arXiv:2509.25533v2 Announce Type: replace-cross Abstract: As Vision Language Models (VLMs) are deployed across safety-critical applications, understanding and controlling their behavioral patterns has become increasingly important. Existing behavioral control methods face significant limitations: system prompting approaches could easily be overridden by user instructions, while applying activation-based steering vectors requires invasive runtime access to model internals, precluding deployment with API-based services and closed-source models. Finding steering methods that transfer across multi
The deployment of powerful VLMs in critical applications necessitates robust control mechanisms, driving research into steerability that transcends traditional prompting and invasive activation methods.
This development addresses a key limitation in VLM deployment, enabling safer and more predictable behavior, which is crucial for their integration into sensitive and mission-critical systems.
The ability to steer VLMs universally without invasive internal access fundamentally alters how these models can be controlled and deployed, particularly in API-based and closed-source environments.
- · AI developers
- · API-based VLM providers
- · Closed-source model developers
- · Safety-critical application sectors
- · Developers reliant solely on prompt engineering
- · Techniques requiring deep internal model access
This research provides a more robust and transferable method for controlling the behavior of large Vision Language Models.
It will accelerate the adoption of VLMs in regulated industries by offering improved safety and predictability mechanisms.
Enhanced VLM steerability could lead to a proliferation of specialized, dependable AI agents that operate within tighter behavioral constraints.
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Read at arXiv cs.AI