arXiv (All)
open access
Rethinking Prediction Horizons: Decoupling Model Queries from State Updates in Few-Step MeanFlow
MeanFlow predicts interval-average velocities, but conventional few-step sampling ties the interval queried from the model to the duration of the state update. We separate these two quantities at inference, fixing the model, update grid, and number of queries while varying the prediction horizon. Ou
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
- Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control#1234865
- Seq-Flow: Efficient Probabilistic Forecasting with Self-Rollout Error Control#1227324
- TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models#1356170
- SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs#1187991
- Learning a Mixture of GFlowNets#1216937
Record · ID 1355007
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