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Wrapping standard CLI coding agents in a state-persisting orchestration layer beats unconstrained autonomous agents at research completeness without altering the underlying model.
GatorOnco outshines traditional models by achieving expert-level treatment planning performance while enhancing readability and completeness in colorectal cancer care.
Stop bloating your agent's context window: a dependency-aware skill graph slashes token costs by 37% and boosts reward by 43% compared to naive skill loading.
Training massive LLMs on a single GPU is now possible, potentially democratizing access to large-scale model development.
