Security by Design: Reducing Information Exchange in Multi-Agent Search Tasks
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
Intrinsic to multi-agent systems is the trait of inter-agent dependency on information exchange. During safety-critical tasks, errors in information exchange between agents can lead to vulnerable system behaviour. Consequently, adversarial agents often seek to exploit this vulnerability, rather than exploiting the communicated information itself. Motivated by this problem, the objective of this research is to investigate alternative coordination strategies of multi-agent systems that reduce information exchange and maximize task efficiency. \n \nWe consider the task of a multi-agent system of autonomous agents, such as a team of vehicles, performing a reconnaissance mission in an unknown, hostile, and urban environment. We abstract this task by considering a two-agent system exploring an unknown and structured maze. The goal of the agents is to search all states in the maze while minimizing communication and maximizing search efficiency. \n \nOur results demonstrate that through restricting communication to line-of-sight, exploiting the structure of the environment, and employing deterministic decision-making policies, information exchange can be reduced while preserving a high degree of efficiency in the coordination of autonomous agents.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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