pith:7GJQYMWP
DecepGPT: Schema-Driven Deception Detection with Multicultural Datasets and Robust Multimodal Learning
DecepGPT augments existing deception benchmarks with cue descriptions and reasoning chains, adds a large multicultural dataset, and introduces two modules to reach state-of-the-art detection that transfers across domains and cultures.
arxiv:2603.23916 v3 · 2026-03-25 · cs.CV · cs.AI
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Claims
Experiments on three established benchmarks and our novel dataset demonstrate that our method achieves state-of-the-art performance in both in-domain and cross-domain scenarios, while exhibiting superior transferability across diverse cultural contexts.
That the manually added cue-level descriptions and reasoning chains in the augmented datasets accurately reflect genuine deception signals rather than annotator bias or post-hoc rationalization.
A new 1695-sample multicultural dataset plus two modules for stable multimodal fusion and modality consistency yield state-of-the-art deception detection with cross-cultural transfer.
Receipt and verification
| First computed | 2026-06-09T01:05:16.350568Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/7GJQYMWPN2DYVRP44XMPRSP4MB \
| 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: f9930c32cf6e878ac5fce5d8f8c9fc606bc5148fca8d5918a2f4cc4a4609894c
Canonical record JSON
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