weights, enabling scene-adaptive color correction with $\mathcal{O}(1)$ complexity. Next, a lightweight fusion prediction network runs on multiple 3D LUTs, with $\mathcal{O}(1)$ complexity for scene-adaptive color correction.Furthermore, to address the inherent representation limitations of LUTs, we design an innovative iterative flow matching method to restore local structural details and eliminate artifacts. Finally, the entire model is jointly optimized under a composite loss function enforcing perceptual and structural fidelity. Extensive experimental results demonstrate the effectiveness of our method on three benchmarks.
BioVessel-Net and RetinaMix: Unsupervised Retinal Vessel Segmentation from OCTA Images
Venue: arXiv cs.CV 2025
Year: 2025

Structural changes in retinal blood vessels are critical biomarkers for the onset and progression of glaucoma and other ocular diseases. However, current vessel segmentation approaches largely rely on supervised learning and extensive manual annotations, which are costly, error-prone, and difficult to obtain in optical coherence tomography angiography. Here we present BioVessel-Net, an unsupervised generative framework that integrates vessel biostatistics with adversarial refinement and a radius-guided segmentation strategy. Unlike pixel-based methods, BioVessel-Net directly models vascular structures with biostatistical coherence, achieving accurate and explainable vessel extraction without labeled data or high-performance computing. To support training and evaluation, we introduce RetinaMix, a new benchmark dataset of 2D and 3D OCTA images with high-resolution vessel details from diverse populations. Experimental results demonstrate that BioVessel-Net achieves near-perfect segmentation accuracy across RetinaMix and existing datasets, substantially outperforming state-of-the-art supervised and semi-supervised methods. Together, BioVessel-Net and RetinaMix provide a label-free, computationally efficient, and clinically interpretable solution for retinal vessel analysis, with broad potential for glaucoma monitoring, blood flow modeling, and progression prediction. Code and dataset are available: https://github.com/VikiXie/SatMar8.
Fast Thinking for Large Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Reasoning-oriented Large Language Models (LLMs) often rely on generating explicit tokens step by step, and their effectiveness typically hinges on large-scale supervised fine-tuning or reinforcement learning. While Chain-of-Thought (CoT) techniques substantially enhance performance on complex reasoning tasks, they remain inefficient, requiring long reasoning traces that increase latency and token usage. In this work, we introduce Latent Codebooks for Fast Thinking, a framework that uses concise CoT sketches only during training to learn a codebook of discrete strategy priors. At inference, the model conditions on a handful of continuous thinking vectors distilled from the codebook in a single pass, enabling strategy-level guidance without producing explicit reasoning tokens. To complement this design, we propose GainRouter, a lightweight routing mechanism that adaptively switches between fast codebook guided inference and slow explicit reasoning, thereby suppressing overthinking and reducing unnecessary token generation. Experiments across multiple reasoning benchmarks show that our approach achieves competitive or superior accuracy while substantially lowering inference cost, offering a practical path toward efficient and controllable reasoning in large language models.
Griffin: Generative Reference and Layout Guided Image Composition
Venue: arXiv cs.CV 2025
Year: 2025

Text-to-image models have achieved a level of realism that enables the generation of highly convincing images. However, text-based control can be a limiting factor when more explicit guidance is needed. Defining both the content and its precise placement within an image is crucial for achieving finer control. In this work, we address the challenge of multi-image layout control, where the desired content is specified through images rather than text, and the model is guided on where to place each element. Our approach is training-free, requires a single image per reference, and provides explicit and simple control for object and part-level composition. We demonstrate its effectiveness across various image composition tasks.
Don't Settle Too Early: Self-Reflective Remasking for Diffusion Language Models
Venue: arXiv cs.CL 2025
Year: 2025

Mask-based Diffusion Language Models (DLMs) struggle to revise incorrect tokens: once a token is generated, it typically remains fixed. The key challenge is to identify potential errors in the inputs. In this paper, we propose \emph{\underline{Rem}asking-\underline{e}nabled \underline{Di}ffusion Language Model (RemeDi}, a mask-based DLM that introduces \emph{remasking} as another fundamental mechanism, enabling more flexible text refinement in diffusion-based text generation. To achieve this, RemeDi jointly predicts token distributions and per-token confidence scores at each step. The confidence scores determine which tokens to be unmasked after the current step, allowing the model to identify tokens with low quality and remask them. These remasked tokens can be resampled with richer context in subsequent steps. We design a remask-aware pipeline to train this ability, including supervised fine-tuning which teaches the model to detect and remask incorrect tokens in addition to predict mask tokens, and reinforcement learning which optimizes full generation trajectories toward higher rewards. Experiments show that RemeDi achieves the state-of-the-art results among open-source DLMs on multiple datasets.
Pure Node Selection for Imbalanced Graph Node Classification
Venue: arXiv cs.LG 2025
Year: 2025

The problem of class imbalance refers to an uneven distribution of quantity among classes in a dataset, where some classes are significantly underrepresented compared to others. Class imbalance is also prevalent in graph-structured data. Graph neural networks (GNNs) are typically based on the assumption of class balance, often overlooking the issue of class imbalance. In our investigation, we identified a problem, which we term the Randomness Anomalous Connectivity Problem (RACP), where certain off-the-shelf models are affected by random seeds, leading to a significant performance degradation. To eliminate the influence of random factors in algorithms, we proposed PNS (Pure Node Sampling) to address the RACP in the node synthesis stage. Unlike existing approaches that design specialized algorithms to handle either quantity imbalance or topological imbalance, PNS is a novel plug-and-play module that operates directly during node synthesis to mitigate RACP. Moreover, PNS also alleviates performance degradation caused by abnormal distribution of node neighbors. We conduct a series of experiments to identify what factors are influenced by random seeds. Experimental results demonstrate the effectiveness and stability of our method, which not only eliminates the effect of unfavorable random seeds but also outperforms the baseline across various benchmark datasets with different GNN backbones. Data and code are available at https://github.com/flzeng1/PNS.
HIVTP: A Training-Free Method to Improve VLMs Efficiency via Hierarchical Visual Token Pruning Using Middle-Layer-Based Importance Score
Venue: arXiv cs.CV 2025
Year: 2025

Vision-Language Models (VLMs) have shown strong capabilities on diverse multimodal tasks. However, the large number of visual tokens output by the vision encoder severely hinders inference efficiency, and prior studies have shown that many of these tokens are not important and can therefore be safely pruned. In this work, we propose HIVTP, a training-free method to improve VLMs efficiency via hierarchical visual token pruning using a novel middle-layer-based importance score. Specifically, we utilize attention maps extracted from the middle layers of the vision encoder, which better reflect fine-grained and object-level attention, to estimate visual token importance. Based on this, we propose a hierarchical visual token pruning method to retain both globally and locally important visual tokens. Specifically, we reshape the 1-D visual token sequence output by the vision encoder into a 2-D spatial layout. In the global retaining stage, we divide the image into regions and retain tokens with higher importance scores in each region; in the local retaining stage, we then divide the image into small windows and retain the most important token in each local window. Experimental results show that our proposed method, HIVTP, can reduce the time-to-first-token (TTFT) of LLaVA-v1.5-7B and LLaVA-Next-7B by up to 50.0% and 55.1%, respectively, and improve the token generation throughput by up to 60.9% and 47.3%, without sacrificing accuracy, and even achieving improvements on certain benchmarks. Compared with prior works, HIVTP achieves better accuracy while offering higher inference efficiency.
Satellite: Detecting and Analyzing Smart Contract Vulnerabilities caused by Subcontract Misuse
Venue: arXiv cs.SE 2025
Year: 2025

Developers of smart contracts pervasively reuse subcontracts to improve development efficiency. Like any program language, such subcontract reuse may unexpectedly include, or introduce vulnerabilities to the end-point smart contract. Unfortunately, automatically detecting such issues poses several unique challenges. Particularly, in most cases, smart contracts are compiled as bytecode, whose class-level information (e.g., inheritance, virtual function table), and even semantics (e.g., control flow and data flow) are fully obscured as a single smart contract after compilation. In this paper, we propose Satellite, a new bytecode-level static analysis framework for subcontract misuse vulnerability (SMV) detection in smart contracts. Satellite incorporates a series of novel designs to enhance its overall effectiveness.. Particularly, Satellite utilizes a transfer learning method to recover the inherited methods, which are critical for identifying subcontract reuse in smart contracts. Further, Satellite extracts a set of fine-grained method-level features and performs a method-level comparison, for identifying the reuse part of subcontract in smart contracts. Finally, Satellite summarizes a set of SMV indicators according to their types, and hence effectively identifies SMVs. To evaluate Satellite, we construct a dataset consisting of 58 SMVs derived from real-world attacks and collect additional 56 SMV patterns from SOTA studies. Experiment results indicate that Satellite exhibits good performance in identifying SMV, with a precision rate of 84.68% and a recall rate of 92.11%. In addition, Satellite successfully identifies 14 new/unknown SMV over 10,011 real-world smart contracts, affecting a total amount of digital assets worth 201,358 USD.
Hedonic Neurons: A Mechanistic Mapping of Latent Coalitions in Transformer MLPs
Venue: arXiv cs.LG 2025
Year: 2025

