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
We consider the problem of monitoring an interactive device, such as an implementation of a network protocol, in order to check whether its execution is consistent with its specification. At rst glance, it appears that a monitor could simply follow the input-output trace of the device and check it against the specification. However, if the monitor is able to observe inputs and outputs only from a vantage point external to the device---as is typically the case---the problem becomes surprisingly difficult. This is because events may be bu ered, and even lost, between the monitor and the device, in which case, even for a correctly running device, the trace observed at the monitor could be inconsistent with the specification.In this paper, we formulate the problem of external monitoring as a language recognition problem . Given a specification that accepts a certain language of input-output sequences, we de ne another language that corresponds to input-output sequences observable externally. We also give an algorithm to check membership of a string in the derived language. It turns out that without any assumptions on the specification, this algorithm may take unbounded time and space. To address this problem, we de ne a series of properties of device specifications or protocols that can be exploited to construct e cient language recognizers at the monitor. We characterize these properties and provide complexity bounds for monitoring in each case.To illustrate our methodology, we describe properties of the Internet Transmission Control Protocol (TCP), and identify features of the protocol that make it challenging to monitor e ciently.
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 | 0.001 | 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 | 0.003 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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