arXiv subjects

Mohamed Shaaban

Publications and source records attributed to Mohamed Shaaban.

4 recordsLinked to original sources

arXiv2026

LUVCam: A high-performance, low-cost, UV/optical camera for the future of astronomy in space

Astronomy-grade cameras with robust performance and heritage in the space environment have long been costly, substantially limiting capacity for space-based astronomy and creating a resource barrier to access. Additionally, ultraviolet observations have historically been limited by the low quantum efficiency of most sensors in this wavelength range. The LUVCam program is designed to address both issues by providing a high-performance, low-cost, UV/optical camera system sufficiently capable to support a wide-array of space-based astronomy missions. LUVCam features a large format, low-noise, large pixel, and high quantum efficiency, commercial-off-the-shelf back(front)-side illuminated CMOS sensor, packaged with custom built readout electronics, firmware, and thermomechanical structure to provide both superlative science capability and precision on-sensor guidance at fast cadence to allow for stable high-resolution imaging. LUVCam is ITAR-free and cheap to fabricate, opening up new opportunities for access to space telescopes. Here we introduce LUVCam, describe its performance characteristics, and the rapid implementation of a technology demonstration for flight. LUVCam, coupled with a small aperture custom-built UV telescope, has been on orbit since July 2024 and has achieved Technology Readiness Level (TRL) 7. LUVCam is manifested for several more near-term orbital missions, including a second technology demonstration CubeSat for launch in 2026, and will provide both focal plane cameras for QUVIK, a two-channel UV transient astronomy mission.

astro-ph.IM↗

arXiv2026

SecureGate: Learning When to Reveal PII Safely via Token-Gated Dual-Adapters for Federated LLMs

Federated learning (FL) enables collaborative training across organizational silos without sharing raw data, making it attractive for privacy-sensitive applications. With the rapid adoption of large language models (LLMs), federated fine-tuning of generative LLMs has gained attention as a way to leverage distributed data while preserving confidentiality. However, this setting introduces fundamental challenges: (i) privacy leakage of personally identifiable information (PII) due to LLM memorization, and (ii) a persistent tension between global generalization and local utility under heterogeneous data. Existing defenses, such as data sanitization and differential privacy, reduce leakage but often degrade downstream performance. We propose SecureGate, a privacy-aware federated fine-tuning framework for LLMs that provides fine-grained privacy control without sacrificing utility. SecureGate employs a dual-adapter LoRA architecture: a secure adapter that learns sanitized, globally shareable representations, and a revealing adapter that captures sensitive, organization-specific knowledge. A token-controlled gating module selectively activates these adapters at inference time, enabling controlled information disclosure without retraining. Extensive experiments across multiple LLMs and real-world datasets show that SecureGate improves task utility while substantially reducing PII leakage, achieving up to a 31.66X reduction in inference attack accuracy and a 17.07X reduction in extraction recall for unauthorized requests. Additionally, it maintains 100% routing reliability to the correct adapter and incurs only minimal computational and communication overhead.

cs.CR↗

arXiv2026

LLM Novice Uplift on Dual-Use, In Silico Biology Tasks

Large language models (LLMs) perform increasingly well on biology benchmarks, but it remains unclear whether they uplift novice users -- i.e., enable humans to perform better than with internet-only resources. This uncertainty is central to understanding both scientific acceleration and dual-use risk. We conducted a multi-model, multi-benchmark human uplift study comparing novices with LLM access versus internet-only access across eight biosecurity-relevant task sets. Participants worked on complex problems with ample time (up to 13 hours for the most involved tasks). We found that LLM access provided substantial uplift: novices with LLMs were 4.16 times more accurate than controls (95% CI [2.63, 6.87]). On four benchmarks with available expert baselines (internet-only), novices with LLMs outperformed experts on three of them. Perhaps surprisingly, standalone LLMs often exceeded LLM-assisted novices, indicating that users were not eliciting the strongest available contributions from the LLMs. Most participants (89.6%) reported little difficulty obtaining dual-use-relevant information despite safeguards. Overall, LLMs substantially uplift novices on biological tasks previously reserved for trained practitioners, underscoring the need for sustained, interactive uplift evaluations alongside traditional benchmarks.

cs.AI↗

arXiv2025

Humanity's Last Exam

Benchmarks are important tools for tracking the rapid advancements in large language model (LLM) capabilities. However, benchmarks are not keeping pace in difficulty: LLMs now achieve over 90\% accuracy on popular benchmarks like MMLU, limiting informed measurement of state-of-the-art LLM capabilities. In response, we introduce Humanity's Last Exam (HLE), a multi-modal benchmark at the frontier of human knowledge, designed to be the final closed-ended academic benchmark of its kind with broad subject coverage. HLE consists of 2,500 questions across dozens of subjects, including mathematics, humanities, and the natural sciences. HLE is developed globally by subject-matter experts and consists of multiple-choice and short-answer questions suitable for automated grading. Each question has a known solution that is unambiguous and easily verifiable, but cannot be quickly answered via internet retrieval. State-of-the-art LLMs demonstrate low accuracy and calibration on HLE, highlighting a significant gap between current LLM capabilities and the expert human frontier on closed-ended academic questions. To inform research and policymaking upon a clear understanding of model capabilities, we publicly release HLE at https://lastexam.ai.

cs.LG↗