pith:S3B3RQO5
FireScope: Wildfire Risk Raster Prediction with a Chain-of-Thought Oracle
A vision-language model with chain-of-thought reasoning predicts wildfire risk rasters that transfer from US training to European testing.
arxiv:2511.17171 v6 · 2025-11-21 · cs.CV · cs.LG
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Claims
When trained in the USA and tested in Europe, FireScope achieves substantial performance gains, while expert feedback and automated analysis confirm that its reasoning traces are faithful and semantically meaningful.
That expert-defined risk rasters in the US and real wildfire events in Europe constitute sufficiently aligned and representative supervision signals for the VLM to learn transferable causal reasoning across continents.
FireScope is a VLM framework that generates wildfire risk rasters together with reasoning traces, showing improved cross-continental generalization when trained on US expert maps and tested on European fire events.
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| First computed | 2026-05-25T02:02:10.443832Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
96c3b8c1dd0abd42ab97719651519284bd33cf2a3eb37f5920ba0e12863bc2e1
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/S3B3RQO5BK6UFK4XOGLFCUMSQS \
| 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: 96c3b8c1dd0abd42ab97719651519284bd33cf2a3eb37f5920ba0e12863bc2e1
Canonical record JSON
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