Uri Alon

Code for: The stochastic nature of cortisol ultradian rhythms

Yaniv Grosskopf, Dor Danan, Glen Pidham, Keren Doenyas-Barak et autres

Model, analysis, and figure-generation code for the paper "The stochastic nature of cortisol ultradian rhythms". Regenerates every script-generated main-text figure from a shipped fitted-model checkpoint. The model is three coupled ODEs for the hypothalamic-pituitary-adrenal axis (CRH, ACTH, cortisol) with glucocorticoid-receptor feedback and …

il, gb (code pays fourni par la source)

Code for: The stochastic nature of cortisol ultradian rhythms

Yaniv Grosskopf, Dor Danan, Glen Pidham, Keren Doenyas-Barak et autres

Model, analysis, and figure-generation code for the paper "The stochastic nature of cortisol ultradian rhythms". Regenerates every script-generated main-text figure from a shipped fitted-model checkpoint. The model is three coupled ODEs for the hypothalamic-pituitary-adrenal axis (CRH, ACTH, cortisol) with glucocorticoid-receptor feedback and …

il, gb (code pays fourni par la source)

Gemma 4 Technical Report

Gemma Team, Sherif El Abd, Vaibhav Aggarwal, Robin Algayres et autres

We introduce Gemma 4, a new generation of open-weight, natively multimodal language models in the Gemma model family. Designed to advance compute efficiency and reasoning, the Gemma 4 model suite features dense and Mixture-of-Experts architectures, ranging from 2.3B to 31B parameters. Alongside …

In-Context Learning with Long-Context Models: An In-Depth Exploration

Amanda Bertsch, Maor Ivgi, Emily Xiao, Uri Alon et autres

Amanda Bertsch, Maor Ivgi, Emily Xiao, Uri Alon, Jonathan Berant, Matthew R. Gormley, Graham Neubig. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

us, il (code pays fourni par la source)

In-Context Learning with Long-Context Models: An In-Depth Exploration

Amanda Bertsch, Maor Ivgi, Uri Alon, Jonathan Berant et autres

As model context lengths continue to increase, the number of demonstrations that can be provided in-context approaches the size of entire training datasets. We study the behavior of in-context learning (ICL) at this extreme scale on multiple datasets and models. We show …

SEGLLM: Topic-Oriented Call Segmentation Via LLM-Based Conversation Synthesis

Itzik Malkiel, Uri Alon, Yakir Yehuda, Shahar Keren et autres

Transcriptions of phone calls are of significant value across diverse fields, such as sales, customer service, healthcare, and law enforcement. Nevertheless, the analysis of these recorded conversations can be an arduous and time-intensive process, especially when dealing with long and multifaceted dialogues. …

gb, il (code pays fourni par la source)

Transformers Can Achieve Length Generalization But Not Robustly

Yongchao Zhou, Uri Alon, Xinyun Chen, Xuezhi Wang et autres

Length generalization, defined as the ability to extrapolate from shorter training sequences to longer test ones, is a significant challenge for language models. This issue persists even with large-scale Transformers handling relatively straightforward tasks. In this paper, we test the Transformer's ability …

In-Context Principle Learning from Mistakes

Tianjun Zhang, Aman Madaan, Luyu Gao, Steven Zheng et autres

In-context learning (ICL, also known as few-shot prompting) has been the standard method of adapting LLMs to downstream tasks, by learning from a few input-output examples. Nonetheless, all ICL-based approaches only learn from correct input-output pairs. In this paper, we revisit this …

Contextual Predictive Mutation Testing

Kush Jain, Uri Alon, Alex Groce, Claire Le Goues

Mutation testing is a powerful technique for assessing and improving test suite quality that artificially introduces bugs and checks whether the test suites catch them. However, it is also computationally expensive and thus does not scale to large systems and projects. One …

us (code pays fourni par la source)

Universal Self-Consistency for Large Language Model Generation

Xinyun Chen, Renat Aksitov, Uri Alon, Jie Ren et autres

Self-consistency with chain-of-thought prompting (CoT) has demonstrated remarkable performance gains on various challenging tasks, by utilizing multiple reasoning paths sampled from large language models (LLMs). However, self-consistency relies on the answer extraction process to aggregate multiple solutions, which is not applicable to …

CAT-LM: Training Language Models on Aligned Code And Tests

Nikitha Rao, Kush Jain, Uri Alon, Claire Le Goues et autres

Testing is an integral part of the software development process. Yet, writing tests is time-consuming and therefore often neglected. Classical test generation tools such as EvoSuite generate behavioral test suites by optimizing for coverage, but tend to produce tests that are hard …

CAT-LM Training Language Models on Aligned Code And Tests

Nikitha Rao, Kush Jain, Uri Alon, Claire Le Goues et autres

Testing is an integral but often neglected part of the software development process. Classical test generation tools such as EvoSuite generate behavioral test suites by optimizing for coverage, but tend to produce tests that are hard to understand. Language models trained on …

us (code pays fourni par la source)