Fine-tuned Large Language Models (LLMs) encode rich task-specific features, but the form of these representations, especially within MLP layers, remains unclear. Empirical inspection of LoRA updates shows that new features concentrate in mid-layer MLPs, yet the scale of these layers obscures meaningful structure. Prior probing suggests that statistical priors may strengthen, split, or vanish across depth, motivating the need to study how neurons work together rather than in isolation. We introduce a mechanistic interpretability framework based on coalitional game theory, where neurons mimic agents in a hedonic game whose preferences capture their synergistic contributions to layer-local computations. Using top-responsive utilities and the PAC-Top-Cover algorithm, we extract stable coalitions of neurons: groups whose joint ablation has non-additive effects. We then track their transitions across layers as persistence, splitting, merging, or disappearance. Applied to LLaMA, Mistral, and Pythia rerankers fine-tuned on scalar IR tasks, our method finds coalitions with consistently higher synergy than clustering baselines. By revealing how neurons cooperate to encode features, hedonic coalitions uncover higher-order structure beyond disentanglement and yield computational units that are functionally important, interpretable, and predictive across domains.
Estimating Time Series Foundation Model Transferability via In-Context Learning
Venue: arXiv cs.LG 2025
Year: 2025

Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training, yet fine-tuning remains critical for boosting performance in domains with limited public data. With the growing number of TSFMs, efficiently identifying the best model for downstream fine-tuning becomes increasingly challenging. In this work, we introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem: given observations on known (source) datasets, it predicts how a TSFM will perform after fine-tuning on a downstream (target) dataset. TimeTic flexibly organizes the observed model-data relationships as contextual information, allowing it to adapt seamlessly to various test-time scenarios. Leveraging the natural tabular structure formed by dataset meta-features, model characteristics, and fine-tuned performance, we employ tabular foundation models to serve as in-context learners. We further introduce a novel model characterization based on entropy evolution across model layers, capturing embedding-space distinctions and enabling TimeTic to generalize across arbitrary model sets. We establish a comprehensive benchmark for transferability estimation including 10 datasets, 10 foundation models, and 3 forecasting tasks. On this benchmark, TimeTic's estimation demonstrates strong alignment with actual fine-tuned performance for previously unseen datasets, achieving a mean rank correlation of approximately 0.6 and a 30% improvement compared to using zero-shot performance as the transferability score.
Confidence Aware SSD Ensemble with Weighted Boxes Fusion for Weapon Detection
Venue: arXiv cs.CV 2025
Year: 2025

The safety and security of public spaces is of vital importance, driving the need for sophisticated surveillance systems capable of accurately detecting weapons, which are often hampered by issues like partial occlusion, varying lighting, and cluttered backgrounds. While single-model detectors are advanced, they often lack robustness in these challenging conditions. This paper presents the hypothesis that ensemble of Single Shot Multibox Detector (SSD) models with diverse feature extraction backbones can significantly enhance detection robustness. To leverage diverse feature representations, individual SSD models were trained using a selection of backbone networks: VGG16, ResNet50, EfficientNet, and MobileNetV3. The study is conducted on a dataset consisting of images of three distinct weapon classes: guns, heavy weapons and knives. The predictions from these models are combined using the Weighted Boxes Fusion (WBF) method, an ensemble technique designed to optimize bounding box accuracy. Our key finding is that the fusion strategy is as critical as the ensemble's diversity, a WBF approach using a 'max' confidence scoring strategy achieved a mean Average Precision (mAP) of 0.838. This represents a 2.948% relative improvement over the best-performing single model and consistently outperforms other fusion heuristics. This research offers a robust approach to enhancing real-time weapon detection capabilities in surveillance applications by demonstrating that confidence-aware fusion is a key mechanism for improving accuracy metrics of ensembles.
CrimEdit: Controllable Editing for Counterfactual Object Removal, Insertion, and Movement
Venue: arXiv cs.CV 2025
Year: 2025

