OpenRaiser/Intern-Atlas huggingface dataset:
Comparing Methods for the Cross-Level Verification of SystemC Peripherals with Symbolic Execution
Venue: arXiv cs.PL 2025
Year: 2025

Virtual Prototypes (VPs) are important tools in modern hardware development. At high abstractions, they are often implemented in SystemC and offer early analysis of increasingly complex designs. These complex designs often combine one or more processors, interconnects, and peripherals to perform tasks in hardware or interact with the environment. Verifying these subsystems is a well-suited task for VPs, as they allow reasoning across different abstraction levels. While modern verification techniques like symbolic execution can be seamlessly integrated into VP-based workflows, they require modifications in the SystemC kernel. Hence, existing approaches modify and replace the SystemC kernel, or ignore the opportunity of cross-level scenarios completely, and would not allow focusing on special challenges of particular subsystems like peripherals. We propose CrosSym and SEFOS, two opposing approaches for a versatile symbolic execution of peripherals. CrosSym modifies the SystemC kernel, while SEFOS instead modifies a modern symbolic execution engine. Our extensive evaluation applies our tools to various peripherals on different levels of abstractions. Both tools' extensive sets of features are demonstrated for (1) different verification scenarios, and (2) identifying 300+ mutants. In comparison with each other, SEFOS convinces with the unmodified SystemC kernel and peripheral, while CrosSym offers slightly better runtime and memory usage. In comparison to the state-of-the-art, that is limited to Transaction Level Modelling (TLM), our tools offered comparable runtime, while enabling cross-level verification with symbolic execution.
OpenEgo: A Large-Scale Multimodal Egocentric Dataset for Dexterous Manipulation
Venue: arXiv cs.CV 2025
Year: 2025

Egocentric human videos provide scalable demonstrations for imitation learning, but existing corpora often lack either fine-grained, temporally localized action descriptions or dexterous hand annotations. We introduce OpenEgo, a multimodal egocentric manipulation dataset with standardized hand-pose annotations and intention-aligned action primitives. OpenEgo totals 1107 hours across six public datasets, covering 290 manipulation tasks in 600+ environments. We unify hand-pose layouts and provide descriptive, timestamped action primitives. To validate its utility, we train language-conditioned imitation-learning policies to predict dexterous hand trajectories. OpenEgo is designed to lower the barrier to learning dexterous manipulation from egocentric video and to support reproducible research in vision-language-action learning. All resources and instructions will be released at www.openegocentric.com.
TreeGPT: Pure TreeFFN Encoder-Decoder Architecture for Structured Reasoning Without Attention Mechanisms
Venue: arXiv cs.AI 2025
Year: 2025

We present TreeGPT, an attention-free neural architecture that explores the potential of pure TreeFFN encoder-decoder design for structured reasoning tasks. Unlike traditional transformer approaches that rely on attention mechanisms, TreeGPT employs bidirectional TreeFFN components that process sequences through adjacent connections in parallel, aiming to achieve computational efficiency while maintaining reasoning capabilities. Our approach centers on a TreeFFN Encoder-Decoder mechanism: $$\text{Encoder TreeFFN (L} \rightarrow \text{R)} + \text{Decoder TreeFFN (R} \leftarrow \text{L)} \rightarrow \text{Parallel Processing}$$ where the encoder processes left-to-right dependencies while the decoder handles right-to-left patterns, both using simple neighbor-to-neighbor connections. This design eliminates attention computation while maintaining sequence modeling capabilities. We evaluate our approach on the ARC Prize 2025 dataset, where TreeGPT achieves 99\% validation accuracy using 3.16M parameters. The model converges within 1500 training steps and demonstrates 100\% token-level accuracy on selected evaluation samples. Our preliminary results suggest that for certain structured reasoning tasks, specialized TreeFFN architectures may offer advantages over attention-based approaches. While these findings are encouraging, we acknowledge that further investigation across diverse tasks and datasets would be valuable to establish the broader applicability of attention-free designs.
Secure and Efficient $L^p$-Norm Computation for Two-Party Learning Applications
Venue: arXiv cs.CR 2025
Year: 2025

