SPIN Unprocessed October 15, 2026 ai_technology research
Lotus: Learning to Understand Time-series by Learning to Forecast
View original on arxiv.orgOverview
arXiv:2610.18877v1 Announce Type: new Abstract: Time-series language models typically align numerical signals with language using labels, captions, or question-answer pairs. Although such supervision encourages linguistic representations, it does not explicitly require them to preserve information about how the underlying time-series signal evolves. We introduce forecast grounding, a pretraining principle that requires language-aligned time-series representations to remain predictive of the futu
SpinGraph analysis pending — check back after processing.
Ask AI about this story
Opens with the SpinGraph .md URL and structured context — one click, prompt included.
More from arXiv Machine Learning
View all →- Backpropagating Verifier Rewards Through Continuous Flow Language Models
- Theoretical and Practical Advances in Hierarchical Bayesian Deep Learning Methodology
- How Inner Optimizers Shape Replica Scaling
- Deep Generative Models for Phylogenetic Inference with Complex Evolutionary Processes
- What Information Buys in Active Inference
- Graph Coarsening Enables Escaping Local Optima in Compute-Constrained Nonconvex MAX-CUT
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO