OpenRaiser/Intern-Atlas huggingface dataset:
ARPGNet: Appearance- and Relation-aware Parallel Graph Attention Fusion Network for Facial Expression Recognition
Venue: arXiv cs.CV 2025
Year: 2025

The key to facial expression recognition is to learn discriminative spatial-temporal representations that embed facial expression dynamics. Previous studies predominantly rely on pre-trained Convolutional Neural Networks (CNNs) to learn facial appearance representations, overlooking the relationships between facial regions. To address this issue, this paper presents an Appearance- and Relation-aware Parallel Graph attention fusion Network (ARPGNet) to learn mutually enhanced spatial-temporal representations of appearance and relation information. Specifically, we construct a facial region relation graph and leverage the graph attention mechanism to model the relationships between facial regions. The resulting relational representation sequences, along with CNN-based appearance representation sequences, are then fed into a parallel graph attention fusion module for mutual interaction and enhancement. This module simultaneously explores the complementarity between different representation sequences and the temporal dynamics within each sequence. Experimental results on three facial expression recognition datasets demonstrate that the proposed ARPGNet outperforms or is comparable to state-of-the-art methods.
Department-Specific Security Awareness Campaigns: A Cross-Organizational Study of HR and Accounting
Venue: arXiv cs.CR 2025
Year: 2025

Many cyberattacks succeed because they exploit flaws at the human level. To address this problem, organizations rely on security awareness programs, which aim to make employees more resilient against social engineering. While some works have suggested that such programs should account for contextual relevance, the common praxis in research is to adopt a "general" viewpoint. For instance, instead of focusing on department-specific issues, prior user studies sought to provide organization-wide conclusions. Such a protocol may lead to overlooking vulnerabilities that affect only specific subsets of an organization. In this paper, we tackle such an oversight. First, through a systematic literature review, we provide evidence that prior literature poorly accounted for department-specific needs. Then, we carry out a multi-company and mixed-methods study focusing on two pivotal departments: human resources (HR) and accounting. We explore three dimensions: threats faced by these departments; topics covered in the security-awareness campaigns delivered to these departments; and delivery methods that maximize the effectiveness of such campaigns. We begin by interviewing 16 employees of a multinational enterprise, and then use these results as a scaffold to design a structured survey through which we collect the responses of over 90 HR/accounting members of 9 organizations. We find that HR is targeted through job applications containing malware and executive impersonation, while accounting is exposed to invoice fraud, credential theft, and ransomware. Current training is often viewed as too generic, with employees preferring shorter, scenario-based formats like videos and simulations. These preferences contradict the common industry practice of annual sessions. Based on these insights, we propose recommendations for designing awareness programs tailored to departmental needs and workflows.
Balancing Two-Dimensional Straight-Line Programs
Venue: arXiv cs.DS 2025
Year: 2025

We consider building, given a straight-line program (SLP) consisting of $g$ productions deriving a two-dimensional string $T$ of size $N\times N$, a structure capable of providing random access to any character of $T$. For one-dimensional strings, it is now known how to build a structure of size $\mathcal{O}(g)$ that provides random access in $\mathcal{O}(\log N)$ time. In fact, it is known that this can be obtained by building an equivalent SLP of size $\mathcal{O}(g)$ and depth $\mathcal{O}(\log N)$ [Ganardi, Jeż, Lohrey, JACM 2021]. We consider the analogous question for two-dimensional strings: can we build an equivalent SLP of roughly the same size and small depth? We show that the answer is negative: there exists an infinite family of two-dimensional strings of size $N\times N$ described by a 2D SLP of size $g$ such that any 2D SLP describing the same string of depth $\mathcal{O}(\log N)$ must be of size $Ω(g\cdot N/\log^{3}N)$. We complement this with an upper bound showing how to construct such a 2D SLP of size $\mathcal{O}(g\cdot N)$. Next, we observe that one can naturally define a generalization of 2D SLP, which we call 2D SLP with holes. We show that a known general balancing theorem by [Ganardi, Jeż, Lohrey, JACM 2021] immediately implies that, given a 2D SLP of size $g$ deriving a string of size $N\times N$, we can construct a 2D SLP with holes of depth $\mathcal{O}(\log N)$ and size $\mathcal{O}(g)$. This allows us to conclude that there is a structure of size $\mathcal{O}(g)$ providing random access in $\mathcal{O}(\log N)$ time for such a 2D SLP. Further, this can be extended (analogously as for a 1D SLP) to obtain a structure of size $\mathcal{O}(g \log^εN)$ providing random access in $\mathcal{O}(\log N/\log \log N)$ time, for any $ε>0$.
TTSnap: Test-Time Scaling of Diffusion Models via Noise-Aware Pruning
Venue: arXiv cs.CV 2025
Year: 2025

