pith:F5X7JSFI
Revealing Interpretable Failure Modes of VLMs
REVELIO uncovers interpretable concept compositions that cause consistent failures in vision-language models.
arxiv:2605.12674 v1 · 2026-05-12 · cs.AI · cs.LG · cs.RO
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\pithnumber{F5X7JSFI5VTT3HFJVPYXSKR33R}
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Claims
We introduce REVELIO, a framework for systematically uncovering interpretable failure modes in VLMs... uncovering previously unreported vulnerabilities in state-of-the-art VLMs.
That the searched concept compositions correspond to genuine, consistent real-world failure modes rather than artifacts of the simulation or search heuristics.
REVELIO uncovers interpretable failure modes in VLMs by searching combinatorial concept spaces with diversity-aware beam search and Gaussian-process Thompson sampling, revealing vulnerabilities in autonomous driving and indoor robotics.
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Receipt and verification
| First computed | 2026-05-18T03:09:50.100529Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2f6ff4c8a8ed673d9ca9abf1792a3bdc73bd12c42f53f645a94850b3fec3b427
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/F5X7JSFI5VTT3HFJVPYXSKR33R \
| jq -c '.canonical_record' \
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# expect: 2f6ff4c8a8ed673d9ca9abf1792a3bdc73bd12c42f53f645a94850b3fec3b427
Canonical record JSON
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