arXiv (All)
open access
Instance-Adaptive Prompts as Context for Time-Series Foundation Models
Longer histories can improve time-series foundation models (TSFMs), but require substantially higher inference cost. We therefore ask whether contextual information can be provided more efficiently through a compact set of learned token embeddings. We introduce PaCTS, which generates a small set of
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- A Foundation Model for Instruction-Conditioned In-Context Time Series Tasks#1062864
- Learning an Anchored Prompt Space for Continual Adaptation of Large Language Models#1116830
- Beyond Similarity: Coverage-Aware Prompt Selection for Time Series Forecasting with LLMs#1048372
- Long-Context Demonstration Selection Using State Space Models#1011603
- DIP: Dynamic In-Context Planner For Diffusion Language Models#792685
Record · ID 1129406
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive