OpenRaiser/Intern-Atlas huggingface dataset:
MambaX: Image Super-Resolution with State Predictive Control
Venue: arXiv cs.CV 2025
Year: 2025

Image super-resolution (SR) is a critical technology for overcoming the inherent hardware limitations of sensors. However, existing approaches mainly focus on directly enhancing the final resolution, often neglecting effective control over error propagation and accumulation during intermediate stages. Recently, Mamba has emerged as a promising approach that can represent the entire reconstruction process as a state sequence with multiple nodes, allowing for intermediate intervention. Nonetheless, its fixed linear mapper is limited by a narrow receptive field and restricted flexibility, which hampers its effectiveness in fine-grained images. To address this, we created a nonlinear state predictive control model \textbf{MambaX} that maps consecutive spectral bands into a latent state space and generalizes the SR task by dynamically learning the nonlinear state parameters of control equations. Compared to existing sequence models, MambaX 1) employs dynamic state predictive control learning to approximate the nonlinear differential coefficients of state-space models; 2) introduces a novel state cross-control paradigm for multimodal SR fusion; and 3) utilizes progressive transitional learning to mitigate heterogeneity caused by domain and modality shifts. Our evaluation demonstrates the superior performance of the dynamic spectrum-state representation model in both single-image SR and multimodal fusion-based SR tasks, highlighting its substantial potential to advance spectrally generalized modeling across arbitrary dimensions and modalities.
Paper2SysArch: Structure-Constrained System Architecture Generation from Scientific Papers
Venue: arXiv cs.AI 2025
Year: 2025

The manual creation of system architecture diagrams for scientific papers is a time-consuming and subjective process, while existing generative models lack the necessary structural control and semantic understanding for this task. A primary obstacle hindering research and development in this domain has been the profound lack of a standardized benchmark to quantitatively evaluate the automated generation of diagrams from text. To address this critical gap, we introduce a novel and comprehensive benchmark, the first of its kind, designed to catalyze progress in automated scientific visualization. It consists of 3,000 research papers paired with their corresponding high-quality ground-truth diagrams and is accompanied by a three-tiered evaluation metric assessing semantic accuracy, layout coherence, and visual quality. Furthermore, to establish a strong baseline on this new benchmark, we propose Paper2SysArch, an end-to-end system that leverages multi-agent collaboration to convert papers into structured, editable diagrams. To validate its performance on complex cases, the system was evaluated on a manually curated and more challenging subset of these papers, where it achieves a composite score of 69.0. This work's principal contribution is the establishment of a large-scale, foundational benchmark to enable reproducible research and fair comparison. Meanwhile, our proposed system serves as a viable proof-of-concept, demonstrating a promising path forward for this complex task.
UltraFlux: Data-Model Co-Design for High-quality Native 4K Text-to-Image Generation across Diverse Aspect Ratios
Venue: arXiv cs.CV 2025
Year: 2025

Diffusion transformers have recently delivered strong text-to-image generation around 1K resolution, but we show that extending them to native 4K across diverse aspect ratios exposes a tightly coupled failure mode spanning positional encoding, VAE compression, and optimization. Tackling any of these factors in isolation leaves substantial quality on the table. We therefore take a data-model co-design view and introduce UltraFlux, a Flux-based DiT trained natively at 4K on MultiAspect-4K-1M, a 1M-image 4K corpus with controlled multi-AR coverage, bilingual captions, and rich VLM/IQA metadata for resolution- and AR-aware sampling. On the model side, UltraFlux couples (i) Resonance 2D RoPE with YaRN for training-window-, frequency-, and AR-aware positional encoding at 4K; (ii) a simple, non-adversarial VAE post-training scheme that improves 4K reconstruction fidelity; (iii) an SNR-Aware Huber Wavelet objective that rebalances gradients across timesteps and frequency bands; and (iv) a Stage-wise Aesthetic Curriculum Learning strategy that concentrates high-aesthetic supervision on high-noise steps governed by the model prior. Together, these components yield a stable, detail-preserving 4K DiT that generalizes across wide, square, and tall ARs. On the Aesthetic-Eval at 4096 benchmark and multi-AR 4K settings, UltraFlux consistently outperforms strong open-source baselines across fidelity, aesthetic, and alignment metrics, and-with a LLM prompt refiner-matches or surpasses the proprietary Seedream 4.0.
Hierarchical Linkage Clustering Beyond Binary Trees and Ultrametrics
Venue: arXiv cs.LG 2025
Year: 2025

