arXiv:2605.20758v1 Announce Type: cross Abstract: Inference-time guided sampling steers state-of-the-art diffusion and flow models without fine-tuning by interpreting the generation process as a controllable trajectory. This provides a simple and flexible way to inject external constraints (e.g., cost functions or pre-trained verifiers) for controlled generation. However, existing methods often fail when composing multiple constraints simultaneously, which leads to deviations from the true data manifold. In this work, we identify root causes of this off-manifold drift and find that the approxi
This research addresses a key technical bottleneck in AI development, as the increasing complexity of AI tasks demands more sophisticated constraint handling in generative models.
Improving the ability of AI models to simultaneously integrate multiple constraints is crucial for developing more reliable, controllable, and commercially viable AI agents and autonomous systems.
The proposed 'Conflict-Aware Additive Guidance' allows for more robust and coherent composition of constraints in generative AI, reducing off-manifold drift and enhancing control.
- · AI developers
- · Generative AI platforms
- · Robotics companies
- · AI-driven automation
- · Platforms with single-constraint AI models
- · AI systems prone to incoherent constraint handling
More sophisticated and reliable AI models become feasible, especially in complex, multi-objective environments.
This technical improvement accelerates the development and deployment of autonomous AI agents capable of handling real-world, often conflicting, constraints.
Enhanced AI controllability could lead to broader adoption of AI in safety-critical sectors, potentially shifting economic value towards highly autonomous systems.
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Read at arXiv cs.LG