OpenRaiser/Intern-Atlas huggingface dataset:
Evaluating Device-First Continuum AI (DFC-AI) for Autonomous Operations in the Energy Sector
Venue: arXiv cs.NI 2026
Year: 2026

Industrial automation in the energy sector requires AI systems that can operate autonomously regardless of network availability, a requirement that cloud-centric architectures cannot meet. This paper evaluates the application of Device-First Continuum AI (DFC-AI) to critical energy sector operations. DFC-AI, a specialized architecture within the Hybrid Edge Cloud paradigm, implements intelligent agents using a microservices architecture that originates at end devices and extends across the computational continuum. Through comprehensive simulations of energy sector scenarios including drone inspections, sensor networks, and worker safety systems, we demonstrate that DFC-AI maintains full operational capability during network outages while cloud and gateway-based systems experience complete or partial failure. Our analysis reveals that zero-configuration GPU discovery and heterogeneous device clustering are particularly well-suited for energy sector deployments, where specialized nodes can handle intensive AI workloads for entire fleets of inspection drones or sensor networks. The evaluation shows that DFC-AI achieves significant latency reduction and energy savings compared to cloud architectures. Additionally, we find that gateway based edge solutions can paradoxically cost more than cloud solutions for certain energy sector workloads due to infrastructure overhead, while DFC-AI can consistently provide cost savings by leveraging enterprise-owned devices. These findings, validated through rigorous statistical analysis, establish that DFC-AI addresses the unique challenges of energy sector operations, ensuring intelligent agents remain available and functional in remote oil fields, offshore platforms, and other challenging environments characteristic of the industry.
Learning the Value of Value Learning
Venue: arXiv cs.AI 2026
Year: 2026

Standard decision frameworks address uncertainty about facts but assume fixed options and values. We extend the Jeffrey-Bolker framework to model refinements in values and prove a value-of-information theorem for axiological refinement. In multi-agent settings, we establish that mutual refinement will characteristically transform zero-sum games into positive-sum interactions and yield Pareto-improvements in Nash bargaining. These results show that a framework of rational choice can be extended to model value refinement. By unifying epistemic and axiological refinement under a single formalism, we broaden the conceptual foundations of rational choice and illuminate the normative status of ethical deliberation.
Generative Adversarial Post-Training Mitigates Reward Hacking in Live Human-AI Music Interaction
Venue: arXiv cs.LG 2026
Year: 2026

Most applications of generative AI involve a sequential interaction in which a person inputs a prompt and waits for a response, and where reaction time and adaptivity are not important factors. In contrast, live jamming is a collaborative interaction that requires real-time coordination and adaptation without access to the other player's future moves, while preserving diversity to sustain a creative flow. Reinforcement learning post-training enables effective adaptation through on-policy interaction, yet it often reduces output diversity by exploiting coherence-based rewards. This collapse, known as ``reward hacking'', affects many RL post-training pipelines, but is especially harmful in live jamming, where musical creativity relies on dynamic variation and mutual responsiveness. In this paper, we propose a novel adversarial training method on policy-generated trajectories to mitigate reward hacking in RL post-training for melody-to-chord accompaniment. A co-evolving discriminator separates policy trajectories from the data distribution, while the policy maximizes the discriminator output in addition to coherence rewards to prevent collapse to trivial outputs. We evaluate accompaniment quality and output diversity in simulation with both fixed test melodies and learned melody agents, and we conduct a user study with the model deployed in a real-time interactive system with expert musicians. Quantitative evaluation and user feedback demonstrate improved output diversity, harmonic coherence, adaptation speed and user agency. Our results demonstrate a simple yet effective method to mitigate reward hacking in RL post-training of generative sequence models.
RoboArmGS: High-Quality Robotic Arm Splatting via Bézier Curve Refinement
Venue: arXiv cs.RO 2026
Year: 2026

