9 papers
DecisionRobust: Benchmarking Robustness for JEV Style Decision Models Across Text and Vision
Yifan Lan, Zhongye Liu, Yurui Chang +4
Emerging decision models such as JEV provide typed interfaces that map task instructions and observations directly to choices, judgments, and scores. As these models are increasing…
From Narrative to Auditable Forecasts: A Structured Scaffold for Agentic Forecasting
Yuanpu Cao, Yongkang Du, Yurui Chang +2
LLM agents are increasingly used for live forecasting, where they retrieve up-to-date information and produce estimates for unresolved future events. However, current agentic forec…
Critic Experience Bank: Self-Evolving Step-Level Confidence Estimation for LLM Agents
Yaopei Zeng, Congchao Wang, JianHang Chen +3
LLM agents operate in stateful environments, where a single erroneous step can waste limited interaction budget or cause irreversible effects before task failure becomes apparent.…
Forced Deferral: Manipulating Routing Decisions in Multimodal LLM Cascades
Zhongye Liu, Yaopei Zeng, Yurui Chang +1
While multimodal large language models (MLLMs) have shown strong visual reasoning abilities, serving a large model for every query is computationally expensive. MLLM cascades mitig…
ForecastCompass: Guiding Agentic Forecasting with Adaptive Factor Memory
Yurui Chang, Yongkang Du, Yuanpu Cao +2
Agentic forecasting is important for decision-making in dynamic environments, but it remains challenging because agents must reason from incomplete, time-limited evidence and produ…
Exposing Vulnerabilities in Explanation for Time Series Classifiers via Dual-Target Attacks
Bohan Wang, Zewen Liu, Lu Lin +4
Interpretable time series deep learning systems are often assessed by checking temporal consistency on explanations, implicitly treating this as evidence of robustness. We show tha…
Monitoring Decoding: Mitigating Hallucination via Evaluating the Factuality of Partial Response during Generation
Yurui Chang, Bochuan Cao, Lu Lin
While large language models have demonstrated exceptional performance across a wide range of tasks, they remain susceptible to hallucinations -- generating plausible yet factually…
AdvI2I: Adversarial Image Attack on Image-to-Image Diffusion models
Yaopei Zeng, Yuanpu Cao, Bochuan Cao +3
Recent advances in diffusion models have significantly enhanced the quality of image synthesis, yet they have also introduced serious safety concerns, particularly the generation o…
Score-based Conditional Out-of-Distribution Augmentation for Graph Covariate Shift
Bohan Wang, Yurui Chang, Wei Jin +1
Distribution shifts between training and testing datasets significantly impair the model performance on graph learning. A commonly-taken causal view in graph invariant learning sug…