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Parallel exploration beats sequential refinement when scaling agent test-time compute, but early-stage trajectory errors create persistent bottlenecks that deeper search and downstream model grafting struggle to overcome.
Model scale is no longer the sole bottleneck for complex causal analysis: pairing RL with simulator-synthesized interventions allows a 35B parameter model to surpass Claude Opus 5 on real-world diagnostic tasks.
APEx achieves a remarkable 14.7-point improvement over GPT-5.4 by effectively distilling procedural skills for adaptive reasoning in complex question answering.