pith:SPQ5Q4W4
LEGO: An LLM Skill-Based Front-End Design Generation Platform
Decomposing front-end design into six steps and 42 reusable circuit skills lets LLMs reach 80.5 percent success on hard RTL tasks that direct prompting cannot solve.
arxiv:2604.23355 v2 · 2026-04-25 · cs.AI
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Claims
individual circuit skills constructed within LEGO raise Pass@1 from 0.000 to 0.805. This is an 80.5% gain over the baseline. Cross-project skill compositions also reach 0.805 Pass@1. They outperform hierarchy-verilog by 14.6% and VerilogCoder by 2.5%.
That the digital front-end flow decomposes cleanly into six independent steps and that the 42 skills extracted from 11 surveyed projects are representative, generalizable, and composable without significant performance loss on new problems.
LEGO extracts 42 circuit skills from open-source projects to enable composable LLM-based front-end design, raising Pass@1 to 0.805 on challenging Verilog problems.
Receipt and verification
| First computed | 2026-05-20T00:04:32.736307Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
93e1d872dc5e6569e05584b473d070bd518b4b73ce82f265b90c3224632f2de1
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/SPQ5Q4W4LZSWTYCVQS2HHUDQXV \
| 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: 93e1d872dc5e6569e05584b473d070bd518b4b73ce82f265b90c3224632f2de1
Canonical record JSON
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