Secure norm computation is becoming increasingly important in many real-world learning applications. However, existing cryptographic systems often lack a general framework for securely computing the $L^p$-norm over private inputs held by different parties. These systems often treat secure norm computation as a black-box process, neglecting to design tailored cryptographic protocols that optimize performance. Moreover, they predominantly focus on the $L^2$-norm, paying little attention to other popular $L^p$-norms, such as $L^1$ and $L^\infty$, which are commonly used in practice, such as machine learning tasks and location-based services. To our best knowledge, we propose the first comprehensive framework for secure two-party $L^p$-norm computations ($L^1$, $L^2$, and $L^\infty$), denoted as \mbox{Crypto-$L^p$}, designed to be versatile across various applications. We have designed, implemented, and thoroughly evaluated our framework across a wide range of benchmarking applications, state-of-the-art (SOTA) cryptographic protocols, and real-world datasets to validate its effectiveness and practical applicability. In summary, \mbox{Crypto-$L^p$} outperforms prior works on secure $L^p$-norm computation, achieving $82\times$, $271\times$, and $42\times$ improvements in runtime while reducing communication overhead by $36\times$, $4\times$, and $21\times$ for $p=1$, $2$, and $\infty$, respectively. Furthermore, we take the first step in adapting our Crypto-$L^p$ framework for secure machine learning inference, reducing communication costs by $3\times$ compared to SOTA systems while maintaining comparable runtime and accuracy.
Ad hoc conventions generalize to new referents
Venue: arXiv cs.CL 2025
Year: 2025

How do people talk about things they've never talked about before? One view suggests that a new shared naming system establishes an arbitrary link to a specific target, like proper names that cannot extend beyond their bearers. An alternative view proposes that forming a shared way of describing objects involves broader conceptual alignment, reshaping each individual's semantic space in ways that should generalize to new referents. We test these competing accounts in a dyadic communication study (N=302) leveraging the recently-released KiloGram dataset containing over 1,000 abstract tangram images. After pairs of participants coordinated on referential conventions for one set of images through repeated communication, we measured the extent to which their descriptions aligned for undiscussed images. We found strong evidence for generalization: partners showed increased alignment relative to their pre-test labels. Generalization also decayed nonlinearly with visual similarity (consistent with Shepard's law) and was robust across levels of the images' nameability. These findings suggest that ad hoc conventions are not arbitrary labels but reflect genuine conceptual coordination, with implications for theories of reference and the design of more adaptive language agents.
OccVLA: Vision-Language-Action Model with Implicit 3D Occupancy Supervision
Venue: arXiv cs.AI 2025
Year: 2025

Multimodal large language models (MLLMs) have shown strong vision-language reasoning abilities but still lack robust 3D spatial understanding, which is critical for autonomous driving. This limitation stems from two key challenges: (1) the difficulty of constructing accessible yet effective 3D representations without expensive manual annotations, and (2) the loss of fine-grained spatial details in VLMs due to the absence of large-scale 3D vision-language pretraining. To address these challenges, we propose OccVLA, a novel framework that integrates 3D occupancy representations into a unified multimodal reasoning process. Unlike prior approaches that rely on explicit 3D inputs, OccVLA treats dense 3D occupancy as both a predictive output and a supervisory signal, enabling the model to learn fine-grained spatial structures directly from 2D visual inputs. The occupancy predictions are regarded as implicit reasoning processes and can be skipped during inference without performance degradation, thereby adding no extra computational overhead. OccVLA achieves state-of-the-art results on the nuScenes benchmark for trajectory planning and demonstrates superior performance on 3D visual question-answering tasks, offering a scalable, interpretable, and fully vision-based solution for autonomous driving.
Reconstruction and Reenactment Separated Method for Realistic Gaussian Head
Venue: arXiv cs.CV 2025
Year: 2025

