Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery
Updated 7 h ago · first seen 11 Sept 2026
paper_01M294GK2YGM6JAQVYJ78S2527
- Published
- 11 Sept 2026
- T1 · 7 h ago
- arXiv
- 2609.09413
- T1 · 7 h ago
- Category
- cs.AI
- T1 · 7 h ago
Abstract
Choosing a recovery process for scale-up requires connecting laboratory results with product requirements, process costs, and scale effects. We analyze records from Pacific Northwest National Laboratory's Computer Intelligence for Critical Element Recovery and Optimization (CICERO) workflow for autonomous selective precipitation. Active learning uses prior results to choose experiments. In a conditional retrospective benchmark with fitted models and recycled neodymium-iron-boron (NdFeB) magnet records, active learning finds the best recorded result with fewer experiments than nonadaptive space filling. Enrichment is the selected rare-earth-to-iron ratio relative to that in the feed. Adaptive policies reach the recorded enrichment maximum by 16 to 24 wells (individual experiments), versus 48. Our two-stage reconstruction ties two adaptive alternatives at 16 wells. Conditional analyses of recycled samarium-cobalt (SmCo) magnets show a Round 2 tradeoff between purity and nominal yield, the recovery fraction calculated from an assumed starting amount - NdFeB Round 1 routes differ in enrichment. Rankings for produced water from oil and gas extraction depend on phase and dilution assumptions requiring confirmation. We propose choosing batches by their expected reduction in downstream Bayes risk: the minimum expected loss among available process decisions under current beliefs. In exploratory simulations, a hybrid that filters candidates has lower estimated loss than the implemented joint search across routes and conditions. Differences involving the synthetic two-stage policy are small relative to estimation uncertainty. We outline a pre-registered prospective test under a shared loss and logging standard, requiring clarified measurements and records, a defined process decision and relevant outputs, credible economic inputs, and validation at the intended scale.
Authors 3
Niranjan Srinivas, Debajyoti Ray, Elias Nakouzi
Specification
- Official page
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Arxiv announce type
- new
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- arXiv id
- 2609.09413
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Categories
- cs.AI, cs.CE, cs.RO
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Primary category
- cs.AI
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
- Published
- 11 Sept 2026
Source:arXiv (Atom API + RSS)T1observed 7 h agohigh
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9
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T19
Freshest observation
7 h ago
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- Authors
- Niranjan Srinivas, Debajyoti Ray, Elias Nakouzi
As of
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Categoriescategories1
| Value | Valid from → to | Status | Source | Confidence | Extractor |
|---|---|---|---|---|---|
| cs.AI, cs.CE, cs.RO | → current | current | arXiv (Atom API + RSS)T1 | high | deterministic |
Claims are temporal and append-only: a new observation closes the previous claim (valid_to) instead of overwriting it. Conflicting claims from different sources are kept side by side and flagged — never averaged. Methodology →
New paper: Decision-Focused Active Learning for Scale-Aware Critical-Materials Recovery
arxiv
| Source | Document | Type | Tier | Last observed | Snapshots |
|---|---|---|---|---|---|
| arXiv (Atom API + RSS) | rss.arxiv.org/rss/cs.AI | feed | T1· Official | 6 h ago | 1 |
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