pith:EYDHFGU7
Aligning Deep Implicit Preferences by Learning to Reason Defensively
CDRA reframes LLM alignment as a critique-driven reasoning process to infer unstated goals, contexts, and risk tolerances.
arxiv:2510.11194 v3 · 2025-10-13 · cs.AI
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Record completeness
Claims
Experiments demonstrate that CDRA excels at discovering and aligning with users' true preferences while executing robust reasoning.
The assumption that a simulated multi-faceted cognitive council can reliably produce critique-annotated reasoning chains that accurately deconstruct query semantics and reveal latent risks for the DeepPref benchmark curation.
CDRA reframes LLM alignment as a critique-driven reasoning process using the DeepPref benchmark and Pers-GenPRM to infer implicit preferences and guide policy via process-level RL.
Receipt and verification
| First computed | 2026-06-04T01:08:33.599423Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
2606729a9f609a403c39fba4c5a8eeb07bfb38b1b8a44e5b26fdca023ed283de
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/EYDHFGU7MCNEAPBZ7OSMLKHOWB \
| 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: 2606729a9f609a403c39fba4c5a8eeb07bfb38b1b8a44e5b26fdca023ed283de
Canonical record JSON
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