In this paper, we explore a reconstruction and reenactment separated framework for 3D Gaussians head, which requires only a single portrait image as input to generate controllable avatar. Specifically, we developed a large-scale one-shot gaussian head generator built upon WebSSL and employed a two-stage training approach that significantly enhances the capabilities of generalization and high-frequency texture reconstruction. During inference, an ultra-lightweight gaussian avatar driven by control signals enables high frame-rate rendering, achieving 90 FPS at a resolution of 512x512. We further demonstrate that the proposed framework follows the scaling law, whereby increasing the parameter scale of the reconstruction module leads to improved performance. Moreover, thanks to the separation design, driving efficiency remains unaffected. Finally, extensive quantitative and qualitative experiments validate that our approach outperforms current state-of-the-art methods.
MFFI: Multi-Dimensional Face Forgery Image Dataset for Real-World Scenarios
Venue: arXiv cs.CV 2025
Year: 2025

Rapid advances in Artificial Intelligence Generated Content (AIGC) have enabled increasingly sophisticated face forgeries, posing a significant threat to social security. However, current Deepfake detection methods are limited by constraints in existing datasets, which lack the diversity necessary in real-world scenarios. Specifically, these data sets fall short in four key areas: unknown of advanced forgery techniques, variability of facial scenes, richness of real data, and degradation of real-world propagation. To address these challenges, we propose the Multi-dimensional Face Forgery Image (\textbf{MFFI}) dataset, tailored for real-world scenarios. MFFI enhances realism based on four strategic dimensions: 1) Wider Forgery Methods; 2) Varied Facial Scenes; 3) Diversified Authentic Data; 4) Multi-level Degradation Operations. MFFI integrates $50$ different forgery methods and contains $1024K$ image samples. Benchmark evaluations show that MFFI outperforms existing public datasets in terms of scene complexity, cross-domain generalization capability, and detection difficulty gradients. These results validate the technical advance and practical utility of MFFI in simulating real-world conditions. The dataset and additional details are publicly available at {https://github.com/inclusionConf/MFFI}.
PaMO: Parallel Mesh Optimization for Intersection-Free Low-Poly Modeling on the GPU
Venue: arXiv cs.GR 2025
Year: 2025

Reducing the triangle count in complex 3D models is a basic geometry preprocessing step in graphics pipelines such as efficient rendering and interactive editing. However, most existing mesh simplification methods exhibit a few issues. Firstly, they often lead to self-intersections during decimation, a major issue for applications such as 3D printing and soft-body simulation. Second, to perform simplification on a mesh in the wild, one would first need to perform re-meshing, which often suffers from surface shifts and losses of sharp features. Finally, existing re-meshing and simplification methods can take minutes when processing large-scale meshes, limiting their applications in practice. To address the challenges, we introduce a novel GPU-based mesh optimization approach containing three key components: (1) a parallel re-meshing algorithm to turn meshes in the wild into watertight, manifold, and intersection-free ones, and reduce the prevalence of poorly shaped triangles; (2) a robust parallel simplification algorithm with intersection-free guarantees; (3) an optimization-based safe projection algorithm to realign the simplified mesh with the input, eliminating the surface shift introduced by re-meshing and recovering the original sharp features. The algorithm demonstrates remarkable efficiency, simplifying a 2-million-face mesh to 20k triangles in 3 seconds on RTX4090. We evaluated the approach on the Thingi10K dataset and showcased its exceptional performance in geometry preservation and speed.
Causal Debiasing Medical Multimodal Representation Learning with Missing Modalities
Venue: arXiv cs.LG 2025
Year: 2025

Medical multimodal representation learning aims to integrate heterogeneous clinical data into unified patient representations to support predictive modeling, which remains an essential yet challenging task in the medical data mining community. However, real-world medical datasets often suffer from missing modalities due to cost, protocol, or patient-specific constraints. Existing methods primarily address this issue by learning from the available observations in either the raw data space or feature space, but typically neglect the underlying bias introduced by the data acquisition process itself. In this work, we identify two types of biases that hinder model generalization: missingness bias, which results from non-random patterns in modality availability, and distribution bias, which arises from latent confounders that influence both observed features and outcomes. To address these challenges, we perform a structural causal analysis of the data-generating process and propose a unified framework that is compatible with existing direct prediction-based multimodal learning methods. Our method consists of two key components: (1) a missingness deconfounding module that approximates causal intervention based on backdoor adjustment and (2) a dual-branch neural network that explicitly disentangles causal features from spurious correlations. We evaluated our method in real-world public and in-hospital datasets, demonstrating its effectiveness and causal insights.
GestoBrush: Facilitating Graffiti Artists' Digital Creation Experiences through Embodied AR Interactions
Venue: arXiv cs.HC 2025
Year: 2025