Hierarchical clustering seeks to uncover nested structures in data by constructing a tree of clusters, where deeper levels reveal finer-grained relationships. Traditional methods, including linkage approaches, face three major limitations: (i) they always return a hierarchy, even if none exists, (ii) they are restricted to binary trees, even if the true hierarchy is non-binary, and (iii) they are highly sensitive to the choice of linkage function. In this paper, we address these issues by introducing the notion of a valid hierarchy and defining a partial order over the set of valid hierarchies. We prove the existence of a finest valid hierarchy, that is, the hierarchy that encodes the maximum information consistent with the similarity structure of the data set. In particular, the finest valid hierarchy is not constrained to binary structures and, when no hierarchical relationships exist, collapses to a star tree. We propose a simple two-step algorithm that first constructs a binary tree via a linkage method and then prunes it to enforce validity. We establish necessary and sufficient conditions on the linkage function under which this procedure exactly recovers the finest valid hierarchy, and we show that all linkage functions satisfying these conditions yield the same hierarchy after pruning. Notably, classical linkage rules such as single, complete, and average satisfy these conditions, whereas Ward's linkage fails to do so.
Hierarchical Semi-Supervised Active Learning for Remote Sensing
Venue: arXiv cs.CV 2025
Year: 2025

The performance of deep learning models in remote sensing (RS) strongly depends on the availability of high-quality labeled data. However, collecting large-scale annotations is costly and time-consuming, while vast amounts of unlabeled imagery remain underutilized. To address this challenge, we propose a Hierarchical Semi-Supervised Active Learning (HSSAL) framework that integrates semi-supervised learning (SSL) and a novel hierarchical active learning (HAL) in a closed iterative loop. In each iteration, SSL refines the model using both labeled data through supervised learning and unlabeled data via weak-to-strong self-training, improving feature representation and uncertainty estimation. Guided by the refined representations and uncertainty cues of unlabeled samples, HAL then conducts sample querying through a progressive clustering strategy, selecting the most informative instances that jointly satisfy the criteria of scalability, diversity, and uncertainty. This hierarchical process ensures both efficiency and representativeness in sample selection. Extensive experiments on three benchmark RS scene classification datasets, including UCM, AID, and NWPU-RESISC45, demonstrate that HSSAL consistently outperforms SSL- or AL-only baselines. Remarkably, with only 8%, 4%, and 2% labeled training data on UCM, AID, and NWPU-RESISC45, respectively, HSSAL achieves over 95% of fully-supervised accuracy, highlighting its superior label efficiency through informativeness exploitation of unlabeled data. Our code will be publicly available.
pFedBBN: A Personalized Federated Test-Time Adaptation with Balanced Batch Normalization for Class-Imbalanced Data
Venue: arXiv cs.LG 2025
Year: 2025

Test-time adaptation (TTA) in federated learning (FL) is crucial for handling unseen data distributions across clients, particularly when faced with domain shifts and skewed class distributions. Class Imbalance (CI) remains a fundamental challenge in FL, where rare but critical classes are often severely underrepresented in individual client datasets. Although prior work has addressed CI during training through reliable aggregation and local class distribution alignment, these methods typically rely on access to labeled data or coordination among clients, and none address class unsupervised adaptation to dynamic domains or distribution shifts at inference time under federated CI constraints. Revealing the failure of state-of-the-art TTA in federated client adaptation in CI scenario, we propose pFedBBN,a personalized federated test-time adaptation framework that employs balanced batch normalization (BBN) during local client adaptation to mitigate prediction bias by treating all classes equally, while also enabling client collaboration guided by BBN similarity, ensuring that clients with similar balanced representations reinforce each other and that adaptation remains aligned with domain-specific characteristics. pFedBBN supports fully unsupervised local adaptation and introduces a class-aware model aggregation strategy that enables personalized inference without compromising privacy. It addresses both distribution shifts and class imbalance through balanced feature normalization and domain-aware collaboration, without requiring any labeled or raw data from clients. Extensive experiments across diverse baselines show that pFedBBN consistently enhances robustness and minority-class performance over state-of-the-art FL and TTA methods.
ActDistill: General Action-Guided Self-Derived Distillation for Efficient Vision-Language-Action Models
Venue: arXiv cs.CV 2025
Year: 2025

