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PILOT Technical Report — arXiv:2608.18637

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.18637
License (as archived): CC0-1.0
Archived: 2026-10-18T14:03:41.969Z
SHA-256 of the archived text: 96b3300217a27e115edd8a08c6d9d1115a5872912ed6f8af9da7a87b6dc476c9
Stable URL of this page: https://metraipolis.com/documento/dc_cd96b1014e50

How to cite

PILOT Technical Report — arXiv:2608.18637 — arXiv (en), https://arxiv.org/abs/2608.18637 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_cd96b1014e50 (Metraipolis archive dc_cd96b1014e50, sha256 96b3300217a27e115edd8a08c6d9d1115a5872912ed6f8af9da7a87b6dc476c9)

BibTeX

@misc{dccd96b1014e50,
  title = {PILOT Technical Report — arXiv:2608.18637},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.18637},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_cd96b1014e50; sha256 96b3300217a27e115edd8a08c6d9d1115a5872912ed6f8af9da7a87b6dc476c9},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.18637. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Existing agentic approaches for recommendation system optimization remain fundamentally reactive: they adjust parameters in response to observed metric changes but lack the ability to proactively design controlled experiments, personalize strategies at the user-segment level, or accumulate reusable experimental methodology across tasks. We present PILOT (Proactive Insight Learner for Online Tree-Experiments), an LLM-agent framework that organizes three roles within a constrained control loop where deterministic services enforce all safety, statistical, and permission boundaries: (1) an Experiment Manager that drives the full experiment lifecycle -- task intake, observation governance, anomaly recovery, and postmortem -- by selecting only from a rule-generated legal-command envelope; (2) a Search Planner that proposes candidate decision trees for user-segment-level personalization, invoked only when the Manager requests planning; and (3) a Memory Curator that asynchronously distills experiment outcomes into strategy-level domain knowledge and provenance-tracked methodology, failure-isolated from the main loop. The Manager makes the agent proactive, the Planner enables population-level personalization beyond global tuning, and the Curator turns every completed task into a learning opportunity for the next. Deployed on Taobao's platform with 5 experimental buckets, PILOT is compared against ROAM(Reactive Optimization with Agent-driven Moves), a free-exploration agent without lifecycle governance or structured hypothesis testing. PILOT achieves up to +1.40% IPV, +1.60% Core IPV, +0.96% transaction count, and +1.50% transaction amount, improving over ROAM's best results (+1.00% IPV, +0.90% Core IPV, +0.60% transaction count, +1.13% transaction amount) while raising search efficiency from 53.3% to 93.3% (+40 pp), with no human intervention throughout the experimental cycle.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-20 in cs.IR).
Categorie arXiv: Information Retrieval (cs.IR).
Pagina del lavoro: https://arxiv.org/abs/2608.18637 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-18T13:59:46.168Z dalla pagina https://arxiv.org/catchup/cs.IR/2026-08-20?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).