Screened, not peer reviewed

CONTINUUM Preprints

Share your work the day it is ready.

Rapid, free dissemination of research before or alongside peer review. Preprints are screened for scope and compliance, posted with a DOI, and can be versioned as the work develops.

✓ Posted within ~2 working days
✓ DOI and version history
✓ Request peer review at any time

Post a preprint Guidelines

Preprints: 9
Authors: 16
Downloads: 6,199

2 resultsSearch & filters ↓

Research ArticleNot peer reviewed

Prompt-Injection Resilience in Browser-Embedded Assistants

Marcus Tan, Sofia Lindqvist, Lena Hartwell

Version 1 · 2 October 2026 · 10.xxxx/ctm.pp.2026.0006

Assistants embedded in web browsers read page content on the user's behalf, which exposes them to indirect prompt injection: instructions hidden in a page that the assistant mistakes for the user's. We build a test suite of 600 injected pages across seven attack styles and measure three defences: instruction-hierarchy prompting, a classifier that screens page text, and permission gating of sensitive actions. No single defence is sufficient; combining a screening classifier with gating of actions that write or send data lowers attack success from 62% to 4% while preserving 93% of benign task success.

Abstract PDF 912 2880 cited

Short CommunicationNot peer reviewed

Calibrated Uncertainty in Retrieval-Augmented Generation

Amélie Roussel, Tomás Ibarra

Version 2 (updated) · 5 June 2026 · 10.xxxx/ctm.pp.2026.0001

Retrieval-augmented generation grounds answers in retrieved documents, yet models remain over-confident when the retrieved evidence is irrelevant or contradictory. We propose a lightweight confidence estimator that combines the generator's token probabilities with a retrieval-agreement score, and calibrate it with temperature scaling on a small validation set. On three question-answering datasets the estimator lowers expected calibration error from 0.19 to 0.06 and enables selective answering: abstaining on the 20% least confident questions raises accuracy from 71% to 84%. The method needs no model retraining.

Abstract PDF 2,718 9043 cited