pith:JM5BIDNV
Beyond Localization: A Comprehensive Diagnosis of Perspective-Conditioned Spatial Reasoning in MLLMs from Omnidirectional Images
Multimodal large language models show a large gap between perception and perspective-conditioned spatial reasoning on omnidirectional images.
arxiv:2605.12413 v3 · 2026-05-12 · cs.CV
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\pithnumber{JM5BIDNVCP3RE4ITLUZ6JFKK5O}
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Record completeness
Claims
PCSR is a key bottleneck in current MLLMs and highlight limited but meaningful room for recovery under targeted optimization.
The eight tasks in PCSR-Bench accurately isolate perspective-conditioned spatial reasoning without confounding effects from omnidirectional projection artifacts or question-generation biases.
A new benchmark reveals MLLMs achieve only 13% or lower accuracy on advanced perspective-conditioned spatial tasks in omnidirectional images, with RL reward shaping raising a 7B model from 31% to 60% in controlled settings.
Formal links
Receipt and verification
| First computed | 2026-05-20T00:05:47.312094Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
4b3a140db513f71271135d33e4954aebb35e09ab2ad406268bff8aa860c39f3b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/JM5BIDNVCP3RE4ITLUZ6JFKK5O \
| 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: 4b3a140db513f71271135d33e4954aebb35e09ab2ad406268bff8aa860c39f3b
Canonical record JSON
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