OpenRaiser/Intern-Atlas huggingface dataset:
MZEN: Multi-Zoom Enhanced NeRF for 3-D Reconstruction with Unknown Camera Poses
Venue: arXiv cs.CV 2025
Year: 2025

Neural Radiance Fields (NeRF) methods excel at 3D reconstruction from multiple 2D images, even those taken with unknown camera poses. However, they still miss the fine-detailed structures that matter in industrial inspection, e.g., detecting sub-micron defects on a production line or analyzing chips with Scanning Electron Microscopy (SEM). In these scenarios, the sensor resolution is fixed and compute budgets are tight, so the only way to expose fine structure is to add zoom-in images; yet, this breaks the multi-view consistency that pose-free NeRF training relies on. We propose Multi-Zoom Enhanced NeRF (MZEN), the first NeRF framework that natively handles multi-zoom image sets. MZEN (i) augments the pin-hole camera model with an explicit, learnable zoom scalar that scales the focal length, and (ii) introduces a novel pose strategy: wide-field images are solved first to establish a global metric frame, and zoom-in images are then pose-primed to the nearest wide-field counterpart via a zoom-consistent crop-and-match procedure before joint refinement. Across eight forward-facing scenes$\unicode{x2013}$synthetic TCAD models, real SEM of micro-structures, and BLEFF objects$\unicode{x2013}$MZEN consistently outperforms pose-free baselines and even high-resolution variants, boosting PSNR by up to $28 \%$, SSIM by $10 \%$, and reducing LPIPS by up to $222 \%$. MZEN, therefore, extends NeRF to real-world factory settings, preserving global accuracy while capturing the micron-level details essential for industrial inspection.
"Mirror" Language AI Models of Depression are Criterion-Contaminated
Venue: arXiv cs.CL 2025
Year: 2025

Recent studies show near-perfect language-based predictions of depression scores (R2 = .70), but these "Mirror" models rely on language responses directly from depression assessments to predict depression assessment scores. These methods suffer from criterion contamination that inflate prediction estimates. We compare "Mirror" models to "Non-Mirror" models, which use other external language to predict depression scores. 110 participants completed both structured diagnostic (Mirror condition) and life history (Non-Mirror condition) interviews. LLMs were prompted to predict diagnostic depression scores. As expected, Mirror models were near-perfect. However, Non-Mirror models also displayed prediction sizes considered large in psychology. Further, both Mirror and Non-Mirror predictions correlated with other questionnaire-based depression symptoms at similar sizes, suggesting bias in Mirror models. Topic modeling revealed different theme structures across model types. As language models for depression continue to evolve, incorporating Non-Mirror approaches may support more valid and clinically useful language-based AI applications in psychological assessment.
VISTA: Vision-Language Imitation of Situational Thinking and Attention for Human-Like Driver Focus in Dynamic Environments
Venue: arXiv cs.CV 2025
Year: 2025

Driver visual attention prediction is a critical task in autonomous driving and human-computer interaction (HCI) research. Most prior studies focus on estimating attention allocation at a single moment in time, typically using static RGB images such as driving scene pictures. In this work, we propose a vision-language framework that models the changing landscape of drivers' gaze through natural language, using few-shot and zero-shot learning on single RGB images. We curate and refine high-quality captions from the BDD-A dataset using human-in-the-loop feedback, then fine-tune LLaVA to align visual perception with attention-centric scene understanding. Our approach integrates both low-level cues and top-down context (e.g., route semantics, risk anticipation), enabling language-based descriptions of gaze behavior. We evaluate performance across training regimes (few shot, and one-shot) and introduce domain-specific metrics for semantic alignment and response diversity. Results show that our fine-tuned model outperforms general-purpose VLMs in attention shift detection and interpretability. To our knowledge, this is among the first attempts to generate driver visual attention allocation and shifting predictions in natural language, offering a new direction for explainable AI in autonomous driving. Our approach provides a foundation for downstream tasks such as behavior forecasting, human-AI teaming, and multi-agent coordination.
The Memory Wars: AI Memory, Network Effects, and the Geopolitics of Cognitive Sovereignty
Venue: arXiv cs.CY 2025
Year: 2025