A prominent approach to test-time scaling for text-to-image diffusion models formulates the problem as a search over multiple noise seeds, selecting the one that maximizes a certain image-reward function. The effectiveness of this strategy heavily depends on the number and diversity of noise seeds explored. However, verifying each candidate is computationally expensive, because each must be fully denoised before a reward can be computed. This severely limits the number of samples that can be explored under a fixed budget. We propose test-time scaling with noise-aware pruning (TTSnap), a framework that prunes low-quality candidates without fully denoising them. The key challenge is that reward models are learned in the clean image domain, and the ranking of rewards predicted for intermediate estimates are often inconsistent with those predicted for clean images. To overcome this, we train noise-aware reward models via self-distillation to align the reward for intermediate estimates with that of the final clean images. To stabilize learning across different noise levels, we adopt a curriculum training strategy that progressively shifts the data domain from clean images to noise images. In addition, we introduce a new metric that measures reward alignment and computational budget utilization. Experiments demonstrate that our approach improves performance by over 16\% compared with existing methods, enabling more efficient and effective test-time scaling. It also provides orthogonal gains when combined with post-training techniques and local test-time optimization. Code: https://github.com/TerrysLearning/TTSnap/.
Toward Diffusible High-Dimensional Latent Spaces: A Frequency Perspective
Venue: arXiv cs.CV 2025
Year: 2025

Latent diffusion has become the default paradigm for visual generation, yet we observe a persistent reconstruction-generation trade-off as latent dimensionality increases: higher-capacity autoencoders improve reconstruction fidelity but generation quality eventually declines. We trace this gap to the different behaviors in high-frequency encoding and decoding. Through controlled perturbations in both RGB and latent domains, we analyze encoder/decoder behaviors and find that decoders depend strongly on high-frequency latent components to recover details, whereas encoders under-represent high-frequency contents, yielding insufficient exposure and underfitting in high-frequency bands for diffusion model training. To address this issue, we introduce FreqWarm, a plug-and-play frequency warm-up curriculum that increases early-stage exposure to high-frequency latent signals during diffusion or flow-matching training -- without modifying or retraining the autoencoder. Applied across several high-dimensional autoencoders, FreqWarm consistently improves generation quality: decreasing gFID by 14.11 on Wan2.2-VAE, 6.13 on LTX-VAE, and 4.42 on DC-AE-f32, while remaining architecture-agnostic and compatible with diverse backbones. Our study shows that explicitly managing frequency exposure can successfully turn high-dimensional latent spaces into more diffusible targets.
DriveVGGT: Visual Geometry Transformer for Autonomous Driving
Venue: arXiv cs.CV 2025
Year: 2025

