pith:FY5AT53B
Breaking the Reasoning Horizon in Entity Alignment Foundation Models
A parallel encoding strategy lets entity alignment foundation models generalize directly to unseen knowledge graphs.
arxiv:2601.21174 v2 · 2026-01-29 · cs.LG
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Claims
We propose a EA foundation model driven by a parallel encoding strategy... This facilitates anchor-conditioned message passing and significantly shortens the inference trajectory by leveraging local structural proximity instead of global search... Extensive experiments verify the effectiveness of our framework, highlighting its strong generalizability to unseen KGs.
That seed EA pairs are always available as reliable local anchors and that parallel streams plus the merged relation graph can fully capture necessary long-range dependencies without introducing new errors or losing critical global structure in sparse heterogeneous KGs.
A parallel encoding strategy with anchor-conditioned message passing and a merged relation graph allows entity alignment foundation models to generalize to unseen knowledge graphs by shortening the reasoning horizon.
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| First computed | 2026-05-17T23:39:16.559256Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2e3a09f761b3cf0205208b2f7ccfd6d375a191c8bfdbc6727eeabf58c43dfc6e
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/FY5AT53BWPHQEBJARMXXZT6W2N \
| 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: 2e3a09f761b3cf0205208b2f7ccfd6d375a191c8bfdbc6727eeabf58c43dfc6e
Canonical record JSON
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