The advent of continuously learning Artificial Intelligence (AI) assistants marks a paradigm shift from episodic interactions to persistent, memory-driven relationships. This paper introduces the concept of "Cognitive Sovereignty", the ability of individuals, groups, and nations to maintain autonomous thought and preserve identity in the age of powerful AI systems, especially those that hold their deep personal memory. It argues that the primary risk of these technologies transcends traditional data privacy to become an issue of cognitive and geopolitical control. We propose "Network Effect 2.0," a model where value scales with the depth of personalized memory, creating powerful cognitive moats and unprecedented user lock-in. We analyze the psychological risks of such systems, including cognitive offloading and identity dependency, by drawing on the "extended mind" thesis. These individual-level risks scale to geopolitical threats, such as a new form of digital colonialism and subtle shifting of public discourse. To counter these threats, we propose a policy framework centered on memory portability, transparency, sovereign cognitive infrastructure, and strategic alliances. This work reframes the discourse on AI assistants in an era of increasingly intimate machines, pointing to challenges to individual and national sovereignty.
Energy Experience Design
Venue: arXiv cs.CY 2025
Year: 2025

The material footprint of information and communications technology (ICT) systems is both significant and growing, inspiring a variety of conversations around sustainability and climate justice. In part this effort has been catalysed by past scholarship and analysis from the LIMITS community. This paper examines energy storage systems for computing, particularly batteries -- which are discarded at the rate of 15 billion a year worldwide. The International Energy Agency (IEA) is now referring to the energy transition toward low carbon systems as a critical mineral problem, and countries are speaking openly of 'mineral security' in policy documents. In this paper I 1) present a definition for energy experience and what this means for the design and making of devices, interactions and experiences. I also 2) explore a series of electronics device prototypes converted to run from batteryless sustainable energy that are extremely long lasting, and make limited use of critical minerals. As transitional energy experience device-design experiments, what do prototypes like these suggest for more mainstream, mass-manufactured systems?
User-Intent-Driven Semantic Communication via Adaptive Deep Understanding
Venue: arXiv cs.IT 2025
Year: 2025

Semantic communication focuses on transmitting task-relevant semantic information, aiming for intent-oriented communication. While existing systems improve efficiency by extracting key semantics, they still fail to deeply understand and generalize users' real intentions. To overcome this, we propose a user-intention-driven semantic communication system that interprets diverse abstract intents. First, we integrate a multi-modal large model as semantic knowledge base to generate user-intention prior. Next, a mask-guided attention module is proposed to effectively highlight critical semantic regions. Further, a channel state awareness module ensures adaptive, robust transmission across varying channel conditions. Extensive experiments demonstrate that our system achieves deep intent understanding and outperforms DeepJSCC, e.g., under a Rayleigh channel at an SNR of 5 dB, it achieves improvements of 8%, 6%, and 19% in PSNR, SSIM, and LPIPS, respectively.
Planning Agents on an Ego-Trip: Leveraging Hybrid Ego-Graph Ensembles for Improved Tool Retrieval in Enterprise Task Planning
Venue: arXiv cs.AI 2025
Year: 2025

