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.
Maurya et al. (Fri,) studied this question.