Recent Vision-Language-Action (VLA) models have shown impressive flexibility and generalization, yet their deployment in robotic manipulation remains limited by heavy computational overhead and inference latency. In this work, we present ActDistill, a general action-guided self-derived distillation framework that transfers the action prediction capability of any existing VLA model to a lightweight counterpart. Unlike previous efficiency strategies that primarily emphasize vision-language correlations, ActDistill leverages action priors to guide knowledge transfer and model compression, achieving action-oriented efficiency for VLA models. Specifically, we employ a well-trained VLA model as the teacher and introduce a graph-structured encapsulation strategy to explicitly model the hierarchical evolution of action prediction. The student model, derived from the graph-encapsulated teacher, is further equipped with a dynamic router that adaptively selects computation paths based on action prediction demands, guided by hierarchical graph-informed supervision to ensure smooth and efficient evolution. During inference, graph-related auxiliary components are removed, allowing the student to execute only dynamically routed layers and predict high-precision actions with minimal computation and latency. Experiments on embodied benchmarks demonstrate that ActDistill achieves comparable or superior performance to full-scale VLA models while reducing computation by over 50% with up to 1.67 times speedup, thereby establishing a general paradigm toward efficient embodied intelligence.
Towards Harnessing the Power of LLMs for ABAC Policy Mining
Venue: arXiv cs.CR 2025
Year: 2025

This paper presents an empirical investigation into the capabilities of Large Language Models (LLMs) to perform automated Attribute-based Access Control (ABAC) policy mining. While ABAC provides fine-grained, context-aware access management, the increasing number and complexity of access policies can make their formulation and evaluation rather challenging. To address the task of synthesizing concise yet accurate policies, we evaluate the performance of some of the state-of-the-art LLMs, specifically Google Gemini (Flash and Pro) and OpenAI ChatGPT, as potential policy mining engines. An experimental framework was developed in Python to generate randomized access data parameterized by varying numbers of subjects, objects, and initial policy sets. The baseline policy sets, which govern permission decisions between subjects and objects, serve as the ground truth for comparison. Each LLM-generated policy was evaluated against the baseline policy using standard performance metrics. The results indicate that LLMs can effectively infer compact and valid ABAC policies for small-scale scenarios. However, as the system size increases, characterized by higher numbers of subjects and objects, LLM outputs exhibit declining accuracy and precision, coupled with significant increase in the size of policy generated, which is beyond the optimal size. These findings highlight both the promise and limitations of current LLM architectures for scalable policy mining in access control domains. Future work will explore hybrid approaches that combine prompt optimization with classical rule mining algorithms to improve scalability and interpretability in complex ABAC environments.
Video4Edit: Viewing Image Editing as a Degenerate Temporal Process
Venue: arXiv cs.CV 2025
Year: 2025

