TADI: Tool-Augmented Drilling Intelligence via Agentic LLM Orchestration over Heterogeneous Wellsite Data
arXiv 2605.00060•276db49ff5632add07e1cec95cd4675ae65e3ccef5eefb3c4fe703878841e409
LLM safetyRLHF/DPOagentic AIbenchmarksdata fusiondecentralized systemsevaluation metricsexplainabilityjailbreak analysismechanistic interpretabilitymilitary applicationspreference optimizationreputation systemssynthetic datasetstool-augmented agents
Paper metadata
- arXiv ID
- 2605.00060
- Version
- Not specified by this published record
- Category
- Computer Science — Artificial Intelligence (cs.AI)
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Evidence and limitations
- Source ID
- arxiv_cs_ai
- Record identifier
- 276db49ff5632add07e1cec95cd4675ae65e3ccef5eefb3c4fe703878841e409
- Enrichment time
- 2026-05-05T08:52:10Z
- AI-assisted enrichment
- Yes
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