LONG INFERENCE COMPUTE

Reasoning-focused post-training is superseding raw model scaling as the primary driver for advancements in math and coding through techniques like self-consistency and verifiable-reward reinforcement learning.

Agentic workflow reliability remains a significant hurdle in system design, where multi-agent systems provide value but are still heavily constrained by consistency and execution accuracy.

Inference-time compute optimization is becoming a central architectural focus, utilizing mixture-of-experts (MoE) and attention efficiency to manage long-context models and complex reasoning tasks.