Feed-forward reconstruction has recently gained significant attention, with VGGT being a notable example. However, directly applying VGGT to autonomous driving (AD) systems leads to sub-optimal results due to the different priors between the two tasks. In AD systems, several important new priors need to be considered: (i) The overlap between camera views is minimal, as autonomous driving sensor setups are designed to achieve coverage at a low cost. (ii) The camera intrinsics and extrinsics are known, which introduces more constraints on the output and also enables the estimation of absolute scale. (iii) Relative positions of all cameras remain fixed though the ego vehicle is in motion. To fully integrate these priors into a feed-forward framework, we propose DriveVGGT, a scale-aware 4D reconstruction framework specifically designed for autonomous driving data. Specifically, we propose a Temporal Video Attention (TVA) module to process multi-camera videos independently, which better leverages the spatiotemporal continuity within each single-camera sequence. Then, we propose a Multi-camera Consistency Attention (MCA) module to conduct window attention with normalized relative pose embeddings, aiming to establish consistency relationships across different cameras while restricting each token to attend only to nearby frames. Finally, we extend the standard VGGT heads by adding an absolute scale head and an ego vehicle pose head. Experiments show that DriveVGGT outperforms VGGT, StreamVGGT, fastVGGT on autonomous driving dataset while extensive ablation studies verify effectiveness of the proposed designs.
Aquas: Enhancing Domain Specialization through Holistic Hardware-Software Co-Optimization based on MLIR
Venue: arXiv cs.AR 2025
Year: 2025

Application-Specific Instruction-Set Processors (ASIPs) built on the RISC-V architecture offer specialization opportunities for various applications. However, existing frameworks from the open-source RISC-V ecosystem suffer from limited performance due to restricted hardware synthesis and rigid compiler support. To address these challenges, we introduce Aquas, a holistic hardware-software co-design framework built upon MLIR. Aquas enhances ASIP synthesis with fast memory access capability via a burst DMA engine and advanced high-level synthesis (HLS) optimizations. On the compiler side, we propose an e-graph based retargetable approach with a novel matching engine for efficient instruction matching. Evaluation demonstrates up to 9.27x speedup on real-world workloads, including point cloud processing and LLM inference.
RecToM: A Benchmark for Evaluating Machine Theory of Mind in LLM-based Conversational Recommender Systems
Venue: arXiv cs.AI 2025
Year: 2025

Large Language models are revolutionizing the conversational recommender systems through their impressive capabilities in instruction comprehension, reasoning, and human interaction. A core factor underlying effective recommendation dialogue is the ability to infer and reason about users' mental states (such as desire, intention, and belief), a cognitive capacity commonly referred to as Theory of Mind. Despite growing interest in evaluating ToM in LLMs, current benchmarks predominantly rely on synthetic narratives inspired by Sally-Anne test, which emphasize physical perception and fail to capture the complexity of mental state inference in realistic conversational settings. Moreover, existing benchmarks often overlook a critical component of human ToM: behavioral prediction, the ability to use inferred mental states to guide strategic decision-making and select appropriate conversational actions for future interactions. To better align LLM-based ToM evaluation with human-like social reasoning, we propose RecToM, a novel benchmark for evaluating ToM abilities in recommendation dialogues. RecToM focuses on two complementary dimensions: Cognitive Inference and Behavioral Prediction. The former focus on understanding what has been communicated by inferring the underlying mental states. The latter emphasizes what should be done next, evaluating whether LLMs can leverage these inferred mental states to predict, select, and assess appropriate dialogue strategies. Extensive experiments on state-of-the-art LLMs demonstrate that RecToM poses a significant challenge. While the models exhibit partial competence in recognizing mental states, they struggle to maintain coherent, strategic ToM reasoning throughout dynamic recommendation dialogues, particularly in tracking evolving intentions and aligning conversational strategies with inferred mental states.
The Collapse of Patches
Venue: arXiv cs.CV 2025
Year: 2025

