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Parallel LLM agents can finally achieve near-linear speedups by strategically parallelizing only when token-based cost estimates predict a net gain, eliminating redundant work and reconciliation overhead.
Fine-tuning a general grounding model on a newly constructed dataset boosts medical localization accuracy from 10.64 to 85.59, showcasing the power of domain-specific supervision.