Constructing photorealistic and controllable robotic arm digital assets from real observations is fundamental to robotic applications. Current approaches naively bind static 3D Gaussians according to URDF links, forcing them to follow an URDF-rigged motion passively. However, the idealized URDF-rigged motion cannot accurately model the actual motion captured in real-world observations, leading to severe rendering artifacts in 3D Gaussians. To address these challenges, we propose RoboArmGS, a novel hybrid representation that refines the URDF-rigged motion with learnable Bézier curves, enabling more accurate real-world motion modeling. To be more specific, we present a learnable Bézier Curve motion refiner that corrects per-joint residuals to address mismatches between real-world motion and URDF-rigged motion. RoboArmGS enables the learning of more accurate real-world motion while achieving a coherent binding of 3D Gaussians across arm parts. To support future research, we contribute a carefully collected dataset named RoboArm4D, which comprises several widely used robotic arms for evaluating the quality of building high-quality digital assets. We evaluate our approach on RoboArm4D, and RoboArmGS achieves state-of-the-art performance in real-world motion modeling and rendering quality. The code and dataset will be released.
Multi-Agent Collaborative Filtering: Orchestrating Users and Items for Agentic Recommendations
Venue: arXiv cs.CL 2026
Year: 2026

Agentic recommendations cast recommenders as large language model (LLM) agents that can plan, reason, use tools, and interact with users of varying preferences in web applications. However, most existing agentic recommender systems focus on generic single-agent plan-execute workflows or multi-agent task decomposition pipelines. Without recommendation-oriented design, they often underuse the collaborative signals in the user-item interaction history, leading to unsatisfying recommendation results. To address this, we propose the Multi-Agent Collaborative Filtering (MACF) framework for agentic recommendations, drawing an analogy between traditional collaborative filtering algorithms and LLM-based multi-agent collaboration. Specifically, given a target user and query, we instantiate similar users and relevant items as LLM agents with unique profiles. Each agent is able to call retrieval tools, suggest candidate items, and interact with other agents. Different from the static preference aggregation in traditional collaborative filtering, MACF employs a central orchestrator agent to adaptively manage the collaboration between user and item agents via dynamic agent recruitment and personalized collaboration instruction. Experimental results on datasets from three different domains show the advantages of our MACF framework compared to strong agentic recommendation baselines.
Radiation-Preserving Selective Imaging for Pediatric Hip Dysplasia: A Cross-Modal Ultrasound-Xray Policy with Limited Labels
Venue: arXiv cs.LG 2026
Year: 2026

We study an ultrasound-first, radiation-preserving policy for developmental dysplasia of the hip (DDH) that requests a radiograph only when needed. We (i) pretrain modality-specific encoders (ResNet-18) with SimSiam on a large unlabelled registry (37186 ultrasound; 19546 radiographs), (ii) freeze the backbones and fit small, measurement-faithful heads on DDH-relevant landmarks and measurements, (iii) calibrate a one-sided conformal deferral rule on ultrasound predictions that provides finite sample marginal coverage guarantees under exchangeability, using a held-out calibration set. Ultrasound heads predict Graf alpha, beta, and femoral head coverage; X-ray heads predict acetabular index (AI), center-edge (CE) angle and IHDI grade. On our held out labeled evaluation set, ultrasound measurement error is modest (e.g., alpha MAE ~= 9.7 degrees, coverage MAE ~= 14.0%), while radiographic probes achieve AI and CE MAEs of ~= 7.6 degrees and ~= 8.9 degrees, respectively. The calibrated US-only policy is explored across rule families (alpha-only; alpha OR coverage; alpha AND coverage), conformal miscoverage levels, and per-utility trade-offs using decision-curve analysis. Conservative settings yield high coverage with near-zero US-only rates; permissive settings (e.g., alpha OR coverage at larger deltas) achieve non-zero US-only throughput with expected coverage tradeoffs. The result is a simple, reproducible pipeline that turns limited labels into interpretable measurements and tunable selective imaging curves suitable for clinical handoff and future external validation.
User-Centric Requirements Prioritization in mHealth Applications: Insights from a Discrete Choice Experiment
Venue: arXiv cs.SE 2026
Year: 2026