We observe that recent advances in multimodal foundation models have propelled instruction-driven image generation and editing into a genuinely cross-modal, cooperative regime. Nevertheless, state-of-the-art editing pipelines remain costly: beyond training large diffusion/flow models, they require curating massive high-quality triplets of \{instruction, source image, edited image\} to cover diverse user intents. Moreover, the fidelity of visual replacements hinges on how precisely the instruction references the target semantics. We revisit this challenge through the lens of temporal modeling: if video can be regarded as a full temporal process, then image editing can be seen as a degenerate temporal process. This perspective allows us to transfer single-frame evolution priors from video pre-training, enabling a highly data-efficient fine-tuning regime. Empirically, our approach matches the performance of leading open-source baselines while using only about one percent of the supervision demanded by mainstream editing models.
SCALER: SAM-Enhanced Collaborative Learning for Label-Deficient Concealed Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Existing methods for label-deficient concealed object segmentation (LDCOS) either rely on consistency constraints or Segment Anything Model (SAM)-based pseudo-labeling. However, their performance remains limited due to the intrinsic concealment of targets and the scarcity of annotations. This study investigates two key questions: (1) Can consistency constraints and SAM-based supervision be jointly integrated to better exploit complementary information and enhance the segmenter? and (2) beyond that, can the segmenter in turn guide SAM through reciprocal supervision, enabling mutual improvement? To answer these questions, we present SCALER, a unified collaborative framework toward LDCOS that jointly optimizes a mean-teacher segmenter and a learnable SAM. SCALER operates in two alternating phases. In \textbf{Phase \uppercase\expandafter{\romannumeral1}}, the segmenter is optimized under fixed SAM supervision using entropy-based image-level and uncertainty-based pixel-level weighting to select reliable pseudo-label regions and emphasize harder examples. In \textbf{Phase \uppercase\expandafter{\romannumeral2}}, SAM is updated via augmentation invariance and noise resistance losses, leveraging its inherent robustness to perturbations. Experiments demonstrate that SCALER yields consistent performance gains across eight semi- and weakly-supervised COS tasks. The results further suggest that SCALER can serve as a general training paradigm to enhance both lightweight segmenters and large foundation models under label-scarce conditions. Code will be released.
eBPF-PATROL: Protective Agent for Threat Recognition and Overreach Limitation using eBPF in Containerized and Virtualized Environments
Venue: arXiv cs.CR 2025
Year: 2025

With the increasing use and adoption of cloud and cloud-native computing, the underlying technologies (i.e., containerization and virtualization) have become foundational. However, strict isolation and maintaining runtime security in these environments has become increasingly challenging. Existing approaches like seccomp and Mandatory Access Control (MAC) frameworks offer some protection up to a limit, but often lack context awareness, syscall argument filtering, and adaptive enforcement, providing the ability to adjust decisions at runtime based on observed application behavior, workload changes, or detected anomalies rather than relying solely on static or predefined rules.This paper introduces eBPF-PATROL (eBPF-Protective Agent for Threat Recognition and Overreach Limitation), an extensible lightweight runtime security agent that uses extended Berkeley Packet Filter (eBPF) technology to monitor and enforce policies in containerized and virtualized environments. By intercepting system calls, analyzing execution context, and applying user-defined rules, eBPF-PATROL detects and prevents real-time boundary violations, such as reverse shells, privilege escalation, and container escape attempts. We describe the architecture, implementation, and evaluation of eBPF-PATROL, demonstrating its low overhead (< 2.5 percent) and high detection accuracy across real-world attack scenarios.
Vector Arithmetic in Concept and Token Subspaces
Venue: arXiv cs.CL 2025
Year: 2025

In order to predict the next token, LLMs must represent semantic and surface-level information about the current word. Previous work identified two types of attention heads that disentangle this information: (i) Concept induction heads, which copy word meanings, and (ii) Token induction heads, which copy literal token representations (Feucht et al., 2025). We show that these heads can be used to identify subspaces of model activations that exhibit coherent semantic structure in Llama-2-7b. Specifically, when we transform hidden states using the attention weights of concept heads, we are able to more accurately perform parallelogram arithmetic (Mikolov et al., 2013) on the resulting hidden states, e.g., showing that "Athens" - "Greece" + "China" = "Beijing". This transformation allows for much higher nearest-neighbor accuracy (80%) than direct use of raw hidden states (47%). Analogously, we show that token heads allow for transformations that reveal surface-level word information in hidden states, allowing for operations like "coding" - "code" + "dance" = "dancing".
Nested Unfolding Network for Real-World Concealed Object Segmentation
Venue: arXiv cs.CV 2025
Year: 2025

