Intelligence-Based Active Inference for Robots with Multimodal Large Language Models and Edge Computing
Bibliographic record
Abstract
With the rapid development of artificial intelligence technologies, multimodal large language models (MLLMs) have demonstrated outstanding performance in handling various complex tasks. Robots, which are crucial for automated production, rely on path planning for efficiency and precision. Thus, applying MLLMs to the path planning of robots is a natural technological progression. However, the limited hardware in robots poses challenges for efficient and timely MLLMs-based inference. While traditional deep reinforcement learning (DRL) methods can offload these inference tasks to servers, existing DRL solutions often suffer from low data efficiency and are insensitive to users’ demands. To address these issues, this paper introduces an intelligence-based active inference (IAI) approach for offloading inference tasks and optimizing resource allocation in cloud-edge networks. Experimental results demonstrate that our proposed method outperforms mainstream DRL approaches in terms of data efficiency and better accommodates user dynamic requirements.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".