Mobile health (mHealth) applications are widely used for chronic disease management, but usability and accessibility challenges persist due to the diverse needs of users. Adaptive User Interfaces (AUIs) offer a personalized solution to enhance user experience, yet barriers to adoption remain. Understanding user preferences and trade-offs is essential to ensure widespread acceptance of adaptation designs. This study identifies key factors influencing user preferences and trade-offs in mHealth adaptation design. A Discrete Choice Experiment (DCE) was conducted with 186 participants who have chronic diseases and use mHealth applications. Participants were asked to select preferred adaptation designs from choices featuring six attributes with varying levels. A mixed logit model was used to analyze preference heterogeneity and determine the factors most likely influencing adoption. Additionally, subgroup analyses were performed to explore differences by age, gender, health conditions, and coping mechanisms. Maintaining usability while ensuring controllability over adaptations, infrequent adaptations, and small-scale changes are key factors that facilitate the adoption of adaptive mHealth app designs. In contrast, frequently used functions and caregiver involvement can diminish the perceived value of such adaptations. This study employs a data-driven approach to quantify user preferences, identify key trade-offs, and reveal variations across demographic and behavioral subgroups through preference heterogeneity modeling. Furthermore, our results offer valuable guidance for developing future adaptive mHealth applications and lay the groundwork for continued exploration into requirements prioritization within the field of software engineering.
Energy-Efficient Routing Protocol in Vehicular Opportunistic Networks: A Dynamic Cluster-based Routing Using Deep Reinforcement Learning
Venue: arXiv cs.NI 2026
Year: 2026

Opportunistic Networks (OppNets) employ the Store-Carry-Forward (SCF) paradigm to maintain communication during intermittent connectivity. However, routing performance suffers due to dynamic topology changes, unpredictable contact patterns, and resource constraints including limited energy and buffer capacity. These challenges compromise delivery reliability, increase latency, and reduce node longevity in highly dynamic environments. This paper proposes Cluster-based Routing using Deep Reinforcement Learning (CR-DRL), an adaptive routing approach that integrates an Actor-Critic learning framework with a heuristic function. CR-DRL enables real-time optimal relay selection and dynamic cluster overlap adjustment to maintain connectivity while minimizing redundant transmissions and enhancing routing efficiency. Simulation results demonstrate significant improvements over state-of-the-art baselines. CR-DRL extends node lifetimes by up to 21%, overall energy use is reduced by 17%, and nodes remain active for 15% longer. Communication performance also improves, with up to 10% higher delivery ratio, 28.5% lower delay, 7% higher throughput, and data requiring 30% fewer transmission steps across the network.
Diffusion Model-Enhanced Environment Reconstruction in ISAC
Venue: arXiv cs.NI 2026
Year: 2026

Recently, environment reconstruction (ER) in integrated sensing and communication (ISAC) systems has emerged as a promising approach for achieving high-resolution environmental perception. However, the initial results obtained from ISAC systems are coarse and often unsatisfactory due to the high sparsity of the point clouds and significant noise variance. To address this problem, we propose a noise-sparsity-aware diffusion model (NSADM) post-processing framework. Leveraging the powerful data recovery capabilities of diffusion models, the proposed scheme exploits spatial features and the additive nature of noise to enhance point cloud density and denoise the initial input. Simulation results demonstrate that the proposed method significantly outperforms existing model-based and deep learning-based approaches in terms of Chamfer distance and root mean square error.
On the Tail Transition of First Arrival Position Channels: From Cauchy to Exponential Decay
Venue: arXiv cs.IT 2026
Year: 2026

While the zero-drift first arrival position (FAP) channel exhibits a Cauchy-distributed lateral displacement, nonzero drift in practical systems introduces advective transport that regularizes this singular limit. This letter characterizes the drift-induced transition of FAP noise from heavy-tailed algebraic decay to exponential regularization. By asymptotically examining the exact FAP density, we identify a characteristic propagation distance (CPD) that serves as the fundamental boundary separating diffusion-dominated and drift-dominated regimes. Numerical evaluations demonstrate that in low-drift environments, variance-matched Gaussian approximations severely underestimate the true communication potential, whereas the zero-drift Cauchy law provides a robust, physically grounded performance baseline.
Hierarchical Dual-Strategy Unlearning for Biomedical and Healthcare Intelligence Using Imperfect and Privacy-Sensitive Medical Data
Venue: arXiv cs.LG 2026
Year: 2026

