הכנס הארצי למדעי המחשב והמידע

Abstract

Diffusion models have transformed the landscape of image generation, offering a reliable sampling from the probability density function of images. These models have also been extended to provide posterior sampling capabilities, by conditioning the process on given external controls. This ability has been demonstrated for handling inverse problems in novel ways, and for producing images conditioned on textual prompts, showing in both tasks remarkable results. A very new and fascinating trend in this domain harnesses diffusion models to image compression, showing great potential here as well. This talk presents this story: The essence of diffusion models, their generative abilities, their posterior-sampling extensions, and most importantly, their ability to lead to novel and highly effective compression schemes for images and other data sources. No background is assumed.

Abstract

Language models (LMs) capture large amounts of factual knowledge applicable to a wide range of tasks. However, we do not yet have a coherent theory for how this knowledge is stored and retrieved. A compelling analogy is that of knowledge bases, which have traditionally been used to represent facts in a structured manner. In this talk, I will present our recent results that shed light on mechanisms for information storage and retrieval in LLMs, and how these relate to a knowledge base view.

Abstract

Multi-robot systems are often designed under a simple assumption: all robots share the same goal and cooperate fully. But what happens when this assumption does not hold? In this talk, I will explore strategic behavior in multi-robot systems, from decentralized swarms to settings involving competition, self-interest, and selective cooperation. I will discuss how physical constraints, limited information, and interactions between robots shape collective decision making, and how these settings create challenges beyond standard cooperative planning.

Abstract

Effective storytelling relies on a delicate balance between meeting the reader's prior expectations and introducing unexpected developments. In the domain of detective fiction, this tension is known as fair play, which includes the implicit agreement between the writer and the reader as to the possibility and challenge of identifying the culprit through the story's clues. I will present a probabilistic framework that aims to quantify this elusive notion. Our definitions present an inherent tension between the coherence of the story, which measures how much the resolution “makes sense” in explaining the clues, and the surprise it induces. Due to this tension, balancing these qualities is challenging. We operationalize the proposed framework, defining an approximate computable metric of “fair play”.

Our validation simulations include both real stories and LLM-generated ones (the latter allowing maximal experimental control). The results present similar trends to the ones predicted by the framework. I will conclude by discussing potential broader implications.

This is joint work with Eitan Wagner and Renana Keydar.

Abstract

Foundation models aim to reuse representations and computation across tasks, domains, and models. Yet different models often operate in incompatible feature spaces: dimensions may differ, coordinates may have unrelated meanings, and representations of objects may not be directly comparable. This raises a basic question: can we construct a common language without requiring shared feature coordinates?

I will discuss recent work that represents information through relations between tokens rather than feature identity, allowing structure to be compared across otherwise incompatible representation spaces. I will also show how related relational views can help us study representations inside large language models.

Abstract

Grokking is the sudden onset of generalization long after a model has already overfit its training data. In this talk, I will present an end-to-end theoretical account of this phenomenon in classical ridge regression. For over-parameterized linear models trained by gradient descent with weight decay, we prove the full grokking trajectory: early overfitting, a prolonged period of poor generalization, and eventual convergence to arbitrarily small generalization error. Our analysis provides quantitative bounds on the generalization delay, and shows how it can be amplified or eliminated through principled hyperparameter tuning. I will also present experiments suggesting that these predictions extend beyond linear models to nonlinear neural networks. Overall, the results suggest that grokking is not an inherent failure mode of deep learning, but rather a consequence of specific training conditions, and thus does not require fundamental changes to the model architecture or learning algorithm to avoid.

Joint work with Mingyue Xu and Gal Vardi.