Papers by Chunyu Yang

5 papers
Failures are Treasures: Constructing a Pedagogical Bridge for Agentic Strategy Distillation (2026.findings-acl)

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Challenge: Existing knowledge distillation methods focus on imitating successful trajectories, whereas small language models are fragile and often collapsing after encountering errors.
Approach: They propose a Pedagogical Bridge for Reflective Insight and Distillation of Guiding Errors that combines reflection-in-action and reflection-on-action to enable agents to diagnose and correct critical errors while abstracting transferable strategies from contrastive student–teacher trajectories.
Outcome: Experiments show that the proposed model significantly elevates performance in large language models (SLMs) .
Decoding the Multimodal Mind: Generalizable Brain-to-Text Translation via Multimodal Alignment and Adaptive Routing (2026.findings-acl)

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Challenge: Current approaches to decoding language from the human brain rely on unimodal representations, neglecting the brain’s inherently multimodal processing.
Approach: They propose a framework that leverages Multimodal Large Language Models to align brain signals with a shared semantic space encompassing text, images, and audio.
Outcome: The proposed framework achieves an 8.48% improvement on the most commonly used benchmark on fMRI datasets with textual, visual, and auditory stimuli.
Evaluating the Expressive Appropriateness of Speech in Rich Contexts (2026.acl-long)

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Challenge: Existing methods for evaluating expressive speech focus on word accuracy, naturalness, signal quality, or emotional intensity at the utterance level.
Approach: They propose a framework for Evaluating Expressive Appropriateness in speech that assesses whether a speech sample aligns with the underlying communicative intent implied by its discourse-level narrative context.
Outcome: The proposed framework outperforms existing speech evaluation and analysis systems on a human-annotated test set.
COSMOS: Connectivity-Oriented Submodular Maximization for Optimal Subgraph Retrieval (2026.acl-long)

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Challenge: Existing paradigms treat facts independently or employ myopic search, failing to optimize collective subgraph utility.
Approach: They propose a framework that formalizes evidence retrieval as a constrained submodular maximization problem.
Outcome: The proposed framework captures the trade-off between information relevance and structural complexity.
Unveil: Unified Visual-Textual Integration and Distillation for Multi-modal Document Retrieval (2025.acl-long)

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Challenge: Document retrieval in real-world scenarios faces significant challenges due to diverse document formats and modalities.
Approach: They propose a visual-textual embedding framework that integrates textual and visual features for robust document representation.
Outcome: The proposed visual-textual embedding framework surpasses existing methods while preserving semantic fidelity.

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