OpenRaiser/Intern-Atlas huggingface dataset:
AI Answer Engine Citation Behavior An Empirical Analysis of the GEO16 Framework
Venue: arXiv cs.AI 2025
Year: 2025

AI answer engines increasingly mediate access to domain knowledge by generating responses and citing web sources. We introduce GEO-16, a 16 pillar auditing framework that converts on page quality signals into banded pillar scores and a normalized GEO score G that ranges from 0 to 1. Using 70 product intent prompts, we collected 1,702 citations across three engines (Brave Summary, Google AI Overviews, and Perplexity) and audited 1,100 unique URLs. In our corpus, the engines differed in the GEO quality of the pages they cited, and pillars related to Metadata and Freshness, Semantic HTML, and Structured Data showed the strongest associations with citation. Logistic models with domain clustered standard errors indicate that overall page quality is a strong predictor of citation, and simple operating points (for example, G at least 0.70 combined with at least 12 pillar hits) align with substantially higher citation rates in our data. We report per engine contrasts, vertical effects, threshold analysis, and diagnostics, then translate findings into a practical playbook for publishers. The study is observational and focuses on English language B2B SaaS pages; we discuss limitations, threats to validity, and reproducibility considerations.
Contextual Budget Bandit for Food Rescue Volunteer Engagement
Venue: arXiv cs.LG 2025
Year: 2025

Volunteer-based food rescue platforms tackle food waste by matching surplus food to communities in need. These platforms face the dual problem of maintaining volunteer engagement and maximizing the food rescued. Existing algorithms to improve volunteer engagement exacerbate geographical disparities, leaving some communities systematically disadvantaged. We address this issue by proposing Contextual Budget Bandit. Contextual Budget Bandit incorporates context-dependent budget allocation in restless multi-armed bandits, a model of decision-making which allows for stateful arms. By doing so, we can allocate higher budgets to communities with lower match rates, thereby alleviating geographical disparities. To tackle this problem, we develop an empirically fast heuristic algorithm. Because the heuristic algorithm can achieve a poor approximation when active volunteers are scarce, we design the Mitosis algorithm, which is guaranteed to compute the optimal budget allocation. Empirically, we demonstrate that our algorithms outperform baselines on both synthetic and real-world food rescue datasets, and show how our algorithm achieves geographical fairness in food rescue.
Well-Conditioned Polynomial Representations for Mathematical Handwriting Recognition
Venue: arXiv cs.CV 2025
Year: 2025

Previous work has made use of a parameterized plane curve polynomial representation for mathematical handwriting, with the polynomials represented in a Legendre or Legendre-Sobolev graded basis. This provides a compact geometric representation for the digital ink. Preliminary results have also been shown for Chebyshev and Chebyshev-Sobolev bases. This article explores the trade-offs between basis choice and polynomial degree to achieve accurate modeling with a low computational cost. To do this, we consider the condition number for polynomial evaluation in these bases and bound how the various inner products give norms for the variations between symbols.
LLM Enhancement with Domain Expert Mental Model to Reduce LLM Hallucination with Causal Prompt Engineering
Venue: arXiv cs.AI 2025
Year: 2025

Difficult decision-making problems abound in various disciplines and domains. The proliferation of generative techniques, especially large language models (LLMs), has excited interest in using them for decision support. However, LLMs cannot yet resolve missingness in their training data, leading to hallucinations. Retrieval-Augmented Generation (RAG) enhances LLMs by incorporating external information retrieval, reducing hallucinations and improving accuracy. Yet, RAG and related methods are only partial solutions, as they may lack access to all necessary sources or key missing information. Even everyday issues often challenge LLMs' abilities. Submitting longer prompts with context and examples is one approach to address knowledge gaps, but designing effective prompts is non-trivial and may not capture complex mental models of domain experts. For tasks with missing critical information, LLMs are insufficient, as are many existing systems poorly represented in available documents. This paper explores how LLMs can make decision-making more efficient, using a running example of evaluating whether to respond to a call for proposals. We propose a technology based on optimized human-machine dialogue and monotone Boolean and k-valued functions to discover a computationally tractable personal expert mental model (EMM) of decision-making. Our EMM algorithm for LLM prompt engineering has four steps: (1) factor identification, (2) hierarchical structuring of factors, (3) generating a generalized expert mental model specification, and (4) generating a detailed generalized expert mental model from that specification.
GAPrune: Gradient-Alignment Pruning for Domain-Aware Embeddings
Venue: arXiv cs.CL 2025
Year: 2025

