A Multi-Agent Human-LLM Collaborative Framework for Closed-Loop Scientific Literature Summarization

arXiv:2604.01452 · cs.AI · Submitted 2026-04-01 · Read on arXiv

cs.AI

Submitted: 2026-04-01

Updated: 2026-08-31

Terminology

• Hallucination — 20× in this paper · explained in 3 episodes
• Iterative Consensus Scoring — 6× in this paper
• Transformer — 6× in this paper · explained in 2 episodes
• Verification — 6× in this paper · explained in 2 episodes
• Iterative Refinement — 3× in this paper · explained in 3 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Clustering — 2× in this paper · explained in 2 episodes
• Consistency — 2× in this paper · explained in 3 episodes
• Hallucination Detection — 2× in this paper · explained in 2 episodes
• Human-in-the-Loop — 2× in this paper · explained in 3 episodes
• Large Language Models (LLMs) — 2× in this paper · explained in 6 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Ablation study — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Attention Mechanism — 1× in this paper · explained in 3 episodes
• Closed-Loop System — 1× in this paper · explained in 4 episodes
• Hallucination Mitigation — 1× in this paper · explained in 2 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Knowledge Graph — 1× in this paper · explained in 7 episodes
• Multi-Agent System — 1× in this paper · explained in 6 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes
• Structural Integrity — 1× in this paper · explained in 3 episodes
• Symbolic Regression — 1× in this paper · explained in 5 episodes
• Tokenization — 1× in this paper · explained in 2 episodes
• Transmission Electron Microscope — 1× in this paper

Sources

• Large Language Models Can Self-Improve
• A Comparative Study of Transformer-Based Language Models on Extractive Question Answering
• PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
• Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering

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