Most Influential ArXiv (Artificial Intelligence) Papers (2025-09 Version)

2025-09-20 · Source: Artificial Intelligence – Resources | Paper Digest · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems, Emerging Technologies & Innovation · Depth: Advanced, extended

Summary

Paper Digest Team has released the "Most Influential ArXiv (Artificial Intelligence) Papers (2025-09 Version)" list, updated on September 23, 2025. This compilation identifies up to 30 top papers annually from the arXiv AI field, excluding Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language, which have dedicated subject areas. The ranking is automatically generated based on citations from both research papers and granted patents. Key papers from 2025 include "Kimi K1.5: Scaling Reinforcement Learning with LLMs" (IF:6) and "Janus-Pro: Unified Multimodal Understanding and Generation with Data and Model Scaling" (IF:5). The list also features influential works from previous years, such as "The Llama 3 Herd of Models" (2024, IF:8) and "A Survey on Large Language Model Based Autonomous Agents" (2023, IF:8). The Paper Digest platform offers AI-powered tools for reading, writing, answering, literature reviews, and research report generation.

Key takeaway

AI Researchers and Scientists focusing on Large Language Models and multi-agent systems should prioritize understanding the latest advancements in reasoning, multimodal integration, and reinforcement learning techniques. Your research should also address the critical aspects of trustworthiness and explainability, as these are increasingly vital for the practical deployment and societal acceptance of AI. Explore the cited papers to identify emerging methodologies and potential collaboration opportunities.

Key insights

The arXiv AI field is rapidly advancing, with a strong focus on LLM reasoning, multi-agent systems, and trustworthy AI.

Principles

• Influence is measured by citations from both research papers and granted patents.
• AI development increasingly emphasizes reasoning capabilities and multi-agent collaboration.
• Trustworthiness and explainability are critical concerns in AI system design.

Method

Paper Digest Team automatically constructs rankings using citation analysis from research papers and patents, focusing on specific arXiv AI sub-fields.

In practice

• Explore recent papers on LLM reasoning and multi-agent systems for current trends.
• Utilize platforms like Paper Digest for AI-powered research assistance.
• Consider citation impact from patents as a measure of real-world influence.

Topics

• Large Language Models
• AI Agents
• Reinforcement Learning
• Reasoning
• Explainable AI

Code references

• mathllm/math-v
• deepseek-ai/janus
• terrierteam/pyterrier_rag
• likaixin2000/screenspot-pro-gui-grounding
• zzli2022/awesome-slow-reason-system

Best for: AI Researcher, AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence – Resources | Paper Digest.