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Tongyi Lab Open-Sources LOGOS, First Unified Scientific Large Model Outperforming NatureLM with 1B Parameters
On June 18, 2026, Tongyi Lab announced the open-source release of LOGOS (Language Of Generative Objects in Science), the first multi-domain scientific generative foundation model built on a unified "scientific grammar."
Developed jointly by ATH-Token Foundry and the Huoqian Institute of Artificial Intelligence at Renmin University, this model aims to break the fragmented "one task, one expert model" status quo in traditional AI for Science (AI4S). By encoding diverse scientific objects—proteins, small molecules, materials, and chemical reactions—into a unified discrete token sequence, it enables cross-domain knowledge integration and autoregressive generation within a native large model framework.
LOGOS's core breakthrough lies in its innovative "scientific grammar" design and spatial interaction discretization technology. This allows the model to deeply understand complex 3D spatial interaction rules and perform autoregressive generation without relying on scarce 3D coordinate data or specialized geometric networks, ensuring full consistency in form and objective between pre-training and downstream tasks.
Evaluation results show that LOGOS-1B, with just 1 billion parameters, consistently matches or surpasses domain-specific methods across six representative tasks: pocket-conditioned ligand generation, retrosynthesis prediction (Top-1 accuracy 74.8%), pocket site identification (Top-n accuracy 58.5% on the HOLO4K dataset), and MOF material generation (a 76% increase in the proportion of new building units). In some tasks, it even outperformed NatureLM—which uses 8×7B parameters—while using only 1/56 of the parameter count.
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On June 18, 2026, Tongyi Lab announced the open-source release of LOGOS (Language Of Generative Objects in Science), the first multi-domain scientific generative foundation model built on a unified "scientific grammar."
Developed jointly by ATH-Token Foundry and the Huoqian Institute of Artificial Intelligence at Renmin University, this model aims to break the fragmented "one task, one expert model" status quo in traditional AI for Science (AI4S). By encoding diverse scientific objects—proteins, small molecules, materials, and chemical reactions—into a unified discrete token sequence, it enables cross-domain knowledge integration and autoregressive generation within a native large model framework.
LOGOS's core breakthrough lies in its innovative "scientific grammar" design and spatial interaction discretization technology. This allows the model to deeply understand complex 3D spatial interaction rules and perform autoregressive generation without relying on scarce 3D coordinate data or specialized geometric networks, ensuring full consistency in form and objective between pre-training and downstream tasks.
Evaluation results show that LOGOS-1B, with just 1 billion parameters, consistently matches or surpasses domain-specific methods across six representative tasks: pocket-conditioned ligand generation, retrosynthesis prediction (Top-1 accuracy 74.8%), pocket site identification (Top-n accuracy 58.5% on the HOLO4K dataset), and MOF material generation (a 76% increase in the proportion of new building units). In some tasks, it even outperformed NatureLM—which uses 8×7B parameters—while using only 1/56 of the parameter count.
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Alibaba DAMO Academy Unveils DAMO EAGLE AI for Non-Invasive Esophageal Cancer Detection
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