This page shows the most relevant public items for al:agentic-ai-2025, ranked by trend activity and review signal. Use weekly for fast changes, monthly for more stable patterns, and all-time for evergreen picks.
Paper • Nov 18, 2025 • arXiv • Ling Chen, David Wu, Yong Zhang
The migration of legacy codebases (e.g., COBOL, Fortran) to modern architectures is a notoriously slow, error-prone, and expensive human endeavor. We introduce 'AutoMigrate,' a persistent, multi-ag...
Paper • Apr 18, 2025 • arXiv • Maciej Besta, Nils Blach, Torsten Hoefler
While Chain-of-Thought and Tree-of-Thoughts prompting greatly enhance LLM reasoning, they strictly enforce linear or hierarchical cognitive paths. We introduce Graph of Thoughts (GoT), a novel cogn...
Paper • Sep 10, 2025 • arXiv • Yao Mu, Tianyu Zheng, Percy Liang, Dan Hendrycks
As multi-agent swarms are deployed in open-ended web environments, standard static benchmarks fail to capture their vulnerability to dynamic threats and deceptive actors. We introduce AgentBench-20...
Paper • Sep 14, 2025 • arXiv • Michael C. Brooks, Anita Desai, Francis Nguyen
As Agentic AI systems assume more responsibility in high-stakes domains, calibrating human trust remains a critical challenge. Under-trust leads to inefficient micromanagement, while over-trust res...
Paper • Jul 15, 2025 • arXiv • Kevin Zhu, Lara Croft, Julian Bao
Evaluating the long-horizon planning and adaptability of Agentic AI in the real world is fraught with safety and cost limitations. We establish Minecraft as the premier sandbox for open-ended agent...
Paper • Aug 8, 2025 • arXiv • Jian Lu, Percy Liang, Chelsea Finn
While scaling laws for single-model LLMs are well established, the relationship between the number of collaborating agents and overall system performance remains poorly understood. This paper inves...
Paper • Dec 5, 2025 • arXiv • Alessandro V. Rossi, Elena M. K., Yong Lin
The delegation of high-stakes financial and infrastructural tasks to Agentic AI requires robust mechanisms for verifying agent behavior without exposing sensitive underlying data. This paper propos...
Paper • Nov 5, 2025 • arXiv • Chen Wei, Marcus T. Holden, Elara Vance
The rapid escalation of automated cyber attacks has outpaced human response times, necessitating a shift toward autonomous defense mechanisms. This paper introduces an Agentic AI framework for cont...
Paper • Nov 20, 2025 • arXiv • Dr. Yannick L., Sophia M., Wei C.
As Agentic AI swarms scale to thousands of interacting nodes driven by frontier language models, unexpected macro-level dynamics arise. We deploy a multi-agent simulation simulating a digital econo...
Paper • Mar 21, 2025 • arXiv • Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Kuhu Syal
Automated theorem proving requires a blend of creative intuition and rigorous logical deduction, a combination that eludes pure deep learning models. We propose a Neurosymbolic Agentic AI framework...
Paper • Aug 25, 2025 • arXiv • Alexander C. K.
As Agentic AI systems are granted read/write access to production databases, cloud infrastructures, and financial APIs, the risk of catastrophic runaway actions increases exponentially. This paper ...
Paper • Oct 29, 2025 • arXiv • Mohamad Abou Ali, Fadi Dornaika
Agentic AI represents a transformative shift in artificial intelligence, but its rapid advancement has led to a fragmented understanding, often conflating modern neural systems with outdated symbol...
Paper • Dec 22, 2025 • arXiv • Percy Liang, Thomas K. V., Eleanor Rigby
Aligning multi-agent systems via traditional human feedback is intractable due to the sheer volume and speed of agent-to-agent interactions. We introduce a novel alignment framework utilizing Inver...
Paper • Apr 11, 2025 • arXiv • Minh N. Nguyen, Sarah J. Conner, Wei Lin
Standard Retrieval-Augmented Generation (RAG) relies on static, single-pass semantic searches that often fail on complex, multi-hop queries. We introduce Agentic-RAG, an autonomous framework where ...
Paper • Oct 21, 2025 • arXiv • Thomas K. V., Yiming Zhang, Nils Bergmann
The integration of decentralized renewable energy sources and the massive power demands of AI datacenters have created unprecedented volatility in modern power grids. We introduce 'Energy Swarm', a...
Paper • Apr 16, 2025 • arXiv • Yannick LeCun, Antoine Bordes, Elena Rossi, Marcus Thorne
The propensity of large language models to hallucinate facts severely limits their deployment in autonomous agentic workflows where verification is costly. This paper introduces 'Agentic Debate,' a...
Paper • Jun 21, 2025 • arXiv • Thomas K. V., Eleanor Rigby, M. Hasan
Traditional AI alignment techniques like RLHF are insufficient for Agentic AI, as autonomous systems frequently encounter novel edge cases in open-ended environments that were absent from their tra...
Paper • Dec 18, 2025 • arXiv • Anonymous Consortium
As agentic AI systems grow in capability and scope, adaptation becomes a central mechanism for improving performance, reliability, and generalization. This paper unifies the rapidly expanding resea...
Paper • May 15, 2025 • arXiv • Ranjan Sapkota, Konstantinos I. Roumeliotis, Manoj Karkee
This review critically distinguishes between traditional 'AI Agents' and the broader paradigm of 'Agentic AI', offering a structured conceptual taxonomy and application mapping. The authors argue t...
Paper • Dec 1, 2025 • arXiv • Susan Athey, Michael I. Jordan
As Agentic AI systems are deployed to autonomously negotiate contracts, manage supply chains, and execute high-frequency trades, they form a complex, decentralized digital economy. This paper appli...
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