Observing certain patches in an image reduces the uncertainty of others. Their realization lowers the distribution entropy of each remaining patch feature, analogous to collapsing a particle's wave function in quantum mechanics. This phenomenon can intuitively be called patch collapse. To identify which patches are most relied on during a target region's collapse, we learn an autoencoder that softly selects a subset of patches to reconstruct each target patch. Graphing these learned dependencies for each patch's PageRank score reveals the optimal patch order to realize an image. We show that respecting this order benefits various masked image modeling methods. First, autoregressive image generation can be boosted by retraining the state-of-the-art model MAR. Next, we introduce a new setup for image classification by exposing Vision Transformers only to high-rank patches in the collapse order. Seeing 22\% of such patches is sufficient to achieve high accuracy. With these experiments, we propose patch collapse as a novel image modeling perspective that promotes vision efficiency. Our project is available at https://github.com/wguo-ai/CoP .
Swarms of Large Language Model Agents for Protein Sequence Design with Experimental Validation
Venue: arXiv cs.AI 2025
Year: 2025

Designing proteins de novo with tailored structural, physicochemical, and functional properties remains a grand challenge in biotechnology, medicine, and materials science, due to the vastness of sequence space and the complex coupling between sequence, structure, and function. Current state-of-the-art generative methods, such as protein language models (PLMs) and diffusion-based architectures, often require extensive fine-tuning, task-specific data, or model reconfiguration to support objective-directed design, thereby limiting their flexibility and scalability. To overcome these limitations, we present a decentralized, agent-based framework inspired by swarm intelligence for de novo protein design. In this approach, multiple large language model (LLM) agents operate in parallel, each assigned to a specific residue position. These agents iteratively propose context-aware mutations by integrating design objectives, local neighborhood interactions, and memory and feedback from previous iterations. This position-wise, decentralized coordination enables emergent design of diverse, well-defined sequences without reliance on motif scaffolds or multiple sequence alignments, validated with experiments on proteins with alpha helix and coil structures. Through analyses of residue conservation, structure-based metrics, and sequence convergence and embeddings, we demonstrate that the framework exhibits emergent behaviors and effective navigation of the protein fitness landscape. Our method achieves efficient, objective-directed designs within a few GPU-hours and operates entirely without fine-tuning or specialized training, offering a generalizable and adaptable solution for protein design. Beyond proteins, the approach lays the groundwork for collective LLM-driven design across biomolecular systems and other scientific discovery tasks.
Named Entity Recognition for the Kurdish Sorani Language: Dataset Creation and Comparative Analysis
Venue: arXiv cs.CL 2025
Year: 2025

This work contributes towards balancing the inclusivity and global applicability of natural language processing techniques by proposing the first 'name entity recognition' dataset for Kurdish Sorani, a low-resource and under-represented language, that consists of 64,563 annotated tokens. It also provides a tool for facilitating this task in this and many other languages and performs a thorough comparative analysis, including classic machine learning models and neural systems. The results obtained challenge established assumptions about the advantage of neural approaches within the context of NLP. Conventional methods, in particular CRF, obtain F1-scores of 0.825, outperforming the results of BiLSTM-based models (0.706) significantly. These findings indicate that simpler and more computationally efficient classical frameworks can outperform neural architectures in low-resource settings.
Tracing Footsteps of Similar Cities: Modeling Urban Economic Vitality with Dynamic Inter-City Graph Embeddings
Venue: arXiv cs.AI 2025
Year: 2025

Urban economic vitality is a crucial indicator of a city's long-term growth potential, comprising key metrics such as the annual number of new companies and the population employed. However, modeling urban economic vitality remains challenging. This study develops ECO-GROW, a multi-graph framework modeling China's inter-city networks (2005-2021) to generate urban embeddings that model urban economic vitality. Traditional approaches relying on static city-level aggregates fail to capture a fundamental dynamic: the developmental trajectory of one city today may mirror that of its structurally similar counterparts tomorrow. ECO-GROW overcomes this limitation by integrating industrial linkages, POI similarities, migration similarities and temporal network evolution over 15 years. The framework combines a Dynamic Top-K GCN to adaptively select influential inter-city connections and an adaptive Graph Scorer mechanism to dynamically weight cross-regional impacts. Additionally, the model incorporates a link prediction task based on Barabasi Proximity, optimizing the graph representation. Experimental results demonstrate ECO-GROW's superior accuracy in predicting entrepreneurial activities and employment trends compared to conventional models. By open-sourcing our code, we enable government agencies and public sector organizations to leverage big data analytics for evidence-based urban planning, economic policy formulation, and resource allocation decisions that benefit society at large.
Prompt-based Consistent Video Colorization
Venue: arXiv cs.CV 2025
Year: 2025

