HalluPrism: When Multimodal Uncertainty Should Diagnose, Not Decide
Multimodal Large Language Models (MLLMs) can assign similar confidence to answers that fail for different reasons. We propose HalluPrism, a behavioral diagnostic that re-runs an answer after visual degradation, blank-image replacement, and grounding or relation checks. These targeted probes yield a signature over visual-perturbation sensitivity (V ), image-removal confidence retention (L), and grounding/relation-probe instability (A). Across 58K+ examples from four benchmarks and four MLLMs, image-removal confidence retention is most prevalent, while grounding/relation-probe instability better separates failure families. Only 18 of 48 source-target checks are diagonally aligned, so the co...
| Authors | Aman Prakash, Sourish Dasgupta, Tanmoy Chakraborty |
|---|---|
| Primary category | cs.LG |
| Published | 29 August 2026 |
| Last updated at source | 29 August 2026 |
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