Improving fault-tolerance by replicating agents
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
Despite the considerable efforts spent on developing multi-agent systems the actual number of deployed systems is surprisingly small. One of the reasons for the significant gap between developed and deployed systems is their brittleness.The absence of centralized control components makes it difficult to detect and treat failures of individual agents thus risking fault-propagation that can seriously impact the performance of the system. Using redundancy by replication of individual agents within a multi-agent system is one possible approach for improving fault-tolerance. Unfortunately the introduction of replicates leads to increased complexity and system load. In this paper we examine the use of transparent agent replication, a technique in which the replicates of agents appear and act as one entity thus avoiding an increase in system complexity and minimizing additional system loads. The paper defines transparent agent replication and identifies the key challenges in using it. Special attention is given to the inter-agent communication, read/write consistency, resource locking, resource synthesis and state synchronization. An implementation of the transparent agent replication for the FIPA-OS framework is presented and the results of testing it within a real-world multi-agent system are shown.
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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 | 0.000 |
| 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 | 0.001 |
| 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 it