Explore/framework/MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image Classification
M

Daniel Perkins, John Squires, Janou Milligan, Chandra Raskoti, Linda Ungerboeck/MLLM-Routed Heterogeneous Ensembles for Robust Cross-Dataset Image ClassificationUnknown

A research paper proposing ARMDIL, an LLM‑driven router that directs images to the most suitable vision backbone from a heterogeneous ensemble for cross‑dataset classification. It demonstrates improved generalization and interpretability for vision systems.

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55 / 100
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2026-08-152026-10-10
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✓ Best for

  • Multi-agent orchestration
  • Production agentic workflows
  • Stateful long-running tasks

◎ Strengths

  • Stable API
  • Active release cadence
  • Strong GitHub community

✕ Not ideal for

  • Simple single-step automation
  • Teams without Python/ML expertise

⚠ Watch-outs

  • Breaking changes between minor versions
  • Ecosystem lock-in if tightly coupled
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AgentHub Score

55
Score 55/100
Below average

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