Colloquia and Seminars

Large language models increasingly translate and explain politically contested events, yet their outputs can introduce narrative bias while preserving the underlying facts.

We create two datasets covering the Russia–Ukraine war and the Israel–Palestine conflict, pairing neutral event descriptions with competing narratives curated from news outlets and Telegram channels.

We introduce a framework for measuring perspectival divergence: systematic alignment with competing narratives about the same events. We characterize model-specific tendencies through narrative framing, emotional manipulation, source credibility, social identity, and contested naming.

Across both datasets and four languages, we show that apparent neutrality varies with the conflict, language, and prompting context. To mitigate these effects, we adapt self-distancing, a psychological intervention that prompts models to adopt a distant observer’s perspective.

Across eight models, self-distancing reduces measured divergence by 45% on average while preserving event content in 93% of outputs. We further introduce Self-Distancing Activation Steering (SDAS), which extracts the internal representational shift induced by self-distancing and applies it during generation without fine-tuning or modifying the original prompt.

Across five open-weight models, SDAS reduces divergence by 58% while preserving event content in 91% of outputs. Steering directions also transfer between conflicts without additional tuning, offering a practical approach to measuring and reducing narrative bias without prescribing a preferred political viewpoint.

The general-purpose nature of Large Language Models (LLMs) presents a significant challenge for domain-specific applications, often leading to out-of-domain (OOD) interactions that undermine the provider's intent. Existing methods for detecting such scenarios treat the LLM as an uninterpretable black box and overlook the internal processing of inputs. In this work we show that layer transitions provide a promising avenue for extracting domain-specific signature. Specifically, we present several lightweight ways of learning on internal dynamics encoded using a sparse autoencoder (SAE) that exhibit great capability in distinguishing OOD texts. Building on top of SAEs representation transitions enables us to better interpret the LLM internal evolution of input processing and shed light on its decisions. We provide a comprehensive analysis of the method and benchmark it with the gemma-2 2B and 9B models. Our results emphasize the efficacy of the internal process in capturing fine-grained input-related details

AI is driving unprecedented growth in data center compute, with the market projected to approach $2 trillion by 2030 and revenue growth averaging approximately 24% annually from 2025 to 2030. As AI adoption progresses from infrastructure and physical systems to scientific discovery, it is creating a major opportunity for verification and in particular formal verification - the crown of design. Natural-language interfaces and AI-driven productivity are introducing a new level of abstraction, while emerging examples of AI-assisted mathematical reasoning demonstrate the potential to extend scientific and engineering capabilities.

This keynote traces the evolution from the man–machine communication vision presented at DAC 1964, through collective intelligence and human-in-the-loop formal methods presented at JUG 2013, to today’s agentic verification workflows in Cadence ChipStack. These workflows combine specialized formal agents, proof engines, machine learning, and expert guidance to address properties that remain difficult to prove manually. By interleaving design and verification, and using platforms such as Cadence ChipStack, the Formal Verification AI Flywheel can transform productivity at scale into greater proof power, enabling engineers to build increasingly complex systems with higher confidence and much less manual effort.

Short bio:  Ziyad Hanna is a Corporate Vice President at Cadence Design Systems and R&D General Manager of Cadence Israel. He leads system verification products and Cadence-wide AI initiatives for autonomous chip-design development. Previously, he served as Senior Vice President of Jasper and senior PE and management roles at Intel, where he developed three generations of formal-verification suites widely deployed since the start of Pentium. Hanna earned degrees from Tel Aviv University and a PhD from Oxford University, where he is a visiting professor. An IEEE Senior Member, he has published over 30 articles, 20 patents, and delivered hundreds of industrial and academic presentations and talks. His honors include Intel IAA awards and an honorary doctorate from Ruppin Collage. Ziyad initiated a formal-verification course at the Technion that attracts hundreds of students interested in pursuing advanced studies in the field.

While digital AI and large multimodal models have achieved massive scaling laws in virtual spaces, grounding artificial intelligence in the unstructured, non-deterministic physical world exposes severe foundational bottlenecks. Physical AI -the synthesis of embodied robotics, multi-modal perception, foundation world models, and real-time control- cannot rely merely on scaling compute or self-supervised text scraping. The transition to embodied agents operating safely at human-level dexterity and situational reasoning presents acute open problems: closing the sim-to-real gap, formulating verifiable neural world models, scaling physics-grounded reinforcement learning under sparse supervision, and developing low-latency edge inference capable of deterministic safety guarantees.

This talk bypasses off-the-shelf industrial deployments to outline the critical scientific and algorithmic roadblocks where industry and public frameworks stall without academic intervention. We will dissect the open research questions Israel must tackle to pioneer embodied autonomy: How do we construct mathematically rigorous bounds for agent safety in unstructured physical environments? What new paradigms in spatial reasoning and tactile-proprioceptive sensor fusion can replace sample-inefficient learning?

Short bio: Guy Altagar is the Vice President and Head of Physical AI at the Israeli National AI Directorate within the Prime Minister's Office, where he leads national strategy, infrastructure development, and R&D testbed initiatives for embodied artificial intelligence and robotics. Prior to his government role, he was the founder and Chief Executive Officer of Unlimited Robotics, leading the development of autonomous service robot platforms and software operating systems for embodied devices. With an interdisciplinary background spanning deep-tech entrepreneurship, engineering leadership, and law, Guy works closely with academic research institutions, government bodies, and international industry leaders to accelerate national-scale computing infrastructure, physical test environments, and cross-disciplinary research in physical autonomy.