Recent Algorithmic Advances in Population Protocols
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
Population protocols are a popular model of distributed computing, introduced by Angluin, Aspnes, Diamadi, Fischer, and Peralta [6] a little over a decade ago. In the meantime, the model has proved a useful abstraction for modeling various settings, from wireless sensor networks [35, 26], to gene regulatory networks [17], and chemical reaction networks [21]. In a nutshell, a population protocol consists of n agents with limited local state that interact randomly in pairs, according to an underlying communication graph, and cooperate to collectively compute global predicates. From a theoretical prospective, population protocols, with the restricted communication and computational power, are probably one of the simplest distributed model one can imagine. Perhaps surprisingly though, solutions to many classical distributed tasks are still possible. Moreover, these solutions often rely on interesting algorithmic ideas for design and interesting probabilistic techniques for analysis, while known lower bound results revolve around complex combinatorial arguments.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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