Effective tool pre-selection via retrieval is essential for AI agents to select from a vast array of tools when identifying and planning actions in the context of complex user queries. Despite its central role in planning, this aspect remains underexplored in the literature. Traditional approaches rely primarily on similarities between user queries and tool descriptions, which significantly limits retrieval accuracy, specifically when handling multi-step user requests. To address these limitations, we propose a Knowledge Graph (KG)-based tool retrieval framework that captures the semantic relationships between tools and their functional dependencies. Our retrieval algorithm leverages ensembles of 1-hop ego tool graphs to model direct and indirect connections between tools, enabling more comprehensive and contextual tool selection for multi-step tasks. We evaluate our approach on a synthetically generated internal dataset across six defined user classes, extending previous work on coherent dialogue synthesis and tool retrieval benchmarks. Results demonstrate that our tool graph-based method achieves 91.85% tool coverage on the micro-average CompleteRecall metric, compared to 89.26% for re-ranked semantic-lexical hybrid retrieval, the strongest non-KG baseline in our experiments. These findings support our hypothesis that the structural information modeled in the graph provides complementary signals to pure similarity matching, particularly for queries requiring sequential tool composition.
HOLODECK 2.0: Vision-Language-Guided 3D World Generation with Editing
Venue: arXiv cs.CV 2025
Year: 2025

3D scene generation plays a crucial role in gaming, artistic creation, virtual reality, and many other domains. However, current 3D scene design still relies heavily on extensive manual effort from creators, and existing automated methods struggle to generate open-domain scenes or support flexible editing. To address those challenges, we introduce HOLODECK 2.0, an advanced vision-language-guided framework for 3D world generation with support for interactive scene editing based on human feedback. HOLODECK 2.0 can generate diverse and stylistically rich 3D scenes (e.g., realistic, cartoon, anime, and cyberpunk styles) that exhibit high semantic fidelity to fine-grained input descriptions, suitable for both indoor and open-domain environments. HOLODECK 2.0 leverages vision-language models (VLMs) to identify and parse the objects required in a scene and generates corresponding high-quality assets via state-of-the-art 3D generative models. Then, HOLODECK 2.0 iteratively applies spatial constraints derived from the VLMs to achieve semantically coherent and physically plausible layouts. Both human and model evaluations demonstrate that HOLODECK 2.0 effectively generates high-quality scenes closely aligned with detailed textual descriptions, consistently outperforming baselines across indoor and open-domain scenarios. Additionally, HOLODECK 2.0 provides editing capabilities that flexibly adapt to human feedback, supporting layout refinement and style-consistent object edits. Finally, we present a practical application of HOLODECK 2.0 in procedural game modeling to generate visually rich and immersive environments that can boost efficiency in game design.
Dual Signal Decomposition of Stochastic Time Series
Venue: arXiv cs.LG 2025
Year: 2025

The decomposition of a stochastic time series into three component series representing a dual signal - namely, the mean and dispersion - while isolating noise is presented. The decomposition is performed by applying machine learning techniques to fit the dual signal. Machine learning minimizes the loss function which compromises between fitting the original time series and penalizing irregularities of the dual signal. The latter includes terms based on the first and second order derivatives along time. To preserve special patterns, weighting of the regularization components of the loss function has been introduced based on Statistical Process Control methodology. The proposed decomposition can be applied as a smoothing algorithm against the mean and dispersion of the time series. By isolating noise, the proposed decomposition can be seen as a denoising algorithm. Two approaches of the learning process have been considered: sequential and jointly. The former approach learns the mean signal first and then dispersion. The latter approach fits the dual signal jointly. Jointly learning can uncover complex relationships for the time series with heteroskedasticity. Learning has been set by solving the direct non-linear unconstrained optimization problem or by applying neural networks that have sequential or twin output architectures. Tuning of the loss function hyperparameters focuses on the isolated noise to be a stationary stochastic process without autocorrelation properties. Depending on the applications, the hyperparameters of the learning can be tuned towards either the discrete states by stepped signal or smoothed series. The decomposed dual signal can be represented on the 2D space and used to learn inherent structures, to forecast both mean and dispersion, or to analyze cross effects in case of multiple time series.
Debiasing Polynomial and Fourier Regression
Venue: arXiv cs.DS 2025
Year: 2025

