Automating disassembly of critical components from end-of-life (EoL) desktops, such as high-value items like RAM modules and CPUs, as well as sensitive parts like hard disk drives, remains challenging due to the inherent variability and uncertainty of these products. Moreover, their disassembly requires sequential, precise, and dexterous operations, further increasing the complexity of automation. Current robotic disassembly processes are typically divided into several stages: perception, sequence planning, task planning, motion planning, and manipulation. Each stage requires explicit modeling, which limits generalization to unfamiliar scenarios. Recent development of vision-language-action (VLA) models has presented an end-to-end approach for general robotic manipulation tasks. Although VLAs have demonstrated promising performance on simple tasks, the feasibility of applying such models to complex disassembly remains largely unexplored. In this paper, we collected a customized dataset for robotic RAM and CPU disassembly and used it to fine-tune two well-established VLA approaches, OpenVLA and OpenVLA-OFT, as a case study. We divided the whole disassembly task into several small steps, and our preliminary experimental results indicate that the fine-tuned VLA models can faithfully complete multiple early steps but struggle with certain critical subtasks, leading to task failure. However, we observed that a simple hybrid strategy that combines VLA with a rule-based controller can successfully perform the entire disassembly operation. These findings highlight the current limitations of VLA models in handling the dexterity and precision required for robotic EoL product disassembly. By offering a detailed analysis of the observed results, this study provides insights that may inform future research to address current challenges and advance end-to-end robotic automated disassembly.

AI policy guidance is predominantly written as prose, which practitioners must first convert into executable rules before frameworks can evaluate or enforce them. This manual step is slow, error-prone, difficult to scale, and often delays the use of safeguards in real-world deployments. To address this gap, we present Policy-to-Tests (P2T), a framework that converts natural-language policy documents into normalized, machine-readable rules. The framework comprises a pipeline and a compact domain-specific language (DSL) that encodes hazards, scope, conditions, exceptions, and required evidence, yielding a canonical representation of extracted rules. To test the framework beyond a single policy, we apply it across general frameworks, sector guidance, and enterprise standards, extracting obligation-bearing clauses and converting them into executable rules. These AI-generated rules closely match strong human baselines on span-level and rule-level metrics, with robust inter-annotator agreement on the gold set. To evaluate downstream behavioral and safety impact, we add HIPAA-derived safeguards to a generative agent and compare it with an otherwise identical agent without guardrails. An LLM-based judge, aligned with gold-standard criteria, measures violation rates and robustness to obfuscated and compositional prompts. Detailed results are provided in the appendix. We release the codebase, DSL, prompts, and rule sets as open-source resources to enable reproducible evaluation.

The evolution toward sixth-generation wireless systems positions intelligence as a native network capability, fundamentally transforming the design of radio access networks (RANs). Within this vision, Semantic-native communication and agentic intelligence are expected to play central roles. SemCom departs from bit-level fidelity and instead emphasizes task-oriented meaning exchange, enabling compact SC and introducing new performance measures such as semantic fidelity and task success rate. Agentic intelligence endows distributed RAN entities with goal-driven autonomy, reasoning, planning, and multi-agent collaboration, increasingly supported by foundation models and knowledge graphs. In this work, we first introduce the conceptual foundations of SemCom and agentic networking, and discuss why existing AI-driven O-RAN solutions remain largely bit-centric and task-siloed. We then present a unified taxonomy that organizes recent research along three axes: i) semantic abstraction level (symbol/feature/intent/knowledge), ii) agent autonomy and coordination granularity (single-, multi-, and hierarchical-agent), and iii) RAN control placement across PHY/MAC, near-real-time RIC, and non-real-time RIC. Based on this taxonomy, we systematically introduce enabling technologies including task-oriented semantic encoders/decoders, multi-agent reinforcement learning, foundation-model-assisted RAN agents, and knowledge-graph-based reasoning for cross-layer awareness. Representative 6G use cases, such as immersive XR, vehicular V2X, and industrial digital twins, are analyzed to illustrate the semantic-agentic convergence in practice. Finally, we identify open challenges in semantic representation standardization, scalable trustworthy agent coordination, O-RAN interoperability, and energy-efficient AI deployment, and outline research directions toward operational semantic-agentic AI-RAN.

