pith:MLZKOIP6
FiRe: Fine-grained Multimodal Reasoning for Enhanced Image Generation
Decomposing text prompts into semantic units and verifying each via visual questions lets multimodal models refine generated images with targeted fixes.
arxiv:2604.13491 v3 · 2026-04-15 · cs.CV
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\usepackage{pith}
\pithnumber{MLZKOIP6MHGCTDJRSGGMHSTZLE}
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Record completeness
Claims
FiMR consistently outperforms image generation baselines, including reasoning-based methods, particularly on compositional text-to-image benchmarks.
That VQA-based verification of decomposed prompt units produces reliable, unbiased fine-grained feedback that leads to targeted improvements without introducing new errors or hallucinations.
FiMR improves text-to-image alignment by breaking prompts into minimal units, verifying each with VQA, and making localized refinements using MLLM reasoning.
Receipt and verification
| First computed | 2026-05-27T01:05:54.608818Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
62f2a721fe61cc298d31918cc3ca795932f0413baad48cfb02a6db9a25f5e6d4
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/MLZKOIP6MHGCTDJRSGGMHSTZLE \
| 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: 62f2a721fe61cc298d31918cc3ca795932f0413baad48cfb02a6db9a25f5e6d4
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by/4.0/",
"primary_cat": "cs.CV",
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"title_canon_sha256": "61fb2bfc827015116a5c93382df4b591306b087e102f7c60aabe079c7869bf13"
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