Afeka College of Engineering

Physician–patient diagnostic interactions often rely on incomplete, uncertain, and noisy symptom descriptions expressed in non-specialist language. To reach an accurate diagnosis efficiently, physicians employ adaptive sequences of focused questions in which each inquiry depends on prior patient responses. Similar requirements apply to AI assistants engaged in clinical dialogue, which must strategically select questions to elicit diagnostic information efficiently. While several datasets and benchmarks address clinical reasoning, few focus on evaluating the strategic inquiry process itself. To address this gap, we introduce Q4Dx (Question-Driven Diagnosis), a benchmark for assessing goal-directed diagnostic questioning. Q4Dx consists of synthetically generated patient cases derived from curated symptom–disease knowledge. Each case is instantiated at multiple information levels (100%, 80%, and 50% symptom exposure) to evaluate performance under varying informational constraints. We simulate patient–physician interactions using GPT-4.1 and GPT-4o-mini agents to generate clinician questions, patient responses, and intermediate diagnostic hypotheses. Performance is evaluated using Zero-shot Diagnostic Accuracy (ZDA), Mean Questions to Correct Diagnosis (MQD), and Inquire Sequence Efficiency (ISE), which measures convergence toward the correct diagnosis. Q4Dx provides a reusable framework for benchmarking large language models in strategic clinical inquiry and supports future research on AI-assisted diagnostic training. The dataset and benchmark are publicly available at: https://github.com/MaiWert/MedQDx.

Operating electronic systems in space environments presents significant challenges due to continuous exposure to cosmic, solar, and trapped radiation, which can induce catastrophic single-event effects. This paper introduces a novel nonintrusive mitigation apparatus designed to protect high-end commercial off-the-shelf electronics in space. The apparatus incorporates an array of real-time particle detectors coupled with a mitigation algorithm. Upon identifying potentially harmful particles, the system power cycles affected electronics, preempting permanent damage. The apparatus was evaluated using GEANT4 simulations, which were compared with empirical data from the "COTS-Capsule" experiment aboard the International Space Station, demonstrating strong agreement. Key results indicate that the system achieves a 95% detection accuracy with a power cycle rate of once every seven hours per square centimeter of sensitive electronics. The COTS-Capsule represents a cost-effective, flexible solution for integrating modern, high-end, non-space-qualified electronics into a variety of space missions, addressing critical challenges in the new-space era.

Objective. Outpatient notes carry follow-up instructions pairing actions with future times ("MRI brain in two weeks"). Extracting (action, date) pairs supports scheduling and audit, but generative extractors miss the date because linking and arithmetic are implicit in decoding. We test a hybrid neural-symbolic pipeline against direct generation. Methods. We define TestSpecification and TimeSpecification entities and a ScheduledFor relation. BioBERT feeds BIO tagging and a biaffine linker; entities are canonicalized via a 28-action ontology and times normalized to day offsets deterministically. We evaluate on a 2,000-note synthetic outpatient corpus with action-disjoint splits (18 train, 6 OOV-test) against zero-shot GPT-4o-mini and LoRA-fine-tuned LLaMA-3 8B with note-level bootstrap 95

Generative AI makes answers easy and understanding hard, and uncritical use invites cognitive offloading. Schools still measure unaided performance, yet the real task is to produce good work with AI: framing an ill-defined task, judging the output, and steering the model toward a better result. This ability is rarely assessed in its own right; where measured, it collapses into one "prompting" score that cannot diagnose why AI use succeeds or fails. We propose CoRe-3 (Co-Reasoning), a competency model factoring productive AI use into three assessable skills we abbreviate FJS: Framing (specifying an ill-defined task before invoking AI), Judging (evaluating output for errors and unstated assumptions), and Steering (iteratively redirecting the model). Its distinguishing claim is the separation of pre-generation Framing from post-generation Steering, with Judging as the gate between. We ground the skills in theory, state five testable propositions, and instantiate them in CoReasoningLab, an open platform that presents flawed AI output and scores them independently. Over simulated learners (generated and graded by different models), the skills dissociate: each tracks its own manipulated competence while staying flat in the others, and grades become correlated when one competence is shared across all three (convergent and discriminant validity), across grader backends from two providers. Human-rater agreement and outcomes are next; we release the instrument, data, and protocol.

Choosing or ranking language models for a specific application is hardest when no task-specific labeled data exists, and standard public benchmarks cannot be trusted, their items having likely leaked into pretraining, so scores reflect memorization rather than fitness. We present CoEval, an open-source, reusable framework that closes this gap end to end: from only a description of a task or domain, teacher models synthesize a fresh, attribute-controlled benchmark with no human labels, contamination-free because items are generated anew on each run, and a cross-family judge ensemble ranks candidate models with no human raters. Validated where ground truth exists, CoEval recovers the true model ranking and tracks ground-truth correctness at ho=0.86. The label-free judging needs no human calibration because judge-panel composition (vendor diversity), not size, drives reliability: a small, well-chosen cross-family panel is most reliable, while a single judge can be anti-correlated with ground truth (judge-choice regret 0.35) and the ensemble never is. Generated items show zero verbatim 13-gram overlap with five major public benchmarks; the panel cancels verbosity bias and precludes same-family self-preference. A four-task study produced 7,978 evaluations for USD 5.89. The same declarative pipeline applies to any domain and is cheap enough to re-run on every model release: a label-free, contamination-free leaderboard any team can regenerate for its own application.