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Agents can learn to act reliably in new environments by generating their own practice tasks and distilling reusable heuristics from self-identified failures, achieving significant performance gains without human supervision or pre-defined benchmarks.
Bidirectional Late Interaction models, despite their architecture, can still fall prey to length biases, challenging assumptions about their robustness.