Unverified50% confidenceOpinionExact time
SpatialASM provides a scalable path to alleviate the scarcity of spatial reasoning training data by synthesizing ASM trajectories
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe core idea of SpatialASM is spatial simulation at training time, without relying on additional tools at inference time80% similarUnverifiedGeospatial foundation models typically use the Transformer architecture as their core, employing self-supervised learning methods for pre-training on large volumes of remote sensing imagery, with typical methods including masked autoencoders (MAE) and contrastive learning.69% similarUnverifiedThe learning path for local LLMs should progress step by step: from API calls, to running small open-source models locally, to fine-tuning, to optimizing inference performance.67% similarUnverified轨迹数据集标准化的意义可类比于ImageNet对计算机视觉、Common Crawl对大语言模型预训练的作用66% similarUnverified使用合成数据训练网络学习的是DMC的布局几何和空间结构，而非具体纹理特征65% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1001978API
curl https://kongchang.com/api/v1/knowledge/claims/1001978MCP
get_claim(id=1001978)