4 citations · 4 across the 10 of their papers we have counts for
7 papers · 1 filter
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…
PromptEmbedder: Efficient and Transferable Text Embedding via Dual-LLM Soft Prompting
Yu-Che Tsai, Kuan-Yu Chen, Yuan-Hao Chen +4
Large Language Models (LLMs) have demonstrated remarkable efficacy in text embedding, yet current adaptation methods like LoRA face significant bottlenecks in computational efficie…
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…
MemCollab: Cross-Model Memory Collaboration via Contrastive Trajectory Distillation
Yurui Chang, Yiran Wu, Qingyun Wu +1
LLM agents increasingly rely on memory mechanisms to reuse knowledge from past problem-solving experiences. However, existing methods typically construct memory for a single agent…