RVLM: Recursive Vision-Language Models with Adaptive Depth

Medical AI systems face two fundamental limitations. First, conventional vision-language models (VLMs) perform single-pass inference, yielding black-box predictions that cannot be audited or explained in clinical terms. Second, iterative reasoning systems that expose intermediate steps rely on fixed iteration budgets wasting compute on simple cases while providing insufficient depth for complex ones. We address both limitations with a unified framework. RVLM replaces single-pass inference with an iterative generate-execute loop: at each step, the model writes Python code, invokes vision sub-agents, manipulates images, and accumulates evidence. Every diagnostic claim is grounded in executable code, satisfying auditability requirements of clinical AI governance frameworks. RRouter makes iteration depth adaptive: a lightweight controller predicts the optimal budget from task-complexity features, then monitors progress and terminates early when reasoning stalls. We evaluate on BraTS 2023 Meningioma (brain MRI) and MIMIC-CXR (chest X-ray) using Gemini 2.5 Flash without fine-tuning. Across repeated runs, RVLM shows high consistency on salient findings (e.g., mass presence and enhancement) and can detect cross-modal discrepancies between Fluid-Attenuated Inversion Recovery (FLAIR) signal characteristics and segmentation boundaries. On MIMIC-CXR, it generates structured reports and correctly recognises view-specific artefacts. Code: https://github.com/nican2018/rvlm.

Mining Reviews in Open Source Code for Developers Trail: A Process Mining Approach

Audit trails are evidential indications of activities performers in any logs. Modern reactive systems such as transaction processing systems, management information systems, decision support systems and even executive management systems log activities of users as they perform their daily tasks for a number of reasons and perhaps one of the most important is security. In order to efficiently monitor and manage privacy and access to information, the trails as captured and recorded in these logs play a pivotal role in this regard. In Open Source realm, however, this is not the case. Although the objective with free software is to allow for access, free distribution and the rights to modify coding, having such audit trails can help to trace and understand how active members of these communities are and the type of activities they perform. In this paper, we propose using process mining to construct logs using as much data as can be found in open source repositories in order to produce a process model, also called a workflow net that graphical depicts the sequential occurrence of developers activities. Our method is exhibited through a simple algorithm called Act-Trace.