arXiv:2610.28211v1 Announce Type: cross Abstract: Applying South Africa's draft R3337 warning criteria to food-package photographs requires nutrition, ingredient and product-form evidence. This study...
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: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...
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...
The study fine‑tuned DINOv2‑base on 2,253 weighed Nutrition5k dishes to estimate food mass from a single RGB photo and compared it with the zero‑shot vision‑language model Qwen3‑VL‑32B across Nutrition5k and three external datasets. While the fine‑tuned model achieved a mean absolute error of 24.8 g on its own domain—better than Qwen—it failed to outperform Qwen on external datasets and its gram scale varied with scene coverage and differed per food type. Adding a single labeled photo of the same food reduced external error significantly, indicating that single‑image mass estimators require labeled photographs from the target setting.
arXiv:2607. 23273v1 Announce Type: cross Abstract: Computational nutrition needs precise ingredient data, but current databases are incomplete, inconsistent, and built for human reference rather than automated reasoning.
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...
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.