Abstract
Large Language Models (LLMs) have significantly advanced natural language processing, yet they remain limited in executing structured, real-world tasks autonomously. This paper introduces the Agentic Language Model (ALM), a novel AI paradigm developed by RecoilLife TenseAI that shifts intelligence from token prediction to task execution. ALM is built upon the Agentic Reinforced Operational Workflow (AROW) and trained using Per Agentic Task (PAT) units, representing complete task lifecycles. With a dataset of approximately 1.8 million PATs, ALM demonstrates improved task completion accuracy, reduced hallucination, and enhanced decision-making. This work presents the architecture, training methodology, evaluation, and future implications of ALM as a foundation for autonomous AI systems.
Keywords
Agentic AI, Autonomous Systems, Task Execution, Reinforcement Learning, Language Models, Workflow Intelligence
Citations
IRE Journals:
Ayush Maurya, Deependra B. Maurya "Agentic Language Model (ALM): A Task-Centric Framework for Autonomous AI Systems" Iconic Research And Engineering Journals Volume 9 Issue 10 2026 Page 1774-1777
IEEE:
Ayush Maurya, Deependra B. Maurya
"Agentic Language Model (ALM): A Task-Centric Framework for Autonomous AI Systems" Iconic Research And Engineering Journals, 9(10)
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