Errors are robustly tamed in cumulative knowledge processes
Bibliographic record
Abstract
As knowledge accumulates in science and society in a distributed fashion, erroneous derivations can be introduced into the corpus of knowledge. Such derivations can compromise the validity of any units of knowledge that rely on them in the future. Can societal knowledge maintain some level of integrity given simple distributed error-checking mechanisms? In this paper, we investigate the following formulation of the question: assuming that a constant fraction of the new derivations is wrong, is it possible for simple error-checking mechanisms that apply when a new unit of knowledge is derived to maintain the integrity of the corpus of knowledge? This question was introduced by Ben-Eliezer et al. [“Is this correct? Let’s check!” in 14th Innovations in Theoretical Computer Science Conference (ITCS, 2023)], who gave a robust affirmative answer in a specific probabilistic model for knowledge accumulation. Namely, this model required that new units depend on just one existing unit and join the process according to a preferential attachment rule. In this work, we consider much more general families of processes of knowledge accumulation, where new units may depend on multiple existing units and join according to varied attachment mechanisms. We also consider models with a (random) fraction of insertions of adversarial nodes. We give a robust affirmative answer to the above question by showing that for all of these models, as long as many of the units follow simple local heuristics for checking a bounded number of units they depend on, all errors will be eventually eliminated.
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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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".