Large language models (LLMs) exhibit exceptional performance but pose substantial privacy risks due to training data memorization, particularly within healthcare contexts involving imperfect or privacy-sensitive patient information. We present a hierarchical dual-strategy framework for selective knowledge unlearning that precisely removes specialized knowledge while preserving fundamental medical competencies. Our approach synergistically integrates geometric-constrained gradient updates to selectively modulate target parameters with concept-aware token-level interventions that distinguish between preservation-critical and unlearning-targeted tokens via a unified four-level medical concept hierarchy. Comprehensive evaluations on the MedMCQA (surgical) and MHQA (anxiety, depression, trauma) datasets demonstrate superior performance, achieving an 82.7% forgetting rate and 88.5% knowledge preservation. Notably, our framework maintains robust privacy guarantees while requiring modification of only 0.1% of parameters, addressing critical needs for regulatory compliance, auditability, and ethical standards in clinical research.
ModHiFi: Identifying High Fidelity predictive components for Model Modification
Venue: arXiv cs.LG 2026
Year: 2026

Open weight models, which are ubiquitous, rarely provide access to their training data or loss function. This makes modifying such models for tasks such as pruning or unlearning, which are constrained by this unavailability, an active area of research. Existing techniques typically require gradients or ground-truth labels, rendering them infeasible in settings with limited computational resources. In this work, we investigate the fundamental question of identifying components that are critical to the model's predictive performance, without access to either gradients or the loss function, and with only distributional access such as synthetic data. We theoretically demonstrate that the global error is linearly bounded by local reconstruction errors for Lipschitz-continuous networks such as CNNs and well-trained Transformers (which, contrary to existing literature, we find exhibit Lipschitz continuity). This motivates using the locally reconstructive behavior of component subsets to quantify their global importance, via a metric that we term Subset Fidelity. In the uncorrelated features setting, selecting individual components based on their Subset Fidelity scores is optimal, which we utilize to propose ModHiFi, an algorithm for model modification that requires neither training data nor access to a loss function. ModHiFi-P, for structured pruning, achieves an 11\% speedup over the current state of the art on ImageNet models and competitive performance on language models. ModHiFi-U, for classwise unlearning, achieves complete unlearning on CIFAR-10 without fine-tuning and demonstrates competitive performance on Swin Transformers.
A Layered Protocol Architecture for the Internet of Agents
Venue: arXiv cs.NI 2026
Year: 2026

Large Language Models (LLMs) have demonstrated remarkable performance improvements and the ability to learn domain-specific languages (DSLs), including APIs and tool interfaces. This capability has enabled the creation of AI agents that can perform preliminary computations and act through tool calling, which is now being standardized via protocols like MCP. However, LLMs face fundamental limitations: their context windows cannot grow indefinitely, restricting their memory and computational capacity. Agent collaboration emerges as essential for solving increasingly complex problems, mirroring how computational systems rely on different types of memory to scale. The "Internet of Agents" (IoA) represents the communication stack that enables agents to scale by distributing computation across collaborating entities. Current network architectural stacks (OSI and TCP/IP) were designed for data delivery between hosts and processes, not for agent collaboration with semantic understanding. To address this gap, we propose two new layers: an Agent Communication Layer (L8) and an Agent Semantic Layer (L9). L8 formalizes the structure of communication, standardizing message envelopes, speech-act performatives (e.g., REQUEST, INFORM), and interaction patterns (e.g., request-reply, publish-subscribe), building on protocols like MCP. The proposed L9 layer: (1) formalizes semantic context discovery and negotiation, (2) provides semantic grounding by binding terms to semantic context, and (3) semantically validates incoming prompts and performs disambiguation as needed. Furthermore, L9 introduces primitives for coordination and consensus, allowing agents to achieve alignment on shared states, collective goals, and distributed beliefs. Together, these layers provide the foundation for scalable, distributed agent collaboration, enabling the next generation of multi-agentic systems.
Dynamic-ICP: Doppler-Aware Iterative Closest Point Registration for Dynamic Scenes
Venue: arXiv cs.RO 2026
Year: 2026

