Applying South Africa's draft R3337 warning criteria to food-package photographs requires nutrition, ingredient and product-form evidence. This study presents a region-guided evidence extraction and a...
arXiv:2609.15427v1 Announce Type: cross Abstract: Using food package images to monitor sodium and salt content against South Africa's R214 sodium limits is challenging when screening decisions requir...
The paper presents a framework that enhances single-image nutrition estimation by using multimodal large language models to inventory visible foods and verify both food identity and the suitability of proposed 2D regions for portion estimation. A whole-image review identifies gaps and triggers a single targeted recovery pass, after which recovered regions are re-verified and reconciled into a final item set. The approach, which requires no task-specific fine-tuning, improves item-level grounding, mass accuracy, energy accuracy, and item-identity precision and recall compared to a retrieval baseline.
arXiv:2607. 08423v1 Announce Type: new Abstract: The rapid integration of Large Vision-Language Models (VLMs) into critical infrastructure promises to revolutionize personalized healthcare and dietary management.
arXiv:2610.32817v1 Announce Type: new Abstract: For agricultural advisory, generating an answer is only part of the problem: the system must also control unsafe requests, work with limited connectivi...
ReuScope is a protocol designed to audit data reuse and evaluation sensitivity, demonstrated on Capsule Vision 2024. It measures data relationships and compares model performance when altering the evaluation population or training data while keeping predictions constant. The study finds that removing training rows linked to Kvasir-Capsule significantly lowers public‑validation accuracy, indicating sensitivity to source removal rather than leakage inflation.
arXiv:2608.23313v1 Announce Type: new Abstract: Vision-language model safety benchmarks typically evaluate only final responses: whether a model refuses, warns, or complies. This outcome-level view c...
arXiv:2610.09726v1 Announce Type: new Abstract: Estimating remaining shelf life (RSL) from images could provide affordable decision support for perishable produce, but evaluation protocols can substa...
arXiv:2606. 08948v1 Announce Type: cross Abstract: Comprehensive estimation of dietary micronutrients from food images could improve clinical nutrition care, but training such models requires large multimodal datasets linking diverse foods to complete nutrient profiles.
arXiv:2610.26538v2 Announce Type: replace-cross Abstract: Photo-based estimation of food energy could ease dietary assessment, and general-purpose multimodal large language models (LLMs) make such es...
The paper introduces TRAILS, a taxonomy of 38 mechanisms that can compromise the reliability of large language model (LLM) read‑and‑evaluate tasks in evidence synthesis, such as study screening, data extraction, risk‑of‑bias assessment, and critical appraisal. It explains how these mechanisms can cause inconsistent or hidden failures across repeated LLM runs and outlines deployment settings, testing approaches, and mitigation strategies for each. The authors argue that repeatability or accuracy alone does not guarantee trustworthy judgments, emphasizing the need for human evaluation to verify that decisions are evidence‑based, free from external influence, and reproducible.
Halal-NeSyNet is a neuro‑symbolic model that uses a DistilBERT encoder and a 16‑dimensional rule‑derived symbolic vector to audit food ingredient clauses for Halal compliance. It was evaluated on a benchmark of 3,199 items, achieving over 97 % accuracy and 99.7 % counterfactual consistency, far surpassing text‑only baselines. The model’s performance demonstrates the importance of clause‑scoped symbolic features for accurate risk‑aware Halal logistics triage.