We study the problem of approximating an unknown function $f:\mathbb{R}\to\mathbb{R}$ by a degree-$d$ polynomial using as few function evaluations as possible, where error is measured with respect to a probability distribution $μ$. Existing randomized algorithms achieve near-optimal sample complexities to recover a $ (1+\varepsilon) $-optimal polynomial but produce biased estimates of the best polynomial approximation, which is undesirable. We propose a simple debiasing method based on a connection between polynomial regression and random matrix theory. Our method involves evaluating $f(λ_1),\ldots,f(λ_{d+1})$ where $λ_1,\ldots,λ_{d+1}$ are the eigenvalues of a suitably designed random complex matrix tailored to the distribution $μ$. Our estimator is unbiased, has near-optimal sample complexity, and experimentally outperforms iid leverage score sampling. Additionally, our techniques enable us to debias existing methods for approximating a periodic function with a truncated Fourier series with near-optimal sample complexity.
Enhancing Software Vulnerability Detection Through Adaptive Test Input Generation Using Genetic Algorithm
Venue: arXiv cs.SE 2025
Year: 2025

Software vulnerabilities continue to undermine the reliability and security of modern systems, particularly as software complexity outpaces the capabilities of traditional detection methods. This study introduces a genetic algorithm-based method for test input generation that innovatively integrates genetic operators and adaptive learning to enhance software vulnerability detection. A key contribution is the application of the crossover operator, which facilitates exploration by searching across a broader space of potential test inputs. Complementing this, an adaptive feedback mechanism continuously learns from the system's execution behavior and dynamically guides input generation toward promising areas of the input space. Rather than relying on fixed or randomly selected inputs, the approach evolves a population of structurally valid test cases using feedback-driven selection, enabling deeper and more effective code traversal. This strategic integration of exploration and exploitation ensures that both diverse and targeted test inputs are developed over time. Evaluation was conducted across nine open-source JSON-processing libraries. The proposed method achieved substantial improvements in coverage compared to a benchmark evolutionary fuzzing method, with average gains of 39.8% in class coverage, 62.4% in method coverage, 105.0% in line coverage, 114.0% in instruction coverage, and 166.0% in branch coverage. These results highlight the method's capacity to detect deeper and more complex vulnerabilities, offering a scalable and adaptive solution to software security testing.
Modular Vacuum-Based Fixturing System for Adaptive Disassembly Workspace Integration
Venue: arXiv cs.RO 2025
Year: 2025

The disassembly of small household appliances poses significant challenges due to their complex and curved geometries, which render traditional rigid fixtures inadequate. In this paper, we propose a modular vacuum-based fixturing system that leverages commercially available balloon-type soft grippers to conform to arbitrarily shaped surfaces and provide stable support during screw-removal tasks. To enable a reliable deployment of the system, we develop a stability-aware planning framework that samples the bottom surface of the target object, filters candidate contact points based on geometric continuity, and evaluates support configurations using convex hull-based static stability criteria. We compare the quality of object placement under different numbers and configurations of balloon hands. In addition, real-world experiments were conducted to compare the success rates of traditional rigid fixtures with our proposed system. The results demonstrate that our method consistently achieves higher success rates and superior placement stability during screw removal tasks.
It's a Complete Haystack: Understanding Dependency Management Needs in Computer-Aided Design
Venue: arXiv cs.HC 2025
Year: 2025

In today's landscape, hardware development teams face increasing demands for better quality products, greater innovation, and shorter manufacturing lead times. Despite the need for more efficient and effective processes, hardware designers continue to struggle with a lack of awareness of design changes and other collaborators' actions, a persistent issue in decades of CSCW research. One significant and unaddressed challenge is understanding and managing dependencies between 3D CAD (computer-aided design) models, especially when products can contain thousands of interconnected components. In this two-phase formative study, we explore designers' pain points of CAD dependency management through a thematic analysis of 100 online forum discussions and semi-structured interviews with 10 designers. We identify nine key challenges related to the traceability, navigation, and consistency of CAD dependencies, that harm the effective coordination of hardware development teams. To address these challenges, we propose design goals and necessary features to enhance hardware designers' awareness and management of dependencies, ultimately with the goal of improving collaborative workflows.
SCALEFeedback: A Large-Scale Dataset of Synthetic Computer Science Assignments for LLM-generated Educational Feedback Research
Venue: arXiv cs.CY 2025
Year: 2025