This paper proposes an Interactive Inference Behavior Tree (IIBT) framework that integrates behavior trees (BTs) with active inference under the free energy principle for distributed multi-robot decision-making. The proposed IIBT node extends conventional BTs with probabilistic reasoning, enabling online joint planning and execution across multiple robots. It remains fully compatible with standard BT architectures, allowing seamless integration into existing multi-robot control systems. Within this framework, multi-robot cooperation is formulated as a free-energy minimization process, where each robot dynamically updates its preference matrix based on perceptual inputs and peer intentions, thereby achieving adaptive coordination in partially observable and dynamic environments. The proposed approach is validated through both simulation and real-world experiments, including a multi-robot maze navigation and a collaborative manipulation task, compared against traditional BTs(https://youtu.be/KX_oT3IDTf4). Experimental results demonstrate that the IIBT framework reduces BT node complexity by over 70%, while maintaining robust, interpretable, and adaptive cooperative behavior under environmental uncertainty.

Abstract--In robotic hand research, minimizing the number of actuators while maintaining human-hand-consistent dimensions and degrees of freedom constitutes a fundamental challenge. Drawing bio-inspiration from human hand kinematic configurations and muscle distribution strategies, this work proposes a novel 15-DoF dexterous robotic hand, with detailed analysis of its mechanical architecture, electrical system, and control system. The bionic hand employs a new tendon-driven mechanism, significantly reducing the number of motors required by traditional tendon-driven systems while enhancing motion performance and simplifying the mechanical structure. This design integrates five motors in the forearm to provide strong gripping force, while ten small motors are installed in the palm to support fine manipulation tasks. Additionally, a corresponding joint sensing and motor driving electrical system was developed to ensure efficient control and feedback. The entire system weighs only 1.4kg, combining lightweight and high-performance features. Through experiments, the bionic hand exhibited exceptional dexterity and robust grasping capabilities, demonstrating significant potential for robotic manipulation tasks. HE development of actuator systems with human-level dexterity presents significant challenges [1], [2], stemming from the bio-integrated nature of the human hand: it is not an isolated entity but a highly coupled system intricately connected through skeletal-muscular-neural networks to the forearm, forming a synergistic functional unit.

Abstract--Deep neural networks (DNNs) have made significant strides in Natural Language Processing (NLP), yet their interpretability remains elusive, particularly when evaluating their intricate decision-making processes. Traditional methods often rely on post-hoc interpretations, such as saliency maps or feature visualization, which might not be directly applicable to the discrete nature of word data in NLP . Addressing this, we introduce the Model-agnostic Saliency Estimation (MASE) framework. MASE offers local explanations for text-based predictive models without necessitating in-depth knowledge of a model's internal architecture. By leveraging Normalized Linear Gaussian Perturbations (NLGP) on the embedding layer instead of raw word inputs, MASE efficiently estimates input saliency. Our results indicate MASE's superiority over other model-agnostic interpretation methods, especially in terms of Delta Accuracy, positioning it as a promising tool for elucidating the operations of text-based models in NLP . Deep learning models are becoming increasingly prevalent in Natural Language Processing (NLP) systems. V arious efficient deep neural networks (DNNs) have been proposed, ranging from classic models such as the Recurrent Neural Network (RNN) [1] and Long-Short Term Memory (LSTM) [2] to more recent Bidirectional Encoder Representations from Transformers (BERT) [3]. Although these DNNs have shown great success in natural language processing tasks, their lack of explain-ability and/or interpretability remains a major weakness when comparing and deploying these black-box models for NLP tasks such as text classification and resume recommendation. Moreover, as NLP models become increasingly complex with millions or even billions of parameters, explaining the precise decision-making process of a DNN is becoming increasingly challenging.

Fine-grained air pollution forecasting is crucial for urban management and the development of healthy buildings. Deploying portable sensors on mobile platforms such as cars and buses offers a low-cost, easy-to-maintain, and wide-coverage data collection solution. However, due to the random and uncontrollable movement patterns of these non-dedicated mobile platforms, the resulting sensor data are often incomplete and temporally inconsistent. By exploring potential training patterns in the reverse process of diffusion models, we propose Spatio-Temporal Physics-Informed Diffusion Models (STeP-Diff). STeP-Diff leverages DeepONet to model the spatial sequence of measurements along with a PDE-informed diffusion model to forecast the spatio-temporal field from incomplete and time-varying data. Through a PDE-constrained regularization framework, the denoising process asymptotically converges to the convection-diffusion dynamics, ensuring that predictions are both grounded in real-world measurements and aligned with the fundamental physics governing pollution dispersion. To assess the performance of the system, we deployed 59 self-designed portable sensing devices in two cities, operating for 14 days to collect air pollution data. Compared to the second-best performing algorithm, our model achieved improvements of up to 89.12% in MAE, 82.30% in RMSE, and 25.00% in MAPE, with extensive evaluations demonstrating that STeP-Diff effectively captures the spatio-temporal dependencies in air pollution fields.

