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Qi Chai

4 accepted papers

2026

Interpreting Fedspeak with Confidence: A LLM-Based Uncertainty-Aware Framework Guided by Monetary Policy Transmission Paths

AAAI 2026technical

"Fedspeak", the stylized and often nuanced language used by the U.S. Federal Reserve, encodes implicit policy signals and strategic stances. The Federal Open Market Committee strategically employs Fedspeak as a communication tool to shape market expectations and influence both domestic and global e

Cited by 0SourcePDFScholar
2025

CausalMACE: Causality Empowered Multi-Agents in Minecraft Cooperative Tasks

EMNLP 2025

Minecraft, as an open-world virtual interactive environment, has become a prominent platform for research on agent decision-making and execution. Existing works primarily adopt a single Large Language Model (LLM) agent to complete various in-game tasks. However, for complex tasks requiring lengthy s

2025

Debate on Graph: A Flexible and Reliable Reasoning Framework for Large Language Models

AAAI 2025technical

Large Language Models (LLMs) may suffer from hallucinations in real-world applications due to the lack of relevant knowledge. In contrast, knowledge graphs encompass extensive, multi-relational structures that store a vast array of symbolic facts. Consequently, integrating LLMs with knowledge graphs…

2025

VistaWise: Building Cost-Effective Agent with Cross-Modal Knowledge Graph for Minecraft

EMNLP 2025

Large language models (LLMs) have shown significant promise in embodied decision-making tasks within virtual open-world environments. Nonetheless, their performance is hindered by the absence of domain-specific knowledge. Methods that finetune on large-scale domain-specific data entail prohibitive d