Existing video colorization methods struggle with temporal flickering or demand extensive manual input. We propose a novel approach automating high-fidelity video colorization using rich semantic guidance derived from language and segmentation. We employ a language-conditioned diffusion model to colorize grayscale frames. Guidance is provided via automatically generated object masks and textual prompts; our primary automatic method uses a generic prompt, achieving state-of-the-art results without specific color input. Temporal stability is achieved by warping color information from previous frames using optical flow (RAFT); a correction step detects and fixes inconsistencies introduced by warping. Evaluations on standard benchmarks (DAVIS30, VIDEVO20) show our method achieves state-of-the-art performance in colorization accuracy (PSNR) and visual realism (Colorfulness, CDC), demonstrating the efficacy of automated prompt-based guidance for consistent video colorization.
Keyless Entry: Breaking and Entering eMMC RPMB with EMFI
Venue: arXiv cs.CR 2025
Year: 2025

The Replay Protected Memory Block (RPMB) in modern storage systems provides a secure area where data integrity is ensured by authentication. This block is used in digital devices to store pivotal information that must be safeguarded against modification by potential attackers. This paper targets the authentication scheme of the RPMB in three different eMMCs from a major manufacturer. A glitch was injected by sending an electromagnetic pulse to the target chip. RPMB authentication was successfully glitched and the information stored in two target eMMCs was overwritten with arbitrary data, without affecting the integrity of other data.
Test Time Training for AC Power Flow Surrogates via Physics and Operational Constraint Refinement
Venue: arXiv cs.LG 2025
Year: 2025

Power Flow (PF) calculation based on machine learning (ML) techniques offer significant computational advantages over traditional numerical methods but often struggle to maintain full physical consistency. This paper introduces a physics-informed test-time training (PI-TTT) framework that enhances the accuracy and feasibility of ML-based PF surrogates by enforcing AC power flow equalities and operational constraints directly at inference time. The proposed method performs a lightweight self-supervised refinement of the surrogate outputs through few gradient-based updates, enabling local adaptation to unseen operating conditions without requiring labeled data. Extensive experiments on the IEEE 14-, 118-, and 300-bus systems and the PEGASE 1354-bus network show that PI-TTT reduces power flow residuals and operational constraint violations by one to two orders of magnitude compared with purely ML-based models, while preserving their computational advantage. The results demonstrate that PI-TTT provides fast, accurate, and physically reliable predictions, representing a promising direction for scalable and physics-consistent learning in power system analysis.
Flowing Backwards: Improving Normalizing Flows via Reverse Representation Alignment
Venue: arXiv cs.CV 2025
Year: 2025

Normalizing Flows (NFs) are a class of generative models distinguished by a mathematically invertible architecture, where the forward pass transforms data into a latent space for density estimation, and the reverse pass generates new samples from this space. This characteristic creates an intrinsic synergy between representation learning and data generation. However, the generative quality of standard NFs is limited by poor semantic representations from log-likelihood optimization. To remedy this, we propose a novel alignment strategy that creatively leverages the invertibility of NFs: instead of regularizing the forward pass, we align the intermediate features of the generative (reverse) pass with representations from a powerful vision foundation model, demonstrating superior effectiveness over naive alignment. We also introduce a novel training-free, test-time optimization algorithm for classification, which provides a more intrinsic evaluation of the NF's embedded semantic knowledge. Comprehensive experiments demonstrate that our approach accelerates the training of NFs by over 3.3$\times$, while simultaneously delivering significant improvements in both generative quality and classification accuracy. New state-of-the-art results for NFs are established on ImageNet 64$\times$64 and 256$\times$256. Our code is available at https://github.com/MCG-NJU/FlowBack.