arXiv subjects

Ahmed Almasoud

Publications and source records attributed to Ahmed Almasoud.

4 recordsLinked to original sources

arXiv2026

Mapping Human Anti-collusion Mechanisms to Multi-agent AI Systems

As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed in human markets and institutions. While human domains have accumulated centuries of anti-collusion mechanisms, it remains unclear how these can be adapted to AI settings. This paper addresses that gap by (i) developing a taxonomy of human anti-collusion mechanisms, including sanctions, leniency & whistleblowing, monitoring & auditing, market design, and governance and (ii) mapping them to potential interventions for multi-agent AI systems. For each mechanism, we propose implementation approaches. We also highlight open challenges, such as the attribution problem (difficulty attributing emergent coordination to specific agents), identity fluidity (agents being easily forked or modified), the boundary problem (distinguishing beneficial cooperation from harmful collusion), and adversarial adaptation (agents learning to evade detection).

cs.MA↗

arXiv2026

Evaluating Multi-Agent LLM Architectures for Rare Disease Diagnosis

While large language models are capable diagnostic tools, the impact of multi-agent topology on diagnostic accuracy remains underexplored. This study evaluates four agent topologies, Control (single agent), Hierarchical, Adversarial, and Collaborative, across 302 cases spanning 33 rare disease categories. We introduce a Reasoning Gap metric to quantify the difference between internal knowledge retrieval and final diagnostic accuracy. Results indicate that the Hierarchical topology (50.0% accuracy) marginally outperforms Collaborative (49.8%) and Control (48.5%) configurations. In contrast, the Adversarial model significantly degrades performance (27.3%), exhibiting a massive Reasoning Gap where valid diagnoses were rejected due to artificial doubt. Across all architectures, performance was strongest in Allergic diseases and Toxic Effects categories but poorest in Cardiac Malformation and Respiratory cases. Critically, while the single-agent baseline was generally robust, all multi-agent systems, including the Adversarial model, yielded superior accuracy in Bone and Thoracic disease categories. These findings demonstrate that increasing system complexity does not guarantee better reasoning, supporting a shift toward dynamic topology selection.

cs.MA↗

arXiv2026

Specialists or Generalists? Multi-Agent and Single-Agent LLMs for Essay Grading

Automated essay scoring (AES) systems increasingly rely on large language models, yet little is known about how architectural choices shape their performance across different essay quality levels. This paper evaluates single-agent and multi-agent LLM architectures for essay grading using the ASAP 2.0 corpus. Our multi-agent system decomposes grading into three specialist agents (Content, Structure, Language) coordinated by a Chairman Agent that implements rubric-aligned logic including veto rules and score capping. We test both architectures in zero-shot and few-shot conditions using GPT-5.1. Results show that the multi-agent system is significantly better at identifying weak essays while the single-agent system performs better on mid-range essays. Both architectures struggle with high-quality essays. Critically, few-shot calibration emerges as the dominant factor in system performance -- providing just two examples per score level improves QWK by approximately 26% for both architectures. These findings suggest architectural choice should align with specific deployment priorities, with multi-agent AI particularly suited for diagnostic screening of at-risk students, while single-agent models provide a cost-effective solution for general assessment.

cs.CL↗

arXiv2023

Uncertainty in AI: Evaluating Deep Neural Networks on Out-of-Distribution Images

As AI models are increasingly deployed in critical applications, ensuring the consistent performance of models when exposed to unusual situations such as out-of-distribution (OOD) or perturbed data, is important. Therefore, this paper investigates the uncertainty of various deep neural networks, including ResNet-50, VGG16, DenseNet121, AlexNet, and GoogleNet, when dealing with such data. Our approach includes three experiments. First, we used the pretrained models to classify OOD images generated via DALL-E to assess their performance. Second, we built an ensemble from the models' predictions using probabilistic averaging for consensus due to its advantages over plurality or majority voting. The ensemble's uncertainty was quantified using average probabilities, variance, and entropy metrics. Our results showed that while ResNet-50 was the most accurate single model for OOD images, the ensemble performed even better, correctly classifying all images. Third, we tested model robustness by adding perturbations (filters, rotations, etc.) to new epistemic images from DALL-E or real-world captures. ResNet-50 was chosen for this being the best performing model. While it classified 4 out of 5 unperturbed images correctly, it misclassified all of them post-perturbation, indicating a significant vulnerability. These misclassifications, which are clear to human observers, highlight AI models' limitations. Using saliency maps, we identified regions of the images that the model considered important for their decisions.

cs.CV↗