Our work presents a novel reinforcement learning (RL) based framework to optimize heuristic selection within the conflict-driven clause learning (CDCL) process, improving the efficiency of Boolean satisfia-bility (SAT) solving. The proposed system, LangSAT, bridges the gap between natural language inputs and propositional logic by converting English descriptions into Conjunctive Normal Form (CNF) expressions and solving them using an RL-enhanced CDCL SAT solver. Unlike existing SAT-solving platforms that require CNF as input, LangSAT enables users to input standard English descriptions, making SAT-solving more accessible. The framework comprises two key components: Lang2Logic, which translates English sentences into CNF expressions, and SmartSAT, an RL-based SAT solver. SmartSAT encodes clause-variable relationships as structured graph representations and extracts global features specific to the SAT problem. This implementation provides the RL agent with deeper contextual information, enabling SAT problems to be solved more efficiently. Lang2Logic was evaluated on diverse natural language inputs, processing descriptions up to 450 words. The generated CNFs were solved by SmartSAT, which demonstrated comparable performance to traditional CDCL heuristics with respect to solving time. The combined LangSAT framework offers a more accessible and scalable solution for SAT-solving tasks across reasoning, formal verification, and debugging.

Autonomous landing on sloped terrain poses significant challenges for small, lightweight spacecraft, such as rotorcraft and landers. These vehicles have limited processing capability and payload capacity, which makes advanced deep learning methods and heavy sensors impractical. Flying insects, such as bees, achieve remarkable landings with minimal neural and sensory resources, relying heavily on optical flow. By regulating flow divergence, a measure of vertical velocity divided by height, they perform smooth landings in which velocity and height decay exponentially together. However, adapting this bio-inspired strategy for spacecraft landings on sloped terrain presents two key challenges: global flow-divergence estimates obscure terrain inclination, and the nonlinear nature of divergence-based control can lead to instability when using conventional controllers. This paper proposes a nonlinear control strategy that leverages two distinct local flow divergence estimates to regulate both thrust and attitude during vertical landings. The control law is formulated based on Incremental Nonlinear Dynamic Inversion to handle the nonlinear flow divergence. The thrust control ensures a smooth vertical descent by keeping a constant average of the local flow divergence estimates, while the attitude control aligns the vehicle with the inclined surface at touchdown by exploiting their difference. The approach is evaluated in numerical simulations using a simplified 2D spacecraft model across varying slopes and divergence setpoints. Results show that regulating the average divergence yields stable landings with exponential decay of velocity and height, and using the divergence difference enables effective alignment with inclined terrain. Overall, the method offers a robust, low-resource landing strategy that enhances the feasibility of autonomous planetary missions with small spacecraft.

Contemporary DevSecOps pipelines have to deal with the evolution of security in an ever-continuously integrated and deployed environment. Existing methods,such as rule-based intrusion detection and static vulnerability scanning, are inadequate and unreceptive to changes in the system, causing longer response times and organization needs exposure to emerging attack vectors. In light of the previous constraints, we introduce AutoGuard to the DevSecOps ecosystem, a reinforcement learning (RL)-powered self-healing security framework built to pre-emptively protect DevSecOps environments. AutoGuard is a self-securing security environment that continuously observes pipeline activities for potential anomalies while preemptively remediating the environment. The model observes and reacts based on a policy that is continually learned dynamically over time. The RL agent improves each action over time through reward-based learning aimed at improving the agent's ability to prevent, detect and respond to a security incident in real-time. Testing using simulated ContinuousIntegration / Continuous Deployment (CI/CD) environments showed AutoGuard to successfully improve threat detection accuracy by 22%, reduce mean time torecovery (MTTR) for incidents by 38% and increase overall resilience to incidents as compared to traditional methods. Keywords- DevSecOps, Reinforcement Learning, Self- Healing Security, Continuous Integration, Automated Threat Mitigation