ACCD detects coordinated online harm campaigns by combining adaptive causal temporal scoring, semi-supervised account classification, and stability validation. On a filtered Twitter IRA task, it improves account-level coordination screening over correlation and fixed-parameter CCM baselines, while reducing manual labeling and runtime.

Modern edge AI systems rely on dynamic, multi-stage pipelines vulnerable to adversarial attacks. Existing attacks primarily target static tasks and lack adaptability to these complex environments. We propose Hydra, an adversarial attack framework for AI-driven video streaming and edge security services. Hydra integrates three innovations: Multi-Stage Service Disruption (MSSD) that coherently misleads successive pipeline components, Resource-Aware Transferability Enhancement (RATE) for effective attacks under edge constraints, and Meta-Reinforcement Learning for Adaptive Perturbation (MetaRAP) that dynamically tunes attack parameters based on real-time system states. Evaluations on a multimodal medical RAG system demonstrate Hydra’s superior efficacy, achieving up to 75% ASR against specialized retrievers at 𝜖=8/255, a 12% absolute improvement over baselines. Hydra also exhibits strong transferability, defense robustness, and a favorable efficiency trade-off, requiring only 2.1 seconds per adversarial example. This work highlights a potent threat to edge intelligence services, urging development of robust multi-stage defenses.

Large language model (LLM) agents increasingly rely on function calling to act across multi-turn interactions, but compact models remain brittle when tool availability, required parameters, and dialogue constraints change over time. Existing synthetic tool-use data often broadens coverage without targeting the recurring failures of a specific model. We present FUSE, a failure-guided refinement approach that uses the model’s own function-calling errors to synthesize harder training scenarios and then emphasizes difficult examples during supervised fine-tuning. This data-centric design turns diagnostic failures into targeted training signal while remaining simple to apply with standard offline fine-tuning. Experiments on BFCLv3 show that FUSE substantially improves a compact Qwen3-based model across standard and stress categories, including missing functions, missing parameters, and long-context interactions. Evaluation on BFCLv4 provides initial evidence that the same refinement strategy can transfer to selected agentic function-calling tasks.

Coordinated inauthentic behavior (CIB) threatens the integrity of public conversation, yet existing detectors remain unreliable in practice. Most systems measure correlation between account activities, which conflates genuine influence with shared reactions to trending topics. They also apply fixed detection parameters and require extensive manual labeling, so configurations tuned on one campaign rarely transfer to another. We present ACCD (Adaptive Causal Coordination Detection), a framework that treats this problem as memory-guided causal inference. ACCD screens account pairs in three stages that each narrow the candidate set: it retrieves convergent-cross-mapping parameters from a context-indexed memory of past performance to estimate causal influence, concentrates annotation on uncertain accounts through active learning in a semi-supervised classifier whose labels filter the surviving pairs, and validates the remainder with estimators and thresholds chosen from prior experience. On the Twitter Internet Research Agency (IRA) dataset, ACCD improves pair-level F1 from 75.8% to 87.3% over a fixed-parameter causal baseline, reduces manual labeling by 68.3%, and cuts end-to-end detection time by 2.5×. On Reddit coordination traces it reaches 84.7% F1. On synthetic data with known causal structure, it recovers true influence edges at 0.934 area under the ROC curve (AUC) against 0.708 for correlation-based detection, and the gap widens to 0.861 against 0.586 once strong confounding is introduced. These results suggest that adapting causal configurations to detection context provides a practical and efficient screening tool for protecting online public discourse.

Adaptive Bitrate (ABR) decision-making is essential for optimizing Quality of Experience (QoE) in video streaming. Traditional ABR methods typically lack fine-grained content understanding and face challenges in balancing multi-dimensional QoE objectives under real-world edge device constraints. To overcome these limitations, we propose **StreamSense Agent**, a novel framework that reformulates ABR as a sequential decision-making process informed by content and network awareness. Our approach introduces three key innovations: (1) a Dynamic Content-Aware Entity Graph (D-CAEG) for structured video representation, (2) a composite QoE-driven optimization objective integrating perceptual quality, rebuffering, smoothness, and computational cost, and (3) a hierarchical resource-aware architecture enabling real-time control through an efficient lightweight actor. We further adopt an offline-to-online co-adaptation training paradigm with federated learning for continuous adaptation. Since effective ABR control requires the same core competencies as general sequential decision-making—multi-step planning across future video chunks, dynamic information gathering from heterogeneous network signals, and adaptive strategy adjustment under resource constraints—we validate these transferable capabilities on five established benchmarks. Specifically, multi-hop search tasks (BrowseComp, BrowseComp-ZH) mirror the integration of dispersed network-trace and content-complexity signals into a bitrate plan; complex navigation tasks (WebWalkerQA) correspond to sequential buffer-state traversal under volatile bandwidth; and information-extraction tasks (Xbench-DS, SimpleQA) reflect the precision required when reading real-time streaming telemetry. StreamSense Agent achieves state-of-the-art results, attaining accuracy of 16.7% on BrowseComp, 30.5% on BrowseComp-ZH, 42.1% on Xbench-DS, and 54.2% on WebWalkerQA. Deployment-oriented evaluations further confirm its computational efficiency, achieving an inference throughput of 12.5 requests per second with a memory footprint of only 2,100 MB under edge-like constraints, yielding a normalized QoE score of 0.92. Comprehensive ablation studies validate the necessity of each proposed component. This work establishes a new paradigm for intelligent, edge-deployable bitrate adaptation.

Intelligent workflows for smart manufacturing reliability and maintenance (RxM) are transitioning from static, sequential multi-agent systems toward autonomous, dynamic decision-making. However, a critical gap persists between agent coordination and the holistic co-optimization required across predictive performance, computational resources, and domain-specific business logic. To address this, we propose AgiSphere-CORE, a novel framework centered on a Co-Optimization Core (COC). The COC maintains a unified Collaborative State Vector and solves a constrained optimization problem to maximize the Collaborative Utility J_co, dynamically orchestrating specialized agents for resources, models, and logic. This enables a paradigm shift from sequential to co-optimization-driven autonomy. Extensive evaluation on multiple manufacturing tasks, including classification, regression, and anomaly detection, demonstrates that AgiSphere-CORE consistently outperforms baselines, achieving superior predictive performance (e.g., 97.3% accuracy, 0.902 F1-score), improved resource efficiency (15% faster workflow time), and higher estimated domain cost savings ($148.7K). Ablation studies confirm the critical role of the COC and the adaptive learning mechanism. A detailed case study further illustrates the framework’s transparent, utility-driven trade-offs. We conclude that AgiSphere-CORE provides a robust, holistic foundation for next-generation autonomous RxM systems in smart manufacturing.