详细信息
- 来源站点
- ArXiv CS.CL
- 作者
- Mingyang Li, Yurou Liu, Jieping Ye, Bing Su, Ji-Rong Wen, Zheng Wang
- 文章类型
- NEWS
- 语言
- en
- 发布日期
- 2026-06-16
摘要
arXiv:2606.16905v1 Announce Type: new Abstract: In this report, we present LOGOS (Language Of Generative Objects in Science), a scientific generative language model that unifies heterogeneous tasks across the natural sciences within a single autoregressive framework based on a shared scientific grammar. It encodes diverse scientific objects and their spatial interactions as token sequences over a common vocabulary. By representing spatial contact and constraint patterns as discrete tokens, the model captures complex structural interactions in a purely sequential manner, without relying on explicit coordinates or geometric neural networks. This unified representation enables a wide range of downstream tasks to be formulated consistently as next-token prediction in the same grammar space, creating strong alignment between continued multi-domain pre-training and downstream objectives.