Recent works on object removal and insertion have enhanced their performance by handling object effects such as shadows and reflections, using diffusion models trained on counterfactual datasets. However, the performance impact of applying classifier-free guidance to handle object effects across removal and insertion tasks within a unified model remains largely unexplored. To address this gap and improve efficiency in composite editing, we propose CrimEdit, which jointly trains the task embeddings for removal and insertion within a single model and leverages them in a classifier-free guidance scheme -- enhancing the removal of both objects and their effects, and enabling controllable synthesis of object effects during insertion. CrimEdit also extends these two task prompts to be applied to spatially distinct regions, enabling object movement (repositioning) within a single denoising step. By employing both guidance techniques, extensive experiments show that CrimEdit achieves superior object removal, controllable effect insertion, and efficient object movement without requiring additional training or separate removal and insertion stages.
StrucADT: Generating Structure-controlled 3D Point Clouds with Adjacency Diffusion Transformer
Venue: arXiv cs.GR 2025
Year: 2025

In the field of 3D point cloud generation, numerous 3D generative models have demonstrated the ability to generate diverse and realistic 3D shapes. However, the majority of these approaches struggle to generate controllable 3D point cloud shapes that meet user-specific requirements, hindering the large-scale application of 3D point cloud generation. To address the challenge of lacking control in 3D point cloud generation, we are the first to propose controlling the generation of point clouds by shape structures that comprise part existences and part adjacency relationships. We manually annotate the adjacency relationships between the segmented parts of point cloud shapes, thereby constructing a StructureGraph representation. Based on this StructureGraph representation, we introduce StrucADT, a novel structure-controllable point cloud generation model, which consists of StructureGraphNet module to extract structure-aware latent features, cCNF Prior module to learn the distribution of the latent features controlled by the part adjacency, and Diffusion Transformer module conditioned on the latent features and part adjacency to generate structure-consistent point cloud shapes. Experimental results demonstrate that our structure-controllable 3D point cloud generation method produces high-quality and diverse point cloud shapes, enabling the generation of controllable point clouds based on user-specified shape structures and achieving state-of-the-art performance in controllable point cloud generation on the ShapeNet dataset.
Do LLMs Understand Romanian Driving Laws? A Study on Multimodal and Fine-Tuned Question Answering
Venue: arXiv cs.CL 2025
Year: 2025

Ensuring that both new and experienced drivers master current traffic rules is critical to road safety. This paper evaluates Large Language Models (LLMs) on Romanian driving-law QA with explanation generation. We release a 1{,}208-question dataset (387 multimodal) and compare text-only and multimodal SOTA systems, then measure the impact of domain-specific fine-tuning for Llama 3.1-8B-Instruct and RoLlama 3.1-8B-Instruct. SOTA models perform well, but fine-tuned 8B models are competitive. Textual descriptions of images outperform direct visual input. Finally, an LLM-as-a-Judge assesses explanation quality, revealing self-preference bias. The study informs explainable QA for less-resourced languages.
Robustness of One-to-Many Interdependent Higher-order Networks Against Cascading Failures
Venue: arXiv cs.SI 2025
Year: 2025

In the real world, the stable operation of a network is usually inseparable from the mutual support of other networks. In such an interdependent network, a node in one layer may depend on multiple nodes in another layer, forming a complex one-to-many dependency relationship. Meanwhile, there may also be higher-order interactions between multiple nodes within a layer, which increases the connectivity within the layer. However, existing research on one-to-many interdependence often neglects intra-layer higher-order structures and lacks a unified theoretical framework for inter-layer dependencies. Moreover, current research on interdependent higher-order networks typically assumes idealized one-to-one inter-layer dependencies, which does not reflect the complexity of real-world systems. These limitations hinder a comprehensive understanding of how such networks withstand failures. Therefore, this paper investigates the robustness of one-to-many interdependent higher-order networks under random attacks. Depending on whether node survival requires at least one dependency edge or multiple dependency edges, we propose four inter-layer interdependency conditions and analyze the network's robustness after cascading failures induced by random attacks. Using percolation theory, we establish a unified theoretical framework that reveals how higher-order interaction structures within intra-layers and inter-layer coupling parameters affect network reliability and system resilience. Additionally, we extend our study to partially interdependent hypergraphs. We validate our theoretical analysis on both synthetic and real-data-based interdependent hypergraphs, offering insights into the optimization of network design for enhanced reliability.