AI Research
A language model built for the public good
Earlier this week in Geneva, around 50 leading global initiatives and organisations dedicated to open-source LLMs and trustworthy AI convened at the International Open-Source LLM Builders Summit. Hosted by the AI centres of EPFL and ETH Zurich, the event marked a significant step in building a vibrant and collaborative international ecosystem for open foundation models. Open LLMs are increasingly viewed as credible alternatives to commercial systems, most of which are developed behind closed doors in the United States or China.
Participants of the summit previewed the forthcoming release of a fully open, publicly developed LLM — co-created by researchers at EPFL, ETH Zurich and other Swiss universities in close collaboration with engineers at CSCS. Currently in final testing, the model will be downloadable under an open license. The model focuses on transparency, multilingual performance, and broad accessibility.
The model will be fully open: source code and weights will be publicly available, and the training data will be transparent and reproducible, supporting adoption across science, government, education, and the private sector. This approach is designed to foster both innovation and accountability.
“Fully open models enable high-trust applications and are necessary for advancing research about the risks and opportunities of AI. Transparent processes also enable regulatory compliance,” says Imanol Schlag, research scientist at the ETH AI Center, who is leading the effort alongside EPFL AI Center faculty members and professors Antoine Bosselut and Martin Jaggi.
Multilingual by design
A defining characteristic of the LLM is its fluency in over 1000 languages. “We have emphasised making the models massively multilingual from the start,” says Antoine Bosselut.
Training of the base model was done on a large text dataset in over 1500 languages — approximately 60% English and 40% non-English languages — as well as code and mathematics data. Given the representation of content from all languages and cultures, the resulting model maintains the highest global applicability.
Designed for scale and inclusion
The model will be released in two sizes — 8 billion and 70 billion parameters, meeting a broad range of users’ needs. The 70B version will rank among the most powerful fully open models worldwide. The number of parameters reflects a model’s capacity to learn and generate complex responses.
High reliability is achieved through training on over 15 trillion high-quality training tokens (units representing a word or part of the word), enabling robust language understanding and versatile use cases.
Responsible data practices
The LLM is being developed with due consideration to Swiss data protection laws, Swiss copyright laws, and the transparency obligations under the EU AI Act. In a external page recent study, the project leaders demonstrated that for most everyday tasks and general knowledge acquisition, respecting web crawling opt-outs during data acquisition produces virtually no performance degradation.
Supercomputer as an enabler of sovereign AI
The model is trained on the “Alps” supercomputer at CSCS in Lugano, one of the world’s most advanced AI platforms, equipped with over 10,000 NVIDIA Grace Hopper Superchips. The system’s scale and architecture made it possible to train the model efficiently using 100% carbon-neutral electricity.
The successful realisation of “Alps” was significantly facilitated by a long-standing collaboration spanning over 15 years with NVDIA and HPE/Cray. This partnership has been pivotal in shaping the capabilities of “Alps”, ensuring it meets the demanding requirements of large-scale AI workloads, including the pre-training of complex LLMs.
“Training this model is only possible because of our strategic investment in ‘Alps’, a supercomputer purpose-built for AI,” says Thomas Schulthess, Director of CSCS and professor at ETH Zurich. “Our enduring collaboration with NVIDIA and HPE exemplifies how joint efforts between public research institutions and industry leaders can drive sovereign infrastructure, fostering open innovation — not just for Switzerland, but for science and society worldwide.”
Public access and global reuse
In late summer, the LLM will be released under the Apache 2.0 License. Accompanying documentation will detail the model architecture, training methods, and usage guidelines to enable transparent reuse and further development.
“As scientists from public institutions, we aim to advance open models and enable organiations to build on them for their own applications”, says Antoine Bosselut.
“By embracing full openness — unlike commercial models that are developed behind closed doors — we hope that our approach will drive innovation in Switzerland, across Europe, and through multinational collaborations. Furthermore, it is a key factor in attracting and nurturing top talent,” says EPFL professor Martin Jaggi.
Artificial intelligence is transforming our world at an unprecedented rate, but what does this mean for Christians, morality and human flourishing?
In this episode of “The Inside Story,” Billy Hallowell sits down with The Christian Post’s Brandon Showalter to unpack the promises and perils of AI.
From positives like Bible translation to fears over what’s to come, they explore how believers can apply a biblical worldview to emerging technology, the dangers of becoming “subjects” of machines, and why keeping Christ at the center is the only true safeguard.
Plus, learn about The Christian Post’s upcoming “AI for Humanity” event at Colorado Christian University and how you can join the conversation in person or via livestream:
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AI Research
Intrinsic Dimension Estimating Autoencoder (IDEA) Using CancelOut Layer and a Projected Loss
arXiv:2509.10011v1 Announce Type: cross
Abstract: This paper introduces the Intrinsic Dimension Estimating Autoencoder (IDEA), which identifies the underlying intrinsic dimension of a wide range of datasets whose samples lie on either linear or nonlinear manifolds. Beyond estimating the intrinsic dimension, IDEA is also able to reconstruct the original dataset after projecting it onto the corresponding latent space, which is structured using re-weighted double CancelOut layers. Our key contribution is the introduction of the projected reconstruction loss term, guiding the training of the model by continuously assessing the reconstruction quality under the removal of an additional latent dimension. We first assess the performance of IDEA on a series of theoretical benchmarks to validate its robustness. These experiments allow us to test its reconstruction ability and compare its performance with state-of-the-art intrinsic dimension estimators. The benchmarks show good accuracy and high versatility of our approach. Subsequently, we apply our model to data generated from the numerical solution of a vertically resolved one-dimensional free-surface flow, following a pointwise discretization of the vertical velocity profile in the horizontal direction, vertical direction, and time. IDEA succeeds in estimating the dataset’s intrinsic dimension and then reconstructs the original solution by working directly within the projection space identified by the network.
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arXiv:2509.10122v1 Announce Type: cross
Abstract: Pre-trained diffusion models have shown great potential in real-world image super-resolution (Real-ISR) tasks by enabling high-resolution reconstructions. While one-step diffusion (OSD) methods significantly improve efficiency compared to traditional multi-step approaches, they still have limitations in balancing fidelity and realism across diverse scenarios. Since the OSDs for SR are usually trained or distilled by a single timestep, they lack flexible control mechanisms to adaptively prioritize these competing objectives, which are inherently manageable in multi-step methods through adjusting sampling steps. To address this challenge, we propose a Realism Controlled One-step Diffusion (RCOD) framework for Real-ISR. RCOD provides a latent domain grouping strategy that enables explicit control over fidelity-realism trade-offs during the noise prediction phase with minimal training paradigm modifications and original training data. A degradation-aware sampling strategy is also introduced to align distillation regularization with the grouping strategy and enhance the controlling of trade-offs. Moreover, a visual prompt injection module is used to replace conventional text prompts with degradation-aware visual tokens, enhancing both restoration accuracy and semantic consistency. Our method achieves superior fidelity and perceptual quality while maintaining computational efficiency. Extensive experiments demonstrate that RCOD outperforms state-of-the-art OSD methods in both quantitative metrics and visual qualities, with flexible realism control capabilities in the inference stage. The code will be released.
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