Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it