Papers with collective
Towards Effective and Efficient Multi-Agent Language Model Systems: Foundations, Prospects, and Applications (2026.acl-tutorials)
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| Challenge: | Multi-agent systems powered by large language models still face challenges . tutorial focuses on three core components to build effective and efficient systems . |
| Approach: | This tutorial introduces recent advances in building effective and efficient multi-agent LLM systems . it focuses on three core components: model distillation, dynamic routing, memory- and compute efficient serving . |
| Outcome: | This tutorial introduces state-of-the-art techniques for building efficient and efficient multi-agent LLM systems . it covers coordination and communication among agents, crucial for collective performance . |
The Power of Many: Multi-Agent Multimodal Models for Cultural Image Captioning (2025.naacl-long)
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| Challenge: | Large Multimodal Models exhibit impressive performance across multimodal tasks . effectiveness in cross-cultural contexts limited due to predominantly Western-centric nature of data and models . multi-agent models have shown significant capability in solving complex tasks despite limitations in crosscultural context . |
| Approach: | They propose to use a multi-agent framework to enhance cross-cultural image captioning using LMMs with distinct cultural personas to evaluate cultural information within image captions. |
| Outcome: | The proposed model outperforms single-agent models across different metrics and offers valuable insights for future research. |
Investigating Pedagogical Teacher and Student LLM Agents: Genetic Adaptation Meets Retrieval-Augmented Generation Across Learning Styles (2025.emnlp-main)
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Debdeep Sanyal, Agniva Maiti, Umakanta Maharana, Dhruv Kumar, Ankur Mali, C. Lee Giles, Murari Mandal
| Challenge: | Existing models for large language models neglect comprehensive student modeling beyond basic knowledge states and lack mechanisms for teachers to dynamically adapt their approach based on student feedback and collective performance. |
| Approach: | They propose a framework that integrates LLM-based diverse student agents with a self-evolving teacher agent to optimize teacher's pedagogical parameters based on simulated student performance. |
| Outcome: | The proposed framework integrates diverse student agents with a self-evolving teacher agent to optimize teacher pedagogical parameters based on simulated student performance. |