SPIN Unprocessed October 15, 2026 ai_technology research
Controllable Diverse Sampling through Pullback Directions
View original on arxiv.orgOverview
arXiv:2610.14294v1 Announce Type: new Abstract: Conditional diffusion models can generate high-quality images that align closely with a given prompt. However, samples generated from different random seeds may remain visually similar, limiting creative exploration. Existing approaches can increase variation, but often require additional optimization or sampling overhead, while stronger perturbations may introduce distortions that degrade image quality or prompt alignment. We propose a training-fr
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