Using LLMs to give educational feedback to students for their assignments has attracted much attention in the AI in Education field. Yet, there is currently no large-scale open-source dataset of student assignments that includes detailed assignment descriptions, rubrics, and student submissions across various courses. As a result, research on generalisable methodology for automatic generation of effective and responsible educational feedback remains limited. In the current study, we constructed a large-scale dataset of Synthetic Computer science Assignments for LLM-generated Educational Feedback research (SCALEFeedback). We proposed a Sophisticated Assignment Mimicry (SAM) framework to generate the synthetic dataset by one-to-one LLM-based imitation from real assignment descriptions, student submissions to produce their synthetic versions. Our open-source dataset contains 10,000 synthetic student submissions spanning 155 assignments across 59 university-level computer science courses. Our synthetic submissions achieved BERTScore F1 0.84, PCC of 0.62 for assignment marks and 0.85 for length, compared to the corresponding real-world assignment dataset, while ensuring perfect protection of student private information. All these results of our SAM framework outperformed results of a naive mimicry method baseline. The LLM-generated feedback for our synthetic assignments demonstrated the same level of effectiveness compared to that of real-world assignment dataset. Our research showed that one-to-one LLM imitation is a promising method for generating open-source synthetic educational datasets that preserve the original dataset's semantic meaning and student data distribution, while protecting student privacy and institutional copyright. SCALEFeedback enhances our ability to develop LLM-based generalisable methods for offering high-quality, automated educational feedback in a scalable way.
Efficient Bayer-Domain Video Computer Vision with Fast Motion Estimation and Learned Perception Residual
Venue: arXiv cs.CV 2025
Year: 2025

Video computer vision systems face substantial computational burdens arising from two fundamental challenges: eliminating unnecessary processing and reducing temporal redundancy in back-end inference while maintaining accuracy with minimal extra computation. To address these issues, we propose an efficient video computer vision framework that jointly optimizes both the front end and back end of the pipeline. On the front end, we remove the traditional image signal processor (ISP) and feed Bayer raw measurements directly into Bayer-domain vision models, avoiding costly human-oriented ISP operations. On the back end, we introduce a fast and highly parallel motion estimation algorithm that extracts inter-frame temporal correspondence to avoid redundant computation. To mitigate artifacts caused by motion inaccuracies, we further employ lightweight perception residual networks that directly learn perception-level residuals and refine the propagated features. Experiments across multiple models and tasks demonstrate that our system achieves substantial acceleration with only minor performance degradation.
ECMF: Enhanced Cross-Modal Fusion for Multimodal Emotion Recognition in MER-SEMI Challenge
Venue: arXiv cs.CV 2025
Year: 2025

Emotion recognition plays a vital role in enhancing human-computer interaction. In this study, we tackle the MER-SEMI challenge of the MER2025 competition by proposing a novel multimodal emotion recognition framework. To address the issue of data scarcity, we leverage large-scale pre-trained models to extract informative features from visual, audio, and textual modalities. Specifically, for the visual modality, we design a dual-branch visual encoder that captures both global frame-level features and localized facial representations. For the textual modality, we introduce a context-enriched method that employs large language models to enrich emotional cues within the input text. To effectively integrate these multimodal features, we propose a fusion strategy comprising two key components, i.e., self-attention mechanisms for dynamic modality weighting, and residual connections to preserve original representations. Beyond architectural design, we further refine noisy labels in the training set by a multi-source labeling strategy. Our approach achieves a substantial performance improvement over the official baseline on the MER2025-SEMI dataset, attaining a weighted F-score of 87.49% compared to 78.63%, thereby validating the effectiveness of the proposed framework.