The field of scientific research and analysis is witnessing significant advancements with the integration of multi-agent systems and large language models (LLMs). Recent developments have focused on enhancing the trustworthiness, scalability, and interpretability of LLMs in various applications, including scientific question answering, research idea evaluation, and financial analysis. Notably, the use of multi-agent frameworks has improved the performance and robustness of LLMs in tasks such as retrieval-augmented generation, debate-based reasoning, and citation prediction. Furthermore, the incorporation of reinforcement learning, graph representation learning, and attention-based methods has led to more accurate and reliable results. The trend towards more transparent, explainable, and auditable AI systems is expected to continue, with potential applications in autonomous data science, financial reporting, and educational settings. Noteworthy papers include SQuAI, which presents a scalable and trustworthy multi-agent retrieval-augmented generation framework for scientific question answering, and PokeeResearch, which introduces a 7B-parameter deep research agent built under a unified reinforcement learning framework. ScholarEval is also notable for its retrieval-augmented evaluation framework that assesses research ideas based on soundness and contribution.
Advancements in Multi-Agent Systems and Large Language Models for Scientific Research and Analysis
Sources
SQuAI: Scientific Question-Answering with Multi-Agent Retrieval-Augmented Generation
PokeeResearch: Effective Deep Research via Reinforcement Learning from AI Feedback and Robust Reasoning Scaffold
ScholarEval: Research Idea Evaluation Grounded in Literature
Agree, Disagree, Explain: Decomposing Human Label Variation in NLI through the Lens of Explanations
Prompt Optimization via Retrieved Reasoning Assets and Multi-Agent Analysis
Unleashing Diverse Thinking Modes in LLMs through Multi-Agent Collaboration
Cross-Genre Authorship Attribution via LLM-Based Retrieve-and-Rerank
FinSight: Towards Real-World Financial Deep Research
DeepAnalyze: Agentic Large Language Models for Autonomous Data Science
Coinvisor: An RL-Enhanced Chatbot Agent for Interactive Cryptocurrency Investment Analysis
Lark: Biologically Inspired Neuroevolution for Multi-Stakeholder LLM Agents
Structured Debate Improves Corporate Credit Reasoning in Financial AI
EduAdapt: A Question Answer Benchmark Dataset for Evaluating Grade-Level Adaptability in LLMs
RubiSCoT: A Framework for AI-Supported Academic Assessment
Enterprise Deep Research: Steerable Multi-Agent Deep Research for Enterprise Analytics
A Design Science Blueprint for an Orchestrated AI Assistant in Doctoral Supervision
From Newborn to Impact: Bias-Aware Citation Prediction
Unfair Mistakes on Social Media: How Demographic Characteristics influence Authorship Attribution
An Evaluation of the Pedagogical Soundness and Usability of AI-Generated Lesson Plans Across Different Models and Prompt Frameworks in High-School Physics
AI PB: A Grounded Generative Agent for Personalized Investment Insights
ResearchGPT: Benchmarking and Training LLMs for End-to-End Computer Science Research Workflows
Citation Failure: Definition, Analysis and Efficient Mitigation
RAGRank: Using PageRank to Counter Poisoning in CTI LLM Pipelines