# LOGOS：面向自然科学的通用科学生成语言模型

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-15 08:00
- AIHOT 分数：55
- AIHOT 链接：https://aihot.virxact.com/items/cmqis08ee03oksl5wj7k2za4w
- 原文链接：https://arxiv.org/abs/2606.16905

## AI 摘要

LOGOS 是一个科学生成语言模型，将自然科学的异构任务统一到同一自回归框架和共享科学语法中。它把科学对象及其空间交互编码成 token 序列，无需依赖坐标或几何神经网络，即可用纯序列方式捕获复杂结构相互作用。该统一表示使得多领域持续预训练与下游任务高度对齐。在多个任务上，LOGOS 匹配或超越领域专用基线，且 1B、3B、8B 三种参数规模与性能呈正相关。模型权重已开源以促进后续研究。

## 正文

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. Across diverse tasks, LOGOS consistently matches or outperforms domain-specific baselines, providing preliminary evidence for the feasibility of "one model fits all" in the natural sciences. We train LOGOS models at different scales (1B, 3B, and 8B parameters) and find a consistent positive correlation between model size and performance. This suggests that the future of AI for Science (AI4S) may not lie in building an independent technical stack that is separated from large language models (LLMs). Instead, it may depend on deeply aligning scientific foundation models with LLMs through shared architectures, shared training paradigms, and shared inference infrastructure, so that LLMs can truly become a new entry point for AI4S. We release the model weights and associated resources to facilitate further research.