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
Anomaly detection underpins quality inspection, medical diagnosis, and safety monitoring, yet progress remains hindered by the scarcity of anomaly samples, limited semantic alignment, and unreliable uncertainty estimates. Here we present ACMAN-AD (Adaptive Cross-Modal Anomaly Network for Anomaly Detection), a unified framework that leverages vision—language pre-training to overcome these bottlenecks. ACMAN- AD integrates four complementary modules: a Cross-Modal Dynamic Adapter (CMDA) for image-guided prompt generation and adaptive alignment; a Self-Supervised Multi-Scale Feature Fusion (SSMFF) strategy for hierarchical representation learning; a Generative Adversarial Anomaly Synthesis (GAAS) module to enrich anomaly diversity; and a Knowledge Distillation and Uncertainty Quantification (KDUQ) scheme for lightweight inference with calibrated confidence. On MVTec AD and VisA, ACMAN-AD surpasses state-of- the-art methods in both detection and segmentation, improving AUROC and AUPRC by 3.2.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 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