Graffiti has long documented the socio-cultural landscapes of urban spaces, yet increasing global regulations have constrained artists' creative freedom, prompting exploration of digital alternatives. Augmented Reality (AR) offers opportunities to extend graffiti into digital environments while retaining spatial and cultural significance, but prior research has largely centered on audience engagement rather than the embodied creative processes of graffiti artists. To address this, we developed GestoBrush, a mobile AR prototype that turns smartphones into virtual spray cans, enabling graffiti creation through embodied gestures. A co-design workshop underscored the role of embodiment-physical engagement with surroundings and body-driven creative processes-in digital workflows. We evaluated GestoBrush with six graffiti artists and findings suggested that embodied AR interactions supporting artists bypass real-world constraints and explore new artistic possibilities, whose AR artworks created enhanced senses of intuitiveness, immersion, and expressiveness. This work highlight how embodied AR tools can bridge the gap between physical graffiti practice and digital expression, suggesting pathways for designing immersive creative systems that respect the cultural ethos of street art while expanding its possibilities in virtual spaces.
Self-supervised Learning for Hyperspectral Images of Trees
Venue: arXiv cs.CV 2025
Year: 2025

Aerial remote sensing using multispectral and RGB imagers has provided a critical impetus to precision agriculture. Analysis of the hyperspectral images with limited or no labels is challenging. This paper focuses on self-supervised learning to create neural network embeddings reflecting vegetation properties of trees from aerial hyperspectral images of crop fields. Experimental results demonstrate that a constructed tree representation, using a vegetation property-related embedding space, performs better in downstream machine learning tasks compared to the direct use of hyperspectral vegetation properties as tree representations.
Orchestrator: Active Inference for Multi-Agent Systems in Long-Horizon Tasks
Venue: arXiv cs.MA 2025
Year: 2025

Complex, non-linear tasks challenge LLM-enhanced multi-agent systems (MAS) due to partial observability and suboptimal coordination. We propose Orchestrator, a novel MAS framework that leverages attention-inspired self-emergent coordination and reflective benchmarking to optimize global task performance. Orchestrator introduces a monitoring mechanism to track agent-environment dynamics, using active inference benchmarks to optimize system behavior. By tracking agent-to-agent and agent-to-environment interaction, Orchestrator mitigates the effects of partial observability and enables agents to approximate global task solutions more efficiently. We evaluate the framework on a series of maze puzzles of increasing complexity, demonstrating its effectiveness in enhancing coordination and performance in dynamic, non-linear environments with long-horizon objectives.
OptiProxy-NAS: Optimization Proxy based End-to-End Neural Architecture Search
Venue: arXiv cs.LG 2025
Year: 2025

Neural architecture search (NAS) is a hard computationally expensive optimization problem with a discrete, vast, and spiky search space. One of the key research efforts dedicated to this space focuses on accelerating NAS via certain proxy evaluations of neural architectures. Different from the prevalent predictor-based methods using surrogate models and differentiable architecture search via supernetworks, we propose an optimization proxy to streamline the NAS as an end-to-end optimization framework, named OptiProxy-NAS. In particular, using a proxy representation, the NAS space is reformulated to be continuous, differentiable, and smooth. Thereby, any differentiable optimization method can be applied to the gradient-based search of the relaxed architecture parameters. Our comprehensive experiments on $12$ NAS tasks of $4$ search spaces across three different domains including computer vision, natural language processing, and resource-constrained NAS fully demonstrate the superior search results and efficiency. Further experiments on low-fidelity scenarios verify the flexibility.
Cross-Question Method Reuse in Large Language Models: From Word-Level Prediction to Rational Logical-Layer Reasoning
Venue: arXiv cs.CL 2025
Year: 2025

