Papers by Dongjie Fu

3 papers
Blind Spot Navigation in Large Language Model Reasoning with Thought Space Explorer (2026.findings-eacl)

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Challenge: Existing studies show that large language models have strong reasoning capabilities through chain-structured methods.
Approach: They propose a framework for navigating and expanding thought structures to overcome blind spots in LLM reasoning.
Outcome: The proposed framework overcomes blind spots in large language models by expanding thought structures . the proposed framework improves accuracy of the final answer and intermediate reasoning steps .
PACHAT: Persona-Aware Speech Assistant for Multi-party Dialogue (2025.emnlp-main)

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Challenge: Extensive research on spoken dialogue systems has advanced the development of intelligent voice assistants, but integration of role information within speech remains an underexplored area.
Approach: They propose a language-based spoken dialogue system that integrates role information within speech to generate contextually appropriate responses.
Outcome: The proposed architecture achieves speaker-specific responses, character understanding, and the generation of targeted replies in multi-party dialogue scenarios, surpassing existing spoken dialogue systems.
Rectifying the Emotional Flow: Aligning Priors and Dynamic Guidance for High-Arousal Text-to-Speech (2026.acl-long)

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Challenge: Existing systems suffer from linguistic collapse when pursuing high intensity or fail to meet target emotional levels.
Approach: They propose an inference framework that introduces a neutral prosody bias and a uniform Classifier-Free Guidance that distorts the acoustic manifold, leading to artifacts.
Outcome: The proposed framework achieves superior linguistic accuracy and expressiveness without model retraining.

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