Deep unfolding networks (DUNs) have recently advanced concealed object segmentation (COS) by modeling segmentation as iterative foreground-background separation. However, existing DUN-based methods (RUN) inherently couple background estimation with image restoration, leading to conflicting objectives and requiring pre-defined degradation types, which are unrealistic in real-world scenarios. To address this, we propose the nested unfolding network (NUN), a unified framework for real-world COS. NUN adopts a DUN-in-DUN design, embedding a degradation-resistant unfolding network (DeRUN) within each stage of a segmentation-oriented unfolding network (SODUN). This design decouples restoration from segmentation while allowing mutual refinement. Guided by a vision-language model (VLM), DeRUN dynamically infers degradation semantics and restores high-quality images without explicit priors, whereas SODUN performs reversible estimation to refine foreground and background. Leveraging the multi-stage nature of unfolding, NUN employs image-quality assessment to select the best DeRUN outputs for subsequent stages, naturally introducing a self-consistency loss that enhances robustness. Extensive experiments show that NUN achieves a leading place on both clean and degraded benchmarks. Code will be released.
The Workflow as Medium: A Framework for Navigating Human-AI Co-Creation
Venue: arXiv cs.CY 2025
Year: 2025

This paper introduces the Creative Intelligence Loop (CIL), a novel socio-technical framework for responsible human-AI co-creation. Rooted in the 'Workflow as Medium' paradigm, the CIL proposes a disciplined structure for dynamic human-AI collaboration, guiding the strategic integration of diverse AI teammates who function as collaborators while the human remains the final arbiter for ethical alignment and creative integrity. The CIL was empirically demonstrated through the practice-led creation of two graphic novellas, investigating how AI could serve as an effective creative colleague within a subjective medium lacking objective metrics. The process required navigating multifaceted challenges including AI's 'jagged frontier' of capabilities, sycophancy, and attention-scarce feedback environments. This prompted iterative refinement of teaming practices, yielding emergent strategies: a multi-faceted critique system integrating adversarial AI roles to counter sycophancy, and prioritizing 'feedback-ready' concrete artifacts to elicit essential human critique. The resulting graphic novellas analyze distinct socio-technical governance failures: 'The Steward' examines benevolent AI paternalism in smart cities, illustrating how algorithmic hubris can erode freedom; 'Fork the Vote' probes democratic legitimacy by comparing centralized AI opacity with emergent collusion in federated networks. This work contributes a self-improving framework for responsible human-AI co-creation and two graphic novellas designed to foster AI literacy and dialogue through accessible narrative analysis of AI's societal implications.
Typing Reinvented: Towards Hands-Free Input via sEMG
Venue: arXiv cs.HC 2025
Year: 2025

We explore surface electromyography (sEMG) as a non-invasive input modality for mapping muscle activity to keyboard inputs, targeting immersive typing in next-generation human-computer interaction (HCI). This is especially relevant for spatial computing and virtual reality (VR), where traditional keyboards are impractical. Using attention-based architectures, we significantly outperform the existing convolutional baselines, reducing online generic CER from 24.98% -> 20.34% and offline personalized CER from 10.86% -> 10.10%, while remaining fully causal. We further incorporate a lightweight decoding pipeline with language-model-based correction, demonstrating the feasibility of accurate, real-time muscle-driven text input for future wearable and spatial interfaces.
Adaptive Conformal Prediction for Quantum Machine Learning
Venue: arXiv cs.LG 2025
Year: 2025

Quantum machine learning seeks to leverage quantum computers to improve upon classical machine learning algorithms. Currently, robust uncertainty quantification methods remain underdeveloped in the quantum domain, despite the critical need for reliable and trustworthy predictions. Recent work has introduced quantum conformal prediction, a framework that produces prediction sets that are guaranteed to contain the true outcome with user-specified probability. In this work, we formalise how the time-varying noise inherent in quantum processors can undermine conformal guarantees, even when calibration and test data are exchangeable. To address this challenge, we draw on Adaptive Conformal Inference, a method which maintains validity over time via repeated recalibration. We introduce Adaptive Quantum Conformal Prediction (AQCP), an algorithm which preserves asymptotic average coverage guarantees under arbitrary hardware noise conditions. Empirical studies on an IBM quantum processor demonstrate that AQCP achieves target coverage levels and exhibits greater stability than quantum conformal prediction.