pith:TYFQR5V6
Learning Without Losing Identity: Capability Evolution for Embodied Agents
Embodied agents improve task success from 32% to 91% by evolving separate capability modules without altering their core identity or safety limits.
arxiv:2604.07799 v2 · 2026-04-09 · cs.RO · cs.AI
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Claims
We demonstrate through simulated embodied tasks that capability evolution improves task success rates from 32.4% to 91.3% over 20 iterations, outperforming both agent-modification baselines and established skill-learning methods (SPiRL, SkiMo), while preserving zero policy drift and zero safety violations.
That embodied capabilities can be cleanly decomposed into independent, versioned ECMs whose evolution leaves the persistent agent identity and safety constraints untouched.
Embodied agents maintain a persistent identity while evolving capabilities via modular ECMs, raising simulated task success from 32.4% to 91.3% over 20 iterations with zero policy drift or safety violations.
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| First computed | 2026-05-22T01:04:01.806262Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
9e0b08f6beb1e96c7d84ae3814768e61d3a02edba71c2468a6fec879ce5bc29f
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· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/TYFQR5V6WHUWY7MEVY4BI5UOMH \
| 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())"
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Canonical record JSON
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