arXiv:2606.05758v1 Announce Type: cross Abstract: Many modern vision-language models (VLMs) build on autoregressive decoding of discrete tokens. While text-based output interfaces enable scalable pretraining and strong zero-shot generalization across diverse tasks, they are poorly suited for problems that require precise continuous outputs, such as localizing temporal boundaries of events or generating robotic control actions. To address this challenge, we propose DRIFT, a general framework for adapting pretrained VLMs to continuous decoding tasks. DRIFT combines a base predictor, which provid
The proliferation of vision-language models (VLMs) and the increasing demand for finely-tuned, precise robotic and temporal control necessitate bridging the gap between discrete text outputs and continuous physical actions.
This development addresses a key limitation in current AI systems, enabling more sophisticated and accurate control for robotics and real-time event interpretation, critical for automation and complex agentic systems.
VLMs can now directly generate continuous outputs, moving beyond text-based descriptions to precise numerical control, thereby expanding their applicability to tasks requiring fine-grained operational directives.
- · Robotics companies
- · Automation sector
- · Developers of AI agents
- · Computer vision researchers
- · Systems relying solely on discrete AI outputs for continuous control
- · Legacy control systems
VLMs become significantly more capable in tasks requiring physical interaction and precise temporal understanding.
This enhanced capability accelerates the development and deployment of advanced AI agents and more dexterous humanoid robots.
Improved control for robotic systems could lead to new forms of manufacturing, logistics, and service industries.
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Read at arXiv cs.LG