Domain-specific embedding models have shown promise for applications that require specialized semantic understanding, such as coding agents and financial retrieval systems, often achieving higher performance gains than general models. However, state-of-the-art embedding models are typically based on LLMs, which contain billions of parameters, making deployment challenging in resource-constrained environments. Model compression through pruning offers a promising solution, but existing pruning methods treat all parameters uniformly, failing to distinguish between general semantic representations and domain-specific patterns, leading to suboptimal pruning decisions. Thus, we propose GAPrune, a pruning framework that addresses this challenge by considering both domain importance and preserving general linguistic foundation. Our method uses Fisher Information to measure importance and general-domain gradient alignment to assess parameter behavior, then combines these signals using our Domain Alignment Importance (DAI) scoring. Lower DAI scores indicate that the parameter is either less important for the domain task or creates conflicts between domain and general objectives. Experiments on two domain benchmarks, FinMTEB and ChemTEB, show that GAPrune maintains performance within 2.5% of dense models in one-shot pruning at 50% sparsity, while outperforming all baselines. With retraining in 100 steps, GAPrune achieves +4.51% improvement on FinMTEB and +1.73% on ChemTEB, demonstrating that our pruning strategy not only preserves but enhances domain-specific capabilities. Our findings demonstrate that principled pruning strategies can achieve model compression and enhanced domain specialization, providing the research community with a new approach for development.
A Universal Wire Testing Machine for Enhancing the Performance of Wire-Driven Robots
Venue: arXiv cs.RO 2025
Year: 2025

Compared with gears and linkages, wires constitute a lightweight, low-friction transmission mechanism. However, because wires are flexible materials, they tend to introduce large modeling errors, and their adoption in industrial and research robots remains limited.In this study, we built a Universal Wire Testing Machine that enables measurement and adjustment of wire characteristics to improve the performance of wire-driven mechanisms. Using this testing machine, we carried out removal of initial wire stretch, measurement of tension transmission efficiency for eight different diameters of passive pulleys, and measurement of the dynamic behavior of variable-length wires. Finally, we applied the data obtained from this testing machine to the force control of an actual wire-driven robot, reducing the end-effector force error.
Agent-based Simulation for Drone Charging in an Internet of Things Environment System
Venue: arXiv cs.MA 2025
Year: 2025

This paper presents an agent-based simulation model for coordinating battery recharging in drone swarms, focusing on applications in Internet of Things (IoT) and Industry 4.0 environments. The proposed model includes a detailed description of the simulation methodology, system architecture, and implementation. One practical use case is explored: Smart Farming, highlighting how autonomous coordination strategies can optimize battery usage and mission efficiency in large-scale drone deployments. This work uses a machine learning technique to analyze the agent-based simulation sensitivity analysis output results.
Nav-R1: Reasoning and Navigation in Embodied Scenes
Venue: arXiv cs.RO 2025
Year: 2025

Embodied navigation requires agents to integrate perception, reasoning, and action for robust interaction in complex 3D environments. Existing approaches often suffer from incoherent and unstable reasoning traces that hinder generalization across diverse environments, and difficulty balancing long-horizon semantic reasoning with low-latency control for real-time navigation. To address these challenges, we propose Nav-R1, an embodied foundation model that unifies reasoning in embodied environments. We first construct Nav-CoT-110K, a large-scale dataset of step-by-step Chains-of-Thought (CoT) for embodied tasks, which enables cold-start initialization with structured reasoning. Building on this foundation, we design a GRPO-based reinforcement learning framework with three complementary rewards: format, understanding, and navigation, to improve structural adherence, semantic grounding, and path fidelity. Furthermore, we introduce a Fast-in-Slow reasoning paradigm, decoupling deliberate semantic reasoning from low-latency reactive control for efficient yet coherent navigation. Extensive evaluations on embodied AI benchmarks demonstrate that Nav-R1 consistently outperforms strong baselines, with over 8% average improvement in reasoning and navigation performance. Real-world deployment on a mobile robot further validates its robustness under limited onboard resources. Code: https://github.com/AIGeeksGroup/Nav-R1. Website: https://aigeeksgroup.github.io/Nav-R1.
Using utility graphs to search for Pareto-optimal outcomes in complex, interdependent issue negotiations
Venue: arXiv cs.MA 2025
Year: 2025

This paper studies how utility graphs decomposition algorithms can be used to effectively search for Pareto-efficient outcomes in complex automated negotiation. We propose a number of algorithms that can efficiently handle high-dimensional utility graphs, and test them on a variety of utility graph topologies, generated based on state of the art methods for analysing complex graphs. We show that we can achieve exponential speed-up, for many structures, even for very large utility graphs. To our knowledge, our approach can handle the largest utility spaces to date for complex interdependent negotiations, in terms of number of issues. Moreover, we examine the performance of our algorithms across two different types of elicitation queries from the literature: value and comparison queries, thus making a connection between automated negotiation and the preference elicitation literature.
Total Variation Subgradient Guided Image Fusion for Dual-Camera CASSI System
Venue: arXiv cs.CV 2025
Year: 2025

Spectral imaging technology has long-faced fundamental challenges in balancing spectral, spatial, and temporal resolutions. While compressive sensing-based Coded Aperture Snapshot Spectral Imaging (CASSI) mitigates this trade-off through optical encoding, high compression ratios result in ill-posed reconstruction problems. Traditional model-based methods exhibit limited performance due to reliance on handcrafted inherent image priors, while deep learning approaches are constrained by their black-box nature, which compromises physical interpretability. To address these limitations, we propose a dual-camera CASSI reconstruction framework that integrates total variation (TV) subgradient theory. By establishing an end-to-end SD-CASSI mathematical model, we reduce the computational complexity of solving the inverse problem and provide a mathematically well-founded framework for analyzing multi-camera systems. A dynamic regularization strategy is introduced, incorporating normalized gradient constraints from RGB/panchromatic-derived reference images, which constructs a TV subgradient similarity function with strict convex optimization guarantees. Leveraging spatial priors from auxiliary cameras, an adaptive reference generation and updating mechanism is designed to provide subgradient guidance. Experimental results demonstrate that the proposed method effectively preserves spatial-spectral structural consistency. The theoretical framework establishes an interpretable mathematical foundation for computational spectral imaging, demonstrating robust performance across diverse reconstruction scenarios. The source code is available at https://github.com/bestwishes43/ADMM-TVDS.
Detectability Thresholds for Network Attacks on Static Graphs and Temporal Networks: Information-Theoretic Limits and Nearly-Optimal Tests
Venue: arXiv cs.IT 2025
Year: 2025

We develop a consolidated theory for the detectability of network-borne attacks under two canonical observation models: (i) a static graph drawn from an Erdos-Renyi background with a planted anomalous community, and (ii) a temporal interaction network modeled by multivariate point processes (Poisson or Hawkes). Our main contribution is to match, up to universal constants, information-theoretic lower and upper bounds that govern when reliable testing is possible. In the static case, the core quantity is the accumulated edgewise signal k^2 * chi^2(Bern(p+Delta) || Bern(p)), where chi^2 ~ Delta^2 / [p(1-p)] for small Delta; detection is impossible when this falls below c * log n, and a non-backtracking spectral statistic succeeds above C * log n. In the temporal case, detectability is controlled by the KL information rate I contributed by internal edges over a window of length T, yielding a threshold T I >= log n; a likelihood-based cumulative-sum (CUSUM) test achieves first-order optimal delay approximately abs(log alpha) / I at false-alarm level alpha. We also quantify robustness to bounded edge perturbations and outline conditional statistical-computational separations. A brief case study shows how to turn these bounds into concrete design choices.
Public Data Assisted Differentially Private In-Context Learning
Venue: arXiv cs.AI 2025
Year: 2025

In-context learning (ICL) in Large Language Models (LLMs) has shown remarkable performance across various tasks without requiring fine-tuning. However, recent studies have highlighted the risk of private data leakage through the prompt in ICL, especially when LLMs are exposed to malicious attacks. While differential privacy (DP) provides strong privacy guarantees, it often significantly reduces the utility of in-context learning (ICL). To address this challenge, we incorporate task-related public data into the ICL framework while maintaining the DP guarantee. Based on this approach, we propose a private in-context learning algorithm that effectively balances privacy protection and model utility. Through experiments, we demonstrate that our approach significantly improves the utility of private ICL with the assistance of public data. Additionally, we show that our method is robust against membership inference attacks, demonstrating empirical privacy protection.
Geospatial AI for Liquefaction Hazard and Impact Forecasting: A Demonstrative Study in the U.S. Pacific Northwest
Venue: arXiv cs.CE 2025
Year: 2025

Recent large-magnitude earthquakes have demonstrated the damaging consequences of soil liquefaction and reinforced the need to understand and plan for liquefaction hazards at a regional scale. In the United States, the Pacific Northwest is uniquely vulnerable to such consequences given the potential for crustal, intraslab, and subduction zone earthquakes. In this study, the liquefaction hazard is predicted geospatially at high resolution and across regional scales for 85 scenario earthquakes in the states of Washington and Oregon. This is accomplished using an emergent geospatial model that is driven by machine learning, and which predicts the probability of damaging ground deformation by surrogating state-of-practice geotechnical models. The adopted model shows improved performance and has conceptual advantages over prior regional-scale modeling approaches in that predictions (i) are informed by mechanics, (ii) employ more geospatial information using machine learning, and (iii) are geostatistically anchored to known subsurface conditions. The utility of the resulting predictions for the 85 scenarios is then demonstrated via asset and network infrastructure vulnerability assessments. The liquefaction hazard forecasts are published in a GIS-ready, public repository and are suitable for disaster simulations, evacuation route planning, network vulnerability analysis, land-use planning, insurance loss modeling, hazard communication, public investment prioritization, and other regional-scale applications.
Decoupling Search and Learning in Neural Net Training
Venue: arXiv cs.LG 2025
Year: 2025

