Cooperative Agents that Adapt for Seamless Messaging in Heterogeneous Communication Networks
Bibliographic record
Abstract
This paper describes the difficult problem of seamless messaging in heterogeneous communication environments and the design of cooperative agents to address the problem. The seamless messaging agents include coarse-grained agents such as user agents, message transfer agents, server agents (for resources such as speech recognition and generation), application agents (interfacing to mail applications such as Microsoft and Unix mail) and device agents (for wireless phones, laptops, etc.). It also includes fine-grained agents, namely, user surrogate agents. Seamless messaging requires real-time performance which necessitates a tradeoff between agent communication, adaptability and intelligence to achieve a variety of tasks. In addition, seamless messaging requires user interface mediation between users and device agents to allow, for example, messages sent by users from a powerful graphic workstation to be received transparently by a cellular phone. 1. Introduction Today's intensive comm...
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".