Bibliographic record
Abstract
Artificial Intelligence (AI) has come in with highly intelligent systems that continuously do ever more complex tasks. The proposed research relates to one of the newer paradigms in AI research, Agentic AI, which can be understood as autonomous, self-directed software agents that can execute goal-driven behavior via multimodal reasoning. This paper explores the design, construction, and deployment of Agentic Artificial Intelligence systems capable of synthesizing information across different modalities, including text, images, audio, and environment monitoring sensors, so as to generate intelligent autonomous choices. The main deliverable of the research is the development of a framework, which combines multimodal mechanisms of reasoning with agent-based architectures, and allows adaptive and context-sensitive behavior. To address this problem, we postulate a modular architecture that integrates the ability to learn fast and enough through reinforcement learning and profound associations throughout symbolic reasoning in this paper to effectuate decision-making in a real-time scenario and learning in a challenging arena. Our literature review is extensive and follows the development of autonomy in AI systems, the purpose of multimodal reasoning and issues in integration. The approach we use presents a layered model, which consists of perception, cognition, and action modules that accomplish specific tasks and communicate with each other using a common knowledge base. Our prototype system has been tested on various benchmarking scenarios, including navigation, task planning, and multi-agent coordination. Experience indicates a significant increase in task completion rate, awareness of context, and learning efficiency compared to unimodal and static AI agents. The paper concludes with a discussion of the ethical implications, limitations, and future trends of developing generalizable, safe, and socially agreeable autonomous agents. The study aims to develop agents that not only act intelligently but also learn and respond to new circumstances in intelligent ways
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.009 |
| Scholarly communication | 0.012 | 0.012 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".