SPIN Unprocessed October 15, 2026 ai_technology research
ConicFlow: constrained Inference-time optimal control for flow matching models
View original on arxiv.orgOverview
arXiv:2610.14307v1 Announce Type: new Abstract: We propose ConicFlow, which frames constrained sampling from a flow matching model at inference time as an optimal control problem that balances deviation from the pretrained trajectory against reward, subject to hard terminal constraints. We discretize this problem and adapt receding-horizon control, optimizing only a short window of the trajectory at each step for computational tractability. We address the resulting sequence of receding-horizon s
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 Artificial Intelligence
View all →- Measuring the Inference Energy Efficiency of Open-Weight LLMs across Benchmarks, Reasoning Modes, and Hardware
- ANASSA: From High-Level Framework to Low-Level Architecture for Spatial Agentic AI
- ACED-Bench: Evaluating How LLM Agents Acquire and Act on Causal Evidence
- When Pleasing the User Compromises Care: AI Sycophancy in Caregiver-Mediated Healthcare
- Actionable Meta-Requirements for AI Robustness Auditing
- Which Metadata Matters for LLM Tool Selection in MCP? Evidence from Controlled Factor-Level Interventions
Markdown (.md) · JSON-LD schema (.json) · Machine-readable for AI & GEO