Reliable odometry in highly dynamic environments remains challenging when it relies on ICP-based registration: ICP assumes near-static scenes and degrades in repetitive or low-texture geometry. We introduce Dynamic-ICP, a Doppler-aware registration framework. The method (i) estimates ego motion from per-point Doppler velocity via robust regression and builds a velocity filter, (ii) clusters dynamic objects and reconstructs object-wise translational velocities from ego-compensated radial measurements, (iii) predicts dynamic points with a constant-velocity model, and (iv) aligns scans using a compact objective that combines point-to-plane geometry residual with a translation-invariant, rotation-only Doppler residual. The approach requires no external sensors or sensor-vehicle calibration and operates directly on FMCW LiDAR range and Doppler velocities. We evaluate Dynamic-ICP on three datasets-HeRCULES, HeLiPR, AevaScenes-focusing on highly dynamic scenes. Dynamic-ICP consistently improves rotational stability and translation accuracy over the state-of-the-art methods. Our approach is also simple to integrate into existing pipelines, runs in real time, and provides a lightweight solution for robust registration in dynamic environments. To encourage further research, the code is available at: https://github.com/JMUWRobotics/Dynamic-ICP.
Material-informed Gaussian Splatting for 3D World Reconstruction in a Digital Twin
Venue: arXiv cs.CV 2026
Year: 2026

3D reconstruction for Digital Twins often relies on LiDAR-based methods, which provide accurate geometry but lack the semantics and textures naturally captured by cameras. Traditional LiDAR-camera fusion approaches require complex calibration and still struggle with certain materials like glass, which are visible in images but poorly represented in point clouds. We propose a camera-only pipeline that reconstructs scenes using 3D Gaussian Splatting from multi-view images, extracts semantic material masks via vision models, converts Gaussian representations to mesh surfaces with projected material labels, and assigns physics-based material properties for accurate sensor simulation in modern graphics engines and simulators. This approach combines photorealistic reconstruction with physics-based material assignment, providing sensor simulation fidelity comparable to LiDAR-camera fusion while eliminating hardware complexity and calibration requirements. We validate our camera-only method using an internal dataset from an instrumented test vehicle, leveraging LiDAR as ground truth for reflectivity validation alongside image similarity metrics.
Exploiting Low Scanwidth to Resolve Soft Polytomies
Venue: arXiv cs.DS 2026
Year: 2026

Phylogenetic networks allow modeling reticulate evolution, capturing events such as hybridization and horizontal gene transfer. A fundamental computational problem in this context is the Tree Containment problem, which asks whether a given phylogenetic network is compatible with a given phylogenetic tree. However, the classical statement of the problem is not robust to poorly supported branches in biological data, possibly leading to false negatives. In an effort to address this, a relaxed version that accounts for uncertainty, called Soft Tree Containment, has been introduced by Bentert, Malík, and Weller [SWAT'18]. We present an algorithm that solves Soft Tree Containment in $2^{O(Δ_T \cdot k \cdot \log(k))} \cdot n^{O(1)}$ time, where $k = \operatorname{sw}(Γ) + Δ_N$, with $Δ_T$ and $Δ_N$ denoting the maximum out-degrees in the tree and the network, respectively, and $\operatorname{sw}(Γ)$ denoting the "scanwidth" [Berry, Scornavacca, and Weller, SOFSEM'20] of a given tree extension of the network, while $n$ is the input size. Our approach leverages the fact that phylogenetic networks encountered in practice often exhibit low scanwidth, making the problem more tractable.