Papers with multi-agent collaborative framework

3 papers
Helios: A Foundational Language Model for Smart Energy Knowledge Reasoning and Application (2026.eacl-long)

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Challenge: Enersys is a collaborative framework for end-to-end dataset construction that combines a large-scale pretraining, SFT, and RLHF datasets to improve performance.
Approach: They propose a large language model tailored to the smart energy domain and a collaborative framework to advance LLM research in this field.
Outcome: The proposed model improves domain knowledge mastery, task execution accuracy, and alignment with human preferences.
RL4F: Generating Natural Language Feedback with Reinforcement Learning for Repairing Model Outputs (2023.acl-long)

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Challenge: Despite their success, even the largest language models make mistakes.
Approach: They propose a framework where one language model can generate critiques to improve its peer's performance.
Outcome: The proposed framework improves the performance of a fixed model 200 times its size by 10% over other models.
Simulating Classroom Education with LLM-Empowered Agents (2025.naacl-long)

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Challenge: Initial studies have focused on task-specific, independent LLM-empowered agents, but the potential of LLMs within a multi-agent collaborative framework for classroom simulation with real user participation remains unexplored.
Approach: They propose a multi-agent classroom simulation teaching framework that recognizes representative class roles and introduces a novel class control mechanism for automatic classroom teaching.
Outcome: The proposed framework can simulate dynamic learning environment for users with active teacher-student and student-studente interactions.

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