arXiv:2607. 18451v1 Announce Type: cross Abstract: Frozen encoders are chosen by how well a lightweight head reads a finding from their features, not whether the geometry separates it.
By Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn
arXiv:2607.18451v3 Announce Type: replace
Abstract: A foundation encoder is pretrained once on a large image corpus and then reused with its weights frozen. Each new task is solved by training a smal...
By Soroosh Tayebi Arasteh, Sven Nebelung, Daniel Truhn
arXiv:2608. 16709v1 Announce Type: cross Abstract: A radiologist reading a model's output faces two problems.
By Vignesh Nagarajan, Sriram Venkatapathy
The paper evaluates nine on‑device named‑entity recognition models ranging from classical taggers to large language models, measuring not only accuracy but also latency and output validity. Using a silver‑gold benchmark derived from an LLM judge panel and a human‑validated corpus, the study shows that encoder‑based models achieve comparable accuracy to a 4 B instruct LLM while being much smaller, faster, and producing no malformed output. Confidence calibration of GLiNER is analyzed, revealing over‑confidence but improved reliability after temperature scaling and thresholding.
By Vinay Kumar Chaganti
arXiv:2609.35470v1 Announce Type: cross
Abstract: Applied researchers increasingly convert images into features with pretrained encoders, then use those features in a downstream prediction model. The...
By Ardyn Nordstrom, Morgan Nordstrom, Vamuyan Sesay, Matthew D. Webb
arXiv:2606. 31495v1 Announce Type: new Abstract: We study a single idea across two settings: that a prediction-error signal, computed by a small predictor over the latent space of a frozen encoder, can serve both as a gate on plasticity and as a substrate for metacognition.
By Louis Mouchon