Yuhan Suo

Yuxin Meng, Yuhan Suo, Junjie Wang et al.

Existing benchmarks for MLLM-generated web artifacts assess interaction through local evidence and miss the requirement-induced states and transitions that determine whether a page works. We introduce WebRISE, which compiles task requirements into Interaction Contract Graphs (ICGs) of observable states, user-intent transitions, and DOM/visual assertions for implementation-agnostic browser execution. WebRISE spans 442 tasks across five input modalities (Text, Markdown, Sketch, Image, Video), with 5,495 transitions and 5,271 requirement checks that separate user-stated functions from implicit product-level constraints. Across 14 MLLMs, even the strongest model reaches only 65.6% transition validity and 66.3% requirement coverage, and visual quality is no proxy for behavior (Qwen3.6-35B-A3B on Markdown: V=80.8 yet T=15.5). Video gives the strongest interaction signal (+10.6 pp implicit coverage over Text), while implicit constraints persist; defect injection shows ICG-based scoring detects state errors at 2-16x the rate of checkpoint-style evaluation.

Yuhan Suo, Runqi Chai, Kaiyuan Chen et al.

With the rapid development of the Internet of Things (IoT), the risks of data tampering and malicious information injection have intensified, making efficient threat detection in large-scale distributed sensor networks a pressing challenge. To address the decline in malicious information detection efficiency as network scale expands, this paper investigates a robust set partitioning strategy and, on this basis, develops a distributed attack detection framework with theoretical guarantees. Specifically, we introduce a gain mutual influence metric to characterize the inter-subset interference arising during gain updates, thereby revealing the fundamental reason for the performance gap between distributed and centralized algorithms. Building on this insight, the set partitioning strategy based on Grassmann distance is proposed, which significantly reduces the computational cost of gain updates while maintaining detection performance, and ensures that the distributed setting under subset partitioning preserves the same theoretical performance bound as the baseline algorithm. Unlike conventional clustering methods, the proposed set partitioning strategy leverages the intrinsic observational features of sensors for robust partitioning, thereby enhancing resilience to noise and interference. Simulation results demonstrate that the proposed method limits the performance gap between distributed and centralized detection to no more than 1.648$\%$, while the computational cost decreases at an order of $O(1/m)$ with the number of subsets $m$. Therefore, the proposed algorithm effectively reduces computational overhead while preserving detection accuracy, offering a practical low-cost and highly reliable security detection solution for edge nodes in large-scale IoT systems.

Kaiyuan Chen, Yuhan Suo, Shaowei Cui et al.

This paper addresses the problem of trajectory optimization for unmanned aerial vehicles (UAVs) performing time-sensitive medical deliveries in urban environments. Specifically, we consider a single UAV with 3 degree-of-freedom dynamics tasked with delivering blood packages to multiple hospitals, each with a predefined time window and priority. Mission objectives are encoded using Signal Temporal Logic (STL), enabling the formal specification of spatial-temporal constraints. To ensure safety, city buildings are modeled as 3D convex obstacles, and obstacle avoidance is handled through a Convex Feasible Set (CFS) method. The entire planning problem-combining UAV dynamics, STL satisfaction, and collision avoidance-is formulated as a convex optimization problem that ensures tractability and can be solved efficiently using standard convex programming techniques. Simulation results demonstrate that the proposed method generates dynamically feasible, collision-free trajectories that satisfy temporal mission goals, providing a scalable and reliable approach for autonomous UAV-based medical logistics.

Yuhan Suo, Senchun Chai, Xudong Zhao et al.

Distributed online learning in multi-agent systems is highly vulnerable to adversarial influence, especially when malicious agents cannot be fully isolated during the transient stage. While existing studies mainly pursue resilient consensus or secure fusion, they pay much less attention to the learning inefficiency and extra evolution cost accumulated during the defense process. This paper addresses this gap by developing a cost-aware distributed online learning framework with strict rejection behavior against adversarial agents.Under this mechanism, the state evolution cost of online adaptation is formulated and the cost amplification effect caused by adversarial interactions is theoretically characterized. To balance robustness, convergence efficiency, and long-term cost, we propose an adaptive adjustment mechanism for the state-evolution rate. The resulting outer-layer update can be equivalently viewed as a constrained online optimization problem. We further establish the well-posedness and regularity of the associated periodic Riccati layer, and show that the outer-layer update ensures feasibility and controlled variation. Based on these properties, closed-loop practical stability is rigorously established via a two-time-scale Lyapunov framework. Simulations demonstrate that the proposed method achieves robust and low-cost convergence under adversarial disturbances. Furthermore, a multi-satellite target tracking scenario with malicious interference further demonstrates the practical effectiveness of the strict rejection behavior.