Drift Adaptation as Supervision Routing Under Heterogeneous Costs
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Updated
Jun 28, 2026 - Python
Drift Adaptation as Supervision Routing Under Heterogeneous Costs
Cost-aware multi-agent LLM framework with quality gates. Simple questions: 1 call. Code tasks: writer+reviewer loop.
Always-on meta-router skill that eliminates the top 5 LLM agent time-wastes: wrong routing, shallow outputs, redundant generation, sequential-when-parallel tool calls, and over-provisioned models.
PyTorch implementation of REACT: longitudinal active feature acquisition with Gumbel-Sigmoid relaxation for cost-aware sequential feature selection with context. ACM BCB 2026.
Alice — a fail-closed job-search agent: deterministic location/travel gates run before the LLM, and an unparseable model reply resolves to NOT-FIT, never FIT. The model only scores roles that are already viable.
High-Volume & Cost-Aware Merchant Payment Platform
Adaptive, cost-aware retry policies for external requests: a Beta posterior decides per call whether retrying is worth it, not a fixed count. Zero-dependency, node-free, TypeScript.
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