pith:ERUBIIKC
Language Acquisition Device in Large Language Models
Pre-pretraining LLMs on MP-STRUCT achieves token efficiency on par with strong baselines while adding resistance to implausible languages.
arxiv:2605.16758 v1 · 2026-05-16 · cs.CL
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\pithnumber{ERUBIIKC47NIQXK2ONAXU4OITB}
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Record completeness
Claims
A brief 500-step PPT with MP-STRUCT matches strong formal-language baselines in token efficiency while additionally imparting a human-like resistance to structurally implausible languages (e.g., REVERSE).
That the structural properties encoded in MP-STRUCT (hierarchical composition, feature-based dependencies, long-distance displacement) successfully instantiate the innate constraints of the LAD hypothesis and transfer to improved natural-language behavior in LLMs.
Pre-pretraining on MP-STRUCT matches k-Shuffle Dyck baselines in efficiency while adding human-like resistance to implausible languages and challenges the need for C-RASP definability in effective PPT languages.
References
Formal links
Receipt and verification
| First computed | 2026-05-20T00:03:20.251045Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2468142142e7da885d5a73417a71c8986260f8de53dc22d3a36340d384292122
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/ERUBIIKC47NIQXK2ONAXU4OITB \
| 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: 2468142142e7da885d5a73417a71c8986260f8de53dc22d3a36340d384292122
Canonical record JSON
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