pith:H2ZNH2MV
The Expense of Seeing: Attaining Trustworthy Multimodal Reasoning Within the Monolithic Paradigm
Vision-language models bypass visual input using language priors, with the penalty increasing as language models scale.
arxiv:2604.20665 v2 · 2026-04-22 · cs.CV · cs.AI
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Claims
state-of-the-art models frequently exhibit functional blindness, i.e., exploiting strong language priors to bypass severe visual representation bottlenecks... hypothesising that as the underlying language engines scale to unprecedented reasoning capabilities, the mathematical penalty of the visual knowledge bottleneck paradoxically increases.
That the Modality Translation Protocol can isolate architectural incapacity from dataset biases without introducing its own translation artifacts or new priors, and that the proposed metrics validly quantify the visual bottleneck.
Vision-language models exhibit functional blindness by exploiting language priors over visual representations; the Modality Translation Protocol and metrics like Toll, Curse, and Fallacy of Seeing reveal this, supporting a Divergence Law where larger language models increase the visual penalty.
Receipt and verification
| First computed | 2026-05-22T01:04:02.885254Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
3eb2d3e9952f4945e5837a095f23e32677716bfe0b25edca762fe6cf458376fa
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/H2ZNH2MVF5EULZMDPIEV6I7DEZ \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 3eb2d3e9952f4945e5837a095f23e32677716bfe0b25edca762fe6cf458376fa
Canonical record JSON
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