arXiv Artificial Intelligence

FLIP: Final Layer Inference-Time Probing for Vision-Language Models

1 hour ago 28.09.2026 07:00 👁 1 views
FLIP: Final Layer Inference-Time Probing for Vision-Language Models

Quick summary

arXiv:2609.30993v1 Announce Type: cross Abstract: We present FLIP, a final-layer inference-time probe for testing whether a logit-facing intervention site in an open-weight vision-language model (VLM) supports structured, task-linked computation rather than generic perturbation. Behavioral change under internal intervention is otherwise mechanistically ambiguous: it may reflect improved use of visual evidence, generic output instability, or outright degradation. FLIP applies elementwise flooring to the final normalized hidden state before logit computation, leaving parameters, prompts, and dec

Key takeaways

  • arXiv:2609.30993v1 Announce Type: cross Abstract: We present FLIP, a final-layer inference-time probe for testing whether a logit-facing intervention site in an open-weight vision-language model (VLM) supports structured, task-linked computation rather than generic perturbation.
  • Behavioral change under internal intervention is otherwise mechanistically ambiguous: it may reflect improved use of visual evidence, generic output instability, or outright degradation.
  • FLIP applies elementwise flooring to the final normalized hidden state before logit computation, leaving parameters, prompts, and dec

Why it matters

“FLIP: Final Layer Inference-Time Probing for Vision-Language Models” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