Gradient descent typically converges to a single minimum of the training loss without mechanisms to explore alternative minima that may generalize better. Searching for diverse minima directly in high-dimensional parameter space is generally intractable. To address this, we propose a framework that performs training in two distinct phases: search in a tractable representation space (the space of intermediate activations) to find diverse representational solutions, and gradient-based learning in parameter space by regressing to those searched representations. Through evolutionary search, we discover representational solutions whose fitness and diversity scale with compute--larger populations and more generations produce better and more varied solutions. These representations prove to be learnable: networks trained by regressing to searched representations approach SGD's performance on MNIST, CIFAR-10, and CIFAR-100. Performance improves with search compute up to saturation. The resulting models differ qualitatively from networks trained with gradient descent, following different representational trajectories during training. This work demonstrates how future training algorithms could overcome gradient descent's exploratory limitations by decoupling search in representation space from efficient gradient-based learning in parameter space.
Statistical Model Checking of NetLogo Models
Venue: arXiv cs.MA 2025
Year: 2025

Agent-based models (ABMs) are gaining increasing traction in several domains, due to their ability to represent complex systems that are not easily expressible with classical mathematical models. This expressivity and richness come at a cost: ABMs can typically be analyzed only through simulation, making their analysis challenging. Specifically, when studying the output of ABMs, the analyst is often confronted with practical questions such as: (i) how many independent replications should be run? (ii) how many initial time steps should be discarded as a warm-up? (iii) after the warm-up, how long should the model run? (iv) what are the right parameter values? Analysts usually resort to rules of thumb and experimentation, which lack statistical rigor. This is mainly because addressing these points takes time, and analysts prefer to spend their limited time improving the model. In this paper, we propose a methodology, drawing on the field of Statistical Model Checking, to automate the process and provide guarantees of statistical rigor for ABMs written in NetLogo, one of the most popular ABM platforms. We discuss MultiVeStA, a tool that dramatically reduces the time and human intervention needed to run statistically rigorous checks on ABM outputs, and introduce its integration with NetLogo. Using two ABMs from the NetLogo library, we showcase MultiVeStA's analysis capabilities for NetLogo ABMs, as well as a novel application to statistically rigorous calibration. Our tool-chain makes it immediate to perform statistical checks with NetLogo models, promoting more rigorous and reliable analyses of ABM outputs.
RU Energy Modeling for O-RAN in ns3-oran
Venue: arXiv cs.NI 2025
Year: 2025

This paper presents a detailed and flexible power consumption model for Radio Units (RUs) in O-RAN using the ns3-oran simulator. This is the first ns3-oran model supporting xApp control to perform the RU power modeling. In contrast to existing frameworks like EARTH or VBS-DRX, the proposed framework is RU-centric and is parameterized by hardware-level features, such as the number of transceivers, the efficiency of the power amplifier, mmWave overheads, and standby behavior. It enables simulation-driven assessment of energy efficiency at various transmit power levels and seamlessly integrates with ns-3's energy tracking system. To help upcoming xApp-driven energy management strategies in O-RAN installations, numerical research validates the model's capacity to represent realistic nonlinear power scaling. It identifies ideal operating points for effective RU behavior.
AD-GS: Alternating Densification for Sparse-Input 3D Gaussian Splatting
Venue: arXiv cs.GR 2025
Year: 2025

3D Gaussian Splatting (3DGS) has shown impressive results in real-time novel view synthesis. However, it often struggles under sparse-view settings, producing undesirable artifacts such as floaters, inaccurate geometry, and overfitting due to limited observations. We find that a key contributing factor is uncontrolled densification, where adding Gaussian primitives rapidly without guidance can harm geometry and cause artifacts. We propose AD-GS, a novel alternating densification framework that interleaves high and low densification phases. During high densification, the model densifies aggressively, followed by photometric loss based training to capture fine-grained scene details. Low densification then primarily involves aggressive opacity pruning of Gaussians followed by regularizing their geometry through pseudo-view consistency and edge-aware depth smoothness. This alternating approach helps reduce overfitting by carefully controlling model capacity growth while progressively refining the scene representation. Extensive experiments on challenging datasets demonstrate that AD-GS significantly improves rendering quality and geometric consistency compared to existing methods. The source code for our model can be found on our project page: https://gurutvapatle.github.io/publications/2025/ADGS.html .