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.

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.

Recent advancements in the application of Large Language Models (LLMs) to formal theorem proving in Lean have unveiled new prospects in complex logical reasoning scenarios. Acknowledging that the difficulty across reasoning steps in a formal theorem proof is not uniform, prior proof search algorithms have not systematically investigated which proof steps are more crucial to explore. This paper introduces Entropy-Based Best-First Search (EBFS), an entropy-inspired heuristic tree search algorithm that enhances the efficiency of proof generation models to explore proof trees, thereby increasing the probability of finding the correct proof. We also introduce a reinforcement fine-tuning approach specifically for proof tree search. Using EBFS during the generation stage further boosts the model’s performance in proof generation. Evaluations demonstrate that our fine-tuned EBFS model achieves consistent improvements over the baseline models. Under the same sample budget, it outperforms whole proof generation by 3.2% on the ProofNet testset, with further gains of 1.9% on MiniF2F and 1.5% on ProverBench.