Daye Kang

Daye Kang, Hyeongboo Baek

The primary tools used to monitor and defend object detectors under adversarial attack assume that when accuracy degrades, detection count drops in tandem. This coupling was assumed, not measured. We report a counterexample observed on a single model: under standard PGD, EMS-YOLO, a spiking neural network (SNN) object detector, retains more than 70% of its detections while mAP collapses from 0.528 to 0.042. We term this count-preserving accuracy collapse Quality Corruption (QC), to distinguish it from the suppression that dominates untargeted evaluation. Across four SNN architectures and two threat models (l-infinity and l-2), QC appears only in one of the four detectors tested (EMS-YOLO). On this model, all five standard defense components fail to detect or mitigate QC, suggesting the defense ecosystem may rely on a shared assumption calibrated on a single substrate. These results provide, to our knowledge, the first evidence that adversarial failure modes can be substrate-dependent.

Wonduk Seo, Seungyong Lee, Daye Kang et al.

Rapid advancements in Large Language Models (LLMs) have accelerated their integration into automated visualization code generation applications. Despite advancements through few-shot prompting and query expansion, existing methods remain limited in handling ambiguous and complex queries, thereby requiring manual intervention. To overcome these limitations, we propose VisPath: a Multi-Path Reasoning and Feedback-Driven Optimization Framework for Visualization Code Generation. VisPath handles underspecified queries through structured, multi-stage processing. It begins by reformulating the user input via Chain-of-Thought (CoT) prompting, which refers to the initial query while generating multiple extended queries in parallel, enabling the LLM to capture diverse interpretations of the user intent. These queries then generate candidate visualization scripts, which are executed to produce diverse images. By assessing the visual quality and correctness of each output, VisPath generates targeted feedback that is aggregated to synthesize an optimal final result. Extensive experiments on widely-used benchmarks including MatPlotBench and the Qwen-Agent Code Interpreter Benchmark show that VisPath outperforms state-of-the-art methods, offering a more reliable solution for AI-driven visualization code generation.

Hyunjn An, Yongwon Kim, Wonduk Seo et al.

While many tools are available for designing AI, non-experts still face challenges in clearly expressing their intent and managing system complexity. We introduce AIAP, a no-code platform that integrates natural language input with visual workflows. AIAP leverages a coordinated multi-agent system to decompose ambiguous user instructions into modular, actionable steps, hidden from users behind a unified interface. A user study involving 32 participants showed that AIAP's AI-generated suggestions, modular workflows, and automatic identification of data, actions, and context significantly improved participants' ability to develop services intuitively. These findings highlight that natural language-based visual programming significantly reduces barriers and enhances user experience in AI service design.