Papers by Dongwook Kwon

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%.
PAC-BENCH: Evaluating Multi-Agent Collaboration under Privacy Constraints (2026.findings-acl)

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Challenge: Recent research explores multi-agent systems where agents collaborate toward shared goals to handle complex tasks.
Approach: They propose a benchmark for systematic evaluation of multi-agent collaboration under privacy constraints.
Outcome: The proposed benchmark shows that privacy constraints degrade collaboration performance and make outcomes depend more on the initiating agent than the partner.

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