Eden Moran

The project develops a system for implicit entity recognition in text, identifying people, organizations, locations, and other entities that are referred to indirectly rather than named explicitly. By combining contextual reasoning, semantic inference, and discourse-level cues, the model infers hidden or implied entities from descriptions, roles, actions, or relationships. This enables deeper text understanding in domains where entities are frequently mentioned obliquely, improving downstream tasks such as information extraction, summarization, and knowledge base construction. 

Omri Sasson

The project develops a framework for generating imperfect student simulations using LLMs, creating virtual learners that exhibit realistic misunderstandings, partial knowledge, inconsistent reasoning, and common conceptual errors. By modeling not only correct responses but also typical mistakes and learning gaps, the system produces richer training data for tutoring platforms, assessment tools, and classroom-support systems. This enables more robust evaluation of educational technologies and provides instructors with realistic scenarios for testing feedback strategies and adaptive teaching methods. 

Yigal Meshulam

The project develops a decentralized multi-robot task-allocation framework operating under a zero-knowledge assumption, in which robots have no prior information about task characteristics or peer capabilities. Instead, each robot independently selects tasks and learns solely by observing its own outcomes. Using a collaborative-filtering-inspired approach, robots share minimal performance signals to infer which task–robot pairings are most effective, gradually converging toward efficient global allocation without central coordination or explicit communication of internal models. This results in scalable, resilient task assignment suitable for environments with uncertainty and dynamism.