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

arXiv:2609.30993 · cs.CV, cs.AI · Submitted 2026-09-25 · Read on arXiv

cs.CV, cs.AI

Submitted: 2026-09-25

Updated: 2026-10-02

Terminology

• Grounding — 33× in this paper · explained in 3 episodes
• Alignment — 27× in this paper · explained in 1 episode
• Benchmark — 14× in this paper · explained in 2 episodes
• Core — 7× in this paper · explained in 2 episodes
• Steering — 6× in this paper · explained in 2 episodes
• Abstraction — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Interpretability — 5× in this paper · explained in 15 episodes
• Calibration — 4× in this paper · explained in 7 episodes
• Final Layer Inference-Time Probing — 4× in this paper
• Hallucination — 4× in this paper · explained in 3 episodes
• Tokenization — 4× in this paper · explained in 2 episodes
• Consistency — 3× in this paper · explained in 3 episodes
• Transformer — 3× in this paper · explained in 2 episodes
• Activation Patching — 2× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• Aggregation — 2× in this paper · explained in 1 episode
• Robustness — 2× in this paper · explained in 5 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Activation Steering — 1× in this paper · explained in 4 episodes
• Clustering — 1× in this paper · explained in 2 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• KV cache — 1× in this paper · explained in 3 episodes
• Latent Space — 1× in this paper · explained in 7 episodes
• Prompt Engineering — 1× in this paper · explained in 4 episodes

Sources

• Qwen3-VL Technical Report
• Circuit Tracing in Vision-Language Models: Understanding the Internal Mechanisms of Multimodal Thinking

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