Large language models (LLMs) have been widely applied to assist in finding solutions for diverse questions. Prior work has proposed representing a method as a pair of a question and its corresponding solution, enabling method reuse. However, existing approaches typically require the questions to be highly similar. In this paper, we extend the scope of method reuse to address questions with low similarity or with hidden similarities that are not explicitly observable. For questions that are similar in a general-specific sense (i.e., broader or narrower in scope), we propose to first separate the question and solution, rather than directly feeding the pair to the LLM. The LLM is then guided to adapt the solution to new but related questions, allowing it to focus on solution transfer rather than question recognition. Furthermore, we extend this approach to cases where questions only share partial features or hidden characteristics. This enables cross-question method reuse beyond conventional similarity constraints. Experimental verification shows that our scope-extension approach increases the probability of filtering out reusable solutions, thereby improving the effectiveness of cross-question method reuse.
Do Vision-Language Models See Visualizations Like Humans? Alignment in Chart Categorization
Venue: arXiv cs.HC 2025
Year: 2025

Vision-language models (VLMs) hold promise for enhancing visualization tools, but effective human-AI collaboration hinges on a shared perceptual understanding of visual content. Prior studies assessed VLM visualization literacy through interpretive tasks, revealing an over-reliance on textual cues rather than genuine visual analysis. Our study investigates a more foundational skill underpinning such literacy: the ability of VLMs to recognize a chart's core visual properties as humans do. We task 13 diverse VLMs with classifying scientific visualizations based solely on visual stimuli, according to three criteria: purpose (e.g., schematic, GUI, visualization), encoding (e.g., bar, point, node-link), and dimensionality (e.g., 2D, 3D). Using expert labels from the human-centric VisType typology as ground truth, we find that VLMs often identify purpose and dimensionality accurately but struggle with specific encoding types. Our preliminary results show that larger models do not always equate to superior performance and highlight the need for careful integration of VLMs in visualization tasks, with human supervision to ensure reliable outcomes.
Simulation Priors for Data-Efficient Deep Learning
Venue: arXiv cs.LG 2025
Year: 2025

How do we enable AI systems to efficiently learn in the real-world? First-principles models are widely used to simulate natural systems, but often fail to capture real-world complexity due to simplifying assumptions. In contrast, deep learning approaches can estimate complex dynamics with minimal assumptions but require large, representative datasets. We propose SimPEL, a method that efficiently combines first-principles models with data-driven learning by using low-fidelity simulators as priors in Bayesian deep learning. This enables SimPEL to benefit from simulator knowledge in low-data regimes and leverage deep learning's flexibility when more data is available, all the while carefully quantifying epistemic uncertainty. We evaluate SimPEL on diverse systems, including biological, agricultural, and robotic domains, showing superior performance in learning complex dynamics. For decision-making, we demonstrate that SimPEL bridges the sim-to-real gap in model-based reinforcement learning. On a high-speed RC car task, SimPEL learns a highly dynamic parking maneuver involving drifting with substantially less data than state-of-the-art baselines. These results highlight the potential of SimPEL for data-efficient learning and control in complex real-world environments.
Automating API Documentation with LLMs: A BERTopic Approach
Venue: arXiv cs.SE 2025
Year: 2025

Developers rely on API documentation, but official sources are often lengthy, complex, or incomplete. Many turn to community-driven forums like Stack Overflow for practical insights. We propose automating the summarization of informal sources, focusing on Android APIs. Using BERTopic, we extracted prevalent topics from 3.6 million Stack Overflow posts and applied extractive summarization techniques to generate concise summaries, including code snippets. A user study with 30 Android developers assessed the summaries for coherence, relevance, informativeness, and satisfaction, showing improved productivity. Integrating formal API knowledge with community-generated content enhances documentation, making API resources more accessible and actionable work.