Papers by Dongwook Lee

3 papers
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems (2025.emnlp-industry)

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Challenge: Existing evaluations focus on the correctness of the final output, overlooking inefficient communication and poor coordination contribute to redundant reasoning and higher computational costs.
Approach: They propose a graph-based evaluation framework that analyzes the internal collaboration process by modeling agent interactions as a directed acyclic graph.
Outcome: The proposed framework shows that outcome-only metrics are insufficient for evaluating multi-agent performance on GSM8K.
CascadeDebate: Multi-Agent Deliberation for Cost-Aware LLM Cascades (2026.acl-industry)

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Challenge: Large language models (LLMs) have demonstrated remarkable proficiency across diverse benchmarks, spanning scientific question answering to medical diagnosis tasks.
Approach: They propose to insert multi-agent deliberation directly at each tier’s escalation boundary to enable consensus-driven resolution of ambiguities internally without invoking higher-cost upgrades.
Outcome: The proposed architecture outperforms strong single-model cascades and standalone multi-agent systems across five benchmarks spanning science, medicine, and general knowledge by up to 26.75%.
Still Between Us? Evaluating and Improving Voice Assistant Robustness to Third-Party Interruptions (2026.acl-long)

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Challenge: Recent Spoken Language Models lack the capability to discern Third-Party Interruptions (TPI) from the primary user’s ongoing flow, leaving them vulnerable to contextual failures.
Approach: They propose a dataset with speaker-aware hard negatives to enforce acoustic cue prioritization for interruption handling and a framework to measure the interruption-handling strategy and precise speaker discrimination in deceptive contexts.
Outcome: The proposed framework mitigates semantic shortcut learning while neglecting acoustic signals essential for discerning speaker changes.

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