pith:QZAS27HM
QUIVER: Cost-Aware Adaptive Preference Querying in Surrogate-Assisted Evolutionary Multi-Objective Optimization
QUIVER adaptively chooses between objective evaluations and two types of preference queries to maximize expected decision quality gain per unit cost, reaching 25% lower final utility regret than fixed strategies on hard benchmarks.
arxiv:2605.04267 v2 · 2026-05-05 · cs.LG · cs.NE · math.OC
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\pithnumber{QZAS27HMI66A6OKTKFIELELYG6}
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Record completeness
Claims
QUIVER achieves the lowest final utility regret on challenging WFG problems (utility regret of 2.14 on WFG4, 2.82 on WFG9: a 25% improvement over baselines), outperforming all single-modality baselines.
The synthetic decision-maker models used to simulate preference responses and costs accurately represent real human behavior and query costs in the target application domains.
QUIVER adaptively balances cheap pairwise preference queries and costly indifference adjustments with objective evaluations to achieve lower utility regret than single-modality baselines on WFG benchmarks.
Formal links
Receipt and verification
| First computed | 2026-06-19T16:11:24.098368Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
86412d7cec47bc0f3953515045917837a90ebfeec9614f49d1d5133e739b94f0
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/QZAS27HMI66A6OKTKFIELELYG6 \
| 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: 86412d7cec47bc0f3953515045917837a90ebfeec9614f49d1